SalesMap AI

Create products with ease

Full.CX effortlessly transforms your ideas into product requirements.

Full.CX turns product visions into detailed product requirements. The product below was entirely generated using our AI and advanced algorithms, exclusively available to our paid subscribers.

Product Details

Name

SalesMap AI

Tagline

AI-Driven Sales Success

Growth Gary

Name: Growth Gary
Description: Growth Gary is a sales team lead at a mid-sized tech company. He is motivated by the need to drive his team's performance, maximize sales efficiency, and leverage data to make informed decisions. Gary interacts with SalesMap AI daily to assess lead quality, strategize campaigns, and track sales performance effectively. His typical day includes reviewing analytics, coordinating with his team, and configuring the platform to optimize sales processes.
Demographics: Age: 38, Gender: Male, Education: Bachelor's degree in Business, Occupation: Sales Team Lead, Income Level: $80,000 per year, Location: Urban area, Tech-savvy, works in a fast-paced environment.
Background: Gary was raised in a suburban neighborhood and was always drawn to leadership roles in school and sports. He graduated with a degree in Business Administration and started as a sales representative, quickly moving up to a managerial position due to his analytical skills and people management style. Outside of work, he enjoys outdoor sports and reading industry-related content to stay updated.
Psychographics: Gary values teamwork, data-driven results, and continuous learning. He believes that empowering his team with the right tools can significantly impact performance. He seeks efficiency and strives to create a motivating work environment where everyone can thrive. His hobbies include hiking and attending sales strategy workshops.
Needs: Gary needs effective tools to automate lead scoring, insights for team performance, and resources for personalized sales strategies. He desires a platform that provides real-time data integration to enhance decision-making processes and streamline communication within his team.
Pain: Gary's main pain points include the overwhelming influx of leads that complicates prioritization, the lack of real-time insights for decision-making, and the challenge of aligning his team's activities with broader business goals. He often faces difficulties in quantifying ROI for his team's efforts and requires a solution that can simplify these processes.
Channels: Gary primarily uses online channels like LinkedIn for networking, industry blogs for continual learning, and email for communication. He values webinars and training sessions for product education, often engaged in technology forums.
Usage: Gary engages with SalesMap AI daily, spending approximately 2-3 hours a day analyzing data, managing leads, and strategizing campaigns. He often uses the platform in team meetings to track performance and adjust strategies accordingly.
Decision: Gary's decision-making process is highly analytical and relies on metrics for justification. He seeks peer recommendations, user reviews, and case studies before adopting new tools. He is influenced by the need for data-backed solutions that can increase team efficiency and performance.

Tech-Savvy Tina

Name: Tech-Savvy Tina
Description: Tech-Savvy Tina is a digital marketing manager at a rapidly growing startup. She is responsible for developing marketing campaigns that align with sales objectives while leveraging innovative technologies to enhance customer engagement. Tina regularly uses SalesMap AI to gather insights that inform her marketing strategies and adjust campaigns in real-time based on performance data.
Demographics: Age: 32, Gender: Female, Education: Master’s degree in Marketing, Occupation: Digital Marketing Manager, Income Level: $70,000 per year, Location: Urban area, works in a high-tech environment.
Background: Tina grew up in a tech-centric family, which sparked her interest in marketing and analytics from an early age. She earned her master's degree in Marketing, focusing on digital strategies. Her career began with an internship at a digital agency, leading her to various roles in marketing. Outside of work, Tina enjoys tech gadgets, attending trade shows, and blogging about marketing trends.
Psychographics: Tina values creativity, innovation, and staying ahead of digital marketing trends. She is motivated by the desire to create impactful campaigns and improve brand visibility. She loves experimenting with new technologies and has a keen interest in consumer behavior research.
Needs: Tina needs an intuitive platform that provides robust analytics, helps automate marketing workflows, and facilitates seamless collaboration with sales teams. She seeks comprehensive data to tailor campaigns that resonate with target audiences effectively.
Pain: Tina’s pain points include difficulties in aligning marketing efforts with sales, challenges in measuring campaign success in terms of sales conversion, and the need for real-time data to adjust strategies dynamically. She often struggles with disconnects between sales and marketing teams.
Channels: Tina utilizes multiple channels including social media platforms like Instagram and Twitter, industry-specific forums, email newsletters, and blogs. She also engages in webinars and attends online marketing courses.
Usage: Tina uses SalesMap AI several times a week, particularly during campaign planning and while analyzing post-campaign data. She dedicates around 1-2 hours each session to optimize her marketing strategies.
Decision: Tina’s decision-making is influenced by analytics and the latest trends in digital marketing. She often consults with her marketing team and relies on user-friendly reports from SalesMap AI that support quick adjustments to campaigns.

Budget-Conscious Brad

Name: Budget-Conscious Brad
Description: Budget-Conscious Brad is a small business owner who runs a local retail store. He is focused on maximizing sales while minimizing costs and seeks efficient ways to manage his sales process. Brad uses SalesMap AI to identify promising leads and automate aspects of his sales process, allowing him to focus more on customer engagement and service.
Demographics: Age: 45, Gender: Male, Education: Associate's degree in Business, Occupation: Small Business Owner, Income Level: $50,000 per year, Location: Suburban area, manages a small retail operation.
Background: Brad grew up helping in his family's retail shop, which instilled a strong work ethic and a passion for customer service. After working for several years in various retail jobs, he realized his dream of opening his own store. He actively participates in community events and values strong customer relationships, often investing in local initiatives.
Psychographics: Brad values practicality, community engagement, and customer satisfaction. He is motivated by the desire to support his family and provide excellent service to his customers. Despite his budget constraints, he is willing to invest in tools that promise substantial returns and improved efficiency.
Needs: Brad needs an affordable solution that delivers lead scoring and sales insights without overwhelming complexity. He requires user-friendly tools that can assist him in managing customer relationships and driving foot traffic into his store.
Pain: Brad’s pain points include limited resources for marketing and sales, struggles with lead generation, and the challenge of competing with larger retailers. He often feels overwhelmed by the demands of running the business and finding time to implement effective sales strategies.
Channels: Brad uses local business networks, community groups, and online platforms such as Facebook for promoting his business. He also relies on email communication and engages in networking events.
Usage: Brad interacts with SalesMap AI a few times a week, spending about 1 hour per session to monitor leads and analyze sales data. He uses it primarily for lead management and tracking marketing efforts.
Decision: Brad's decision-making process is influenced by price sensitivity and the potential ROI from tools. He consults with fellow small business owners and seeks tools that are straightforward and cost-effective.

Analytical Alex

Name: Analytical Alex
Description: Analytical Alex is a data analyst who works within a sales team, focusing on collecting and analyzing data to drive sales strategies. He uses SalesMap AI to dig into data, generate reports, and deliver actionable insights to the sales team regarding lead performance and market trends.
Demographics: Age: 28, Gender: Male, Education: Bachelor’s degree in Data Science, Occupation: Data Analyst, Income Level: $65,000 per year, Location: Urban area, operates in a collaborative work environment.
Background: Alex was always drawn to numbers and technology, pursuing a degree in Data Science to fuel his passion. He began his career as a junior analyst and has since taken on more responsibility, enjoying the challenge of uncovering patterns in data. His analytical mindset drives him to seek continuous improvement.
Psychographics: Alex values accuracy, innovation, and the power of data to influence decision-making. He is motivated by the desire to support his team with insights that lead to tangible improvements in sales performance. He has a keen interest in machine learning and programming languages.
Needs: Alex needs sophisticated analytics tools that can provide real-time data visualization and reporting capabilities. He seeks to automate routine tasks to focus on deeper analysis while ensuring his team has the insights they need to thrive.
Pain: Alex's main pain points revolve around data accessibility, the challenge of aligning analytical insights with actionable sales strategies, and time wasted on manual reporting. He feels frustrated when data is siloed or when he lacks the tools needed to present findings effectively.
Channels: Alex consumes information primarily through online research, analytical blogs, and webinars. He engages in industry forums and utilizes platforms like LinkedIn for professional development.
Usage: Alex consistently uses SalesMap AI on a daily basis, averaging 3-4 hours per day. He deeply engages with the data and prepares reports for the sales team to enable informed strategy development.
Decision: Alex's decision-making is heavily influenced by data quality and tool reliability. He makes choices based on analytical outcomes and user feedback, often consulting with colleagues in analytics and sales teams for collaborative inputs.

Strategy Sophia

Name: Strategy Sophia
Description: Strategy Sophia is a senior sales manager in a medium-sized firm, responsible for overseeing the sales department and ensuring alignment with overall business goals. She utilizes SalesMap AI to derive insights from team performance, improve lead management processes, and forecast sales growth accurately.
Demographics: Age: 41, Gender: Female, Education: MBA, Occupation: Senior Sales Manager, Income Level: $90,000 per year, Location: Urban area, thrives in a growth-oriented business environment.
Background: Sophia grew up with a family background in business, sparking her interest in sales and management early on. She earned her MBA and progressed through various sales roles, eventually stepping into management. Her leadership style focuses on collaboration and results-driven tactics. In her personal time, she enjoys mentoring young professionals and participating in community service initiatives.
Psychographics: Sophia values results, collaboration, and strategic thinking. She is motivated by the success of her team and the impact it has on the company's growth. She values innovation and strategic planning as tools for achieving sales excellence and is invested in the professional development of her team.
Needs: Sophia needs tools that deliver comprehensive analytics, streamline communication between sales and marketing, and enhance lead management capabilities. She seeks solutions that facilitate strategic alignment across her organization and provide actionable insights for leadership.
Pain: Sophia's pain points include the need for better communication between departments, challenges in real-time data access, and the struggle to keep her team aligned with shifting business priorities. She often deals with inadequate sales forecasting methods and requires reliable tools to navigate these issues.
Channels: Sophia frequently engages with professional networks like LinkedIn, subscribes to sales and leadership courses, and values industry conferences for networking opportunities. She utilizes email for client and team communication as well as reading leadership content online.
Usage: Sophia typically uses SalesMap AI daily for 1-2 hours, focusing on performance analysis, team coordination, and strategy refinement during team meetings and planning sessions.
Decision: Sophia's decision-making process is strategic and stakeholder-focused; she considers how choices impact overall company goals. She values case studies and real success stories and often collaborates with her peers for advice before implementing new technologies.

Product Ideas

LeadBoost Insights: LeadBoost Insights utilizes advanced AI algorithms to analyze and provide actionable insights on lead engagement, helping sales professionals tailor follow-ups and maximize conversion chances. By integrating behavioral data and feedback loops, this feature enhances lead nurturing strategies for better closure rates.
Smart Campaign Scheduler: The Smart Campaign Scheduler automates the optimal timing and channels for marketing campaigns based on historical performance data and market trends. This feature ensures campaigns reach the right audience at the right time, increasing engagement and conversion rates for SalesMap AI users.
AI-Powered Onboarding Assistant: The AI-Powered Onboarding Assistant guides new users through SalesMap AI, providing personalized tips and recommendations based on their role and goals. This feature streamlines the onboarding process, reducing time to productivity and ensuring users fully leverage the platform's capabilities.
Customizable Dashboard Widgets: Customizable Dashboard Widgets allow users to personalize their SalesMap AI experience by choosing and arranging data visualizations that matter most to their daily operations. This feature enhances user engagement and facilitates quicker access to critical insights for better decision-making.
Dynamic Lead Scoring System: The Dynamic Lead Scoring System evolves based on real-time data inputs and user-defined parameters, ensuring that lead scores reflect the current market conditions and engagement levels. This feature enhances the accuracy of lead prioritization for better sales outcomes.
Intelligent Upsell Alerts: Intelligent Upsell Alerts track customer purchase patterns and trigger suggestions for upsell opportunities. By leveraging data from previous transactions, this feature empowers sales teams to increase average deal sizes and boost revenue.

Product Features

Engagement Heatmap

The Engagement Heatmap provides a visual representation of lead interaction levels, highlighting the most engaged leads during specific timeframes. By identifying peak engagement times and behaviors, sales professionals can prioritize follow-ups more strategically to enhance conversion rates. This feature empowers users to focus their efforts where they are most likely to yield results.

Requirements

Interactive Engagement Visualization

User Story: As a sales professional, I want to see a visual representation of lead interactions so that I can prioritize my follow-up efforts based on the most engaged leads and improve my conversion rates.
Description: The Engagement Heatmap requirement necessitates the development of an interactive visual interface that displays lead interaction levels over specified timeframes. It should incorporate color-coding to represent varying levels of engagement effectively. The heatmap should provide users with the ability to filter data based on different metrics, such as time ranges or specific lead attributes, allowing sales professionals to visualize when and how frequently leads interacted with marketing efforts or communications. This functionality will assist users in identifying patterns that correlate with higher engagement, enabling targeted deployment of sales resources to maximize conversion opportunities. Additionally, the integration of this feature with the existing dashboard will ensure that insights are readily accessible and actionable within the SalesMap AI ecosystem, ultimately enhancing user experience and empowering data-driven decision-making.
Acceptance Criteria: Sales professionals accessing the Engagement Heatmap to analyze lead interactions during a targeted sales campaign using the interactive visual interface.

Given a sales professional opens the Engagement Heatmap, when a specific time frame and lead attributes are selected, then the heatmap should update to display only the lead interaction data corresponding to the selected criteria, with appropriate color-coding representing engagement levels.

A user filtering the Engagement Heatmap data by specific lead attributes, such as industry or lead score, to identify targeted follow-up opportunities.

Given a user chooses to filter the Engagement Heatmap based on lead attributes, when they apply the filter, then the heatmap should refresh and reflect only those leads that meet the specified criteria, visually indicating their engagement levels.

A sales manager reviewing the Engagement Heatmap to identify peak engagement times and strategize follow-ups for high-potential leads.

Given a sales manager views the Engagement Heatmap, when the maximum engagement time is identified, then the system should generate a report summarizing the leads active during that time to facilitate follow-up planning.

Sales representatives utilizing the Engagement Heatmap during a team meeting to discuss engagement trends and devise strategies for approaching leads.

Given a team meeting is in progress, when the Engagement Heatmap is presented, then all team members should be able to see real-time data updates and discuss the visualized engagement patterns clearly on the shared display.

A user testing the responsiveness of the Engagement Heatmap interface on various devices, including mobile and desktop.

Given a user accesses the Engagement Heatmap on a mobile device, when they interact with the heatmap (zoom, filter, etc.), then the interface should respond promptly and retain usability across all devices.

Time-Based Engagement Analytics

User Story: As a sales manager, I want to receive insights into engagement patterns over various timeframes so that I can guide my team on when to reach out to leads for maximum impact.
Description: This requirement outlines the need for analytical capabilities that track and assess lead engagement over different time periods. It will involve the aggregation and analysis of interaction data to identify peak engagement times and corresponding lead behaviors. The feature should utilize predictive analytics to forecast future engagement trends based on historical data, allowing users to anticipate optimal times for outreach. By understanding these dynamics, sales professionals can make informed decisions about their timing and approach, resulting in increased efficiency in their sales strategies. The system must be built to automatically generate insightful reports that summarize these findings, ensuring that users can easily access critical information without manual intervention.
Acceptance Criteria: Tracking Engagement Over Time for Lead Prioritization

Given a selected time frame, when the user accesses the Engagement Heatmap, then the analytics display a clear visual representation of lead engagement metrics during that period, with clearly defined peak engagement times and behaviors.

Automated Report Generation for Engagement Insights

Given that lead engagement data has been tracked, when the user requests a report, then the system automatically generates a comprehensive report summarizing engagement analytics without any manual input required from the user.

Forecasting Future Engagement Trends Based on Historical Data

Given historical lead engagement data, when the user accesses the predictive analytics feature, then the system displays forecasted engagement trends, enabling users to identify optimal outreach times for future campaigns.

Filtering and Sorting Leads by Engagement Levels

Given the Engagement Heatmap is displayed, when the user applies filters to sort leads by engagement levels, then only leads matching the specified engagement criteria are shown in a clear and organized manner.

User Dashboard Integration for Engagement Metrics

Given the user is on the dashboard, when they navigate to the Engagement Heatmap section, then the heatmap is seamlessly integrated and visually coherent, allowing users to quickly interpret engagement data alongside other metrics.

User Notifications for Engagement Changes

Given that a lead's engagement level changes significantly, when the change occurs, then the system generates an automatic notification to the user to prompt timely follow-up actions.

User Training on Engagement Analytics Features

Given that the Engagement Heatmap feature is launched, when users access training materials, then they must find comprehensive resources that effectively explain how to utilize the engagement analytics tools for maximum benefits.

Engagement Score Calculation

User Story: As a sales representative, I want to receive quantitative scores for lead engagement so that I can prioritize my follow-ups more effectively based on measurable interaction data.
Description: This requirement focuses on the implementation of an engagement scoring system that quantifies the engagement levels of leads based on their interactions. The score should consider multiple factors such as email opens, click rates on campaigns, website visits, and social media engagement. This scoring mechanism will allow for a more quantitative approach to lead prioritization, enhancing the existing intelligent lead scoring algorithms in SalesMap AI. By providing a clearer picture of lead engagement, sales teams can prioritize follow-ups with higher-scoring leads, thus optimizing their efforts and resource allocation effectively. This requirement also includes the need for users to access these scores easily via the engagement heatmap interface, ensuring seamless integration of this valuable metric into daily workflows.
Acceptance Criteria: Sales professionals are using the Engagement Score Calculation feature to evaluate leads based on their interaction history and prioritize which leads to follow up with in their sales strategy meetings.

Given that a user has access to the Engagement Heatmap, when they view the engagement scores of leads, then the scores reflect an accurate calculation based on the latest available interaction data.

A sales manager wants to ensure that the engagement scoring framework is functioning correctly for different interaction types, including email, website visits, and social media engagement.

Given a lead with specific interaction data, when the engagement score is calculated, then it should sum the contribution of each interaction type according to the predefined weightings set in the system.

In a scenario where sales representatives check the heatmap during a sales call preparation, they need to understand which leads have the highest engagement scores to focus their follow-up efforts.

Given that leads have a range of engagement scores, when the user filters the heatmap by engagement level, then only leads with scores above a certain threshold should be displayed on the interface.

Users need to gain insights quickly from the Engagement Heatmap regarding lead activity patterns over the last month to make rapid decisions.

Given that the heatmap can display data for specific time frames, when a user selects the last 30 days, then the heatmap should show an accurate representation of lead engagement levels during that period.

A sales representative wants to verify that the Engagement Score Calculation reflects changes in lead interactions in near real-time to adjust their strategy accordingly.

Given that lead interactions are occurring frequently, when a lead's interaction data is updated, then the engagement score should automatically refresh and display the updated score within a user-friendly timeframe.

Customizable Notifications for Engagement Alerts

User Story: As a sales professional, I want to receive real-time alerts when my leads show significant engagement so that I can respond promptly and improve my chances of converting them into customers.
Description: The Engagement Heatmap must include a feature that allows users to set custom notifications regarding lead engagement activities. Users should be able to configure alerts based on specific thresholds or behaviors, such as when a lead reaches a certain interaction score or engages at critical times. This functionality will serve to keep users informed of significant lead activities in real-time, enabling immediate responses from sales professionals. Alerts could be provided through various channels, such as email and in-app notifications, ensuring that important interactions are never missed. This requirement supports the objective of improving conversion rates by ensuring timely follow-up with engaged leads.
Acceptance Criteria: User Configures Custom Notification for Lead Engagement

Given a user is logged into SalesMap AI, when they navigate to the Engagement Heatmap settings and set a notification for a lead reaching an interaction score of 80 or above, then the user should receive an alert via email when a lead meets this threshold.

User Receives Notifications for Critical Engagements

Given a user has configured a notification for engagements at critical times, when a lead engages during the specified peak hours, then the user should receive an in-app notification immediately after the engagement occurs.

User Invalidates and Reconfigures Notifications

Given a user has an existing notification for lead engagement, when they change the threshold from an interaction score of 80 to 70, then the previous notification should be invalidated, and a new notification should be created and activated for the new threshold.

User Tests Notification Functionality

Given a user has configured a custom notification, when a lead simulates engagement that meets the configured threshold, then the user should see the notification displayed in the app and received via email within 5 minutes of the engagement.

User Views Notification History for Engagement Alerts

Given a user has received multiple notifications regarding lead engagements, when they navigate to the notification history section, then they should see a complete list of all past notifications along with timestamps and lead details.

User Sets Multiple Custom Notifications

Given a user wants to keep track of various leads, when they configure multiple notifications with different thresholds for several leads, then all respective notifications should be active, and users should receive alerts as each threshold is met.

User Adjusts Notification Preferences

Given a user is in the engagement notifications settings, when they choose to receive alerts only through in-app notifications and disable email alerts, then they should no longer receive email alerts for engagement activities starting from that moment.

User Training and Support Documentation

User Story: As a new user of SalesMap AI, I want to access training and support materials on the Engagement Heatmap so that I can learn how to use the feature effectively and get the most out of the tool.
Description: This requirement emphasizes the necessity for comprehensive training resources and support documentation regarding the Engagement Heatmap feature. Given the novelty of this tool, it is crucial for users to have access to user-friendly training materials, including guides, video tutorials, and FAQs that explain how to effectively use and interpret the heatmap, analytical insights, and scoring system. Establishing a well-structured support system will enhance user adoption rates and ensure that customers maximize the utility of the Engagement Heatmap to drive their sales efforts. It is imperative that the documentation is easily accessible within the SalesMap AI platform, fostering a user-friendly experience.
Acceptance Criteria: User accesses the Engagement Heatmap for the first time and seeks guidance on how to interpret the data presented.

Given the user is on the Engagement Heatmap page, when they click on the 'Help' icon, then a user-friendly guide should be displayed that clearly explains how to interpret the heatmap, including definitions of key terms and visuals.

A user wants to learn how to identify peak engagement times using the Engagement Heatmap feature.

Given the user navigates to the training section of the platform, when they select the video tutorial for the Engagement Heatmap, then the video should start playing and cover the process of identifying peak engagement times in under 5 minutes.

A user encounters an issue with the Engagement Heatmap and requires immediate support.

Given the user is on the Engagement Heatmap page when they click the 'Support' button, then they should be redirected to a FAQ section that includes common issues and solutions specific to the Engagement Heatmap feature.

Sales leads want to access historical data to compare engagement trends over multiple months.

Given the user is using the Engagement Heatmap, when they select the date range filter to view engagement for the past six months, then the heatmap should update to accurately reflect the engagement data for that period.

User needs to provide feedback on the Engagement Heatmap training materials after trying them out.

Given the user has completed viewing the training materials, when they are prompted to submit feedback, then they should have access to a feedback form that collects their comments on clarity, utility, and suggestions for improvement.

New users require a structured onboarding process specifically for the Engagement Heatmap feature.

Given a new user logs into the SalesMap AI platform for the first time, when they are guided through an onboarding tutorial, then the tutorial should include a specific section on the Engagement Heatmap, detailing its features and benefits, lasting no longer than 10 minutes.

A user wants to quickly access documentation without navigating away from the Engagement Heatmap.

Given the user is on the Engagement Heatmap page, when they click on 'Documentation' from the sidebar, then the documentation should open in a modal window without navigating away from the current page, allowing for seamless use of the feature while viewing the documentation.

Lead Activity Tracker

The Lead Activity Tracker monitors and logs all interactions a lead has with marketing materials, emails, and website content. This comprehensive view of lead behavior allows sales teams to tailor follow-ups and targeted messaging based on actual engagement patterns. Users benefit from deeper insights into lead interests, enabling more personalized and effective communications.

Requirements

User Interaction Logging

User Story: As a sales representative, I want to see a detailed log of all interactions my leads have had with our marketing content, so that I can personalize my follow-up strategies and improve my chances of closing deals.
Description: The User Interaction Logging requirement involves capturing and storing detailed records of all user interactions with various marketing materials, emails, and website content associated with leads. This feature will enhance user insights by allowing sales teams to access comprehensive engagement data, which can be utilized to tailor follow-up strategies and optimize communication efforts. By analyzing this information, sales teams can identify trends, understand user preferences, and create personalized outreach that resonates with leads. The integration of this feature within SalesMap AI will fundamentally improve lead conversion rates and enhance client's overall experience, providing a clear competitive advantage in sales automation.
Acceptance Criteria: User Interaction Logging for Marketing Emails Sent to Leads

Given a lead receives a marketing email, when the lead opens the email, then the interaction is logged in the system with a timestamp and the email subject line.

User Interaction Logging for Website Visits by Leads

Given a lead visits the SalesMap AI website, when the lead navigates to different pages, then all page visits are logged with timestamps and duration on each page.

User Interaction Logging for Downloading Marketing Materials

Given a lead downloads a marketing resource (e.g., eBook, whitepaper), when the download occurs, then the interaction is logged with the resource name, lead ID, and datetime of the download.

User Interaction Logging for Event Participation

Given a lead participates in a virtual event hosted by SalesMap AI, when the lead completes the registration and attends the event, then both the registration and attendance are logged with relevant details in the lead's profile.

User Interaction Logging for Click-Through Rates on Emails

Given a lead clicks a link in a marketing email, when the click event occurs, then it is logged with the URL clicked, lead ID, and datetime of the action.

User Interaction Logging for Overall Engagement Summary

Given multiple logged interactions for a lead, when a sales rep views the lead profile, then a summary displaying total interactions and engagement metrics is available, with insights derived from the logged data.

User Interaction Logging for Social Media Engagement

Given a lead engages with posts shared on social media, when a lead visits SalesMap AI's social media link or clicks on shared content, then the interaction is logged with the specific post details and datetime.

Lead Scoring Integration

User Story: As a sales manager, I want to see a lead scoring system that evaluates how engaged my leads are, so that I can allocate my resources efficiently and focus on leads with the highest conversion potential.
Description: This requirement focuses on integrating a lead scoring mechanism that assesses the engagement level of leads based on their interactions, such as email opens, clicks, and time spent on specific pages. By leveraging AI algorithms, this feature will quantify potential conversion likelihood, enabling sales teams to prioritize their efforts on leads exhibiting high engagement. This integration will provide visual scoring indicators in the dashboard, allowing users to quickly identify which leads require immediate attention versus those that may not be as promising. This scoring system will significantly sharpen the focus of sales efforts, enhancing operational efficiency and bolstering overall sales effectiveness.
Acceptance Criteria: Monitoring Engagement Levels of Leads through Email Campaigns

Given a lead has received an email campaign, when the lead opens and interacts with the email, then the lead's engagement score should increase by a defined metric indicating their interest level.

Assessing Page Interactions on the Website

Given a lead visits specific pages on the SalesMap AI website, when the lead spends a significant amount of time on a high-value page, then the lead's engagement score should reflect this activity and increase accordingly.

Visual Representation of Lead Scores on Dashboard

Given that lead scores are generated based on interactions, when a user accesses the dashboard, then lead scores should be clearly displayed with visual indicators showing high, medium, and low engagement levels.

Updating Lead Scores with New Interactions

Given that a lead has a recorded engagement score, when the lead performs a new interaction, then the score should be updated in real-time to reflect the latest engagement data.

Prioritizing Leads Based on Engagement Scores

Given a list of leads with varying engagement scores, when a sales representative accesses the lead list, then leads should be sorted automatically with high-scoring leads at the top for optimized follow-up.

Notification of High-Engagement Leads

Given that a lead's engagement score exceeds a certain threshold, when this score is reached, then the sales team should receive an automatic notification to follow up with the lead immediately.

Custom Reporting Features

User Story: As a marketing analyst, I want to create custom reports that highlight important metrics about lead interactions and campaign performance, so that I can derive insights and inform our sales strategy.
Description: The Custom Reporting Features requirement will enable users to generate tailored reports based on specific interactions, lead scores, and campaign performance metrics. This functionality will allow sales teams to analyze data more effectively by customizing reports to focus on relevant KPIs, such as conversion rates, engagement levels, and the success of different marketing strategies. Users will be able to easily visualize their data through charts and graphs, enhancing their ability to derive actionable insights from their sales activities. Ultimately, this feature will empower organizations to make data-driven decisions and refine their sales tactics for greater results.
Acceptance Criteria: User generates a custom report displaying lead activity over the last 30 days with specific filters applied, including lead scores and interaction types.

Given the user accesses the custom reporting module, when they select filters for lead activity, lead scores, and date range, then the system displays a report summarizing the chosen data in a clear format with visual charts and graphs.

Sales team members want to analyze the performance of a specific marketing campaign using the custom reporting features.

Given the user is on the reporting dashboard, when they select the campaign from the list and run the report, then the report accurately reflects the conversion rates, engagement levels, and outcomes associated with that campaign.

User creates a report to compare lead engagement levels across multiple campaigns to determine which marketing strategy performed best.

Given the user is on the custom report creation page, when they select multiple campaigns and request a comparative analysis report, then the system generates a side-by-side comparison chart highlighting key performance indicators for each campaign.

A user wants to save a frequently used report format for future access and quick generation.

Given the user has customized a report with selected filters and metrics, when they choose to save this configuration, then the system prompts for a name and successfully stores the report format for future use.

Sales manager needs to track and visualize overall sales performance metrics based on custom reporting functionality monthly.

Given the user accesses the custom reporting feature, when they specify the report parameters for the last month, then the report generated shows accurate monthly sales performance metrics with appropriate visualization tools (charts and tables).

Real-Time Notifications

User Story: As a sales agent, I want to receive real-time notifications whenever my leads interact with our content, so that I can reach out to them promptly and improve conversion rates.
Description: The Real-Time Notifications requirement aims to provide immediate alerts to sales representatives whenever a lead engages with marketing content, such as downloading resources or requesting demos. This feature will facilitate timely follow-ups, allowing teams to act on engagement quickly and improve their chances of conversion. Notifications can be configured to be received via email or in-app, ensuring that sales efforts can be synchronized seamlessly with lead actions. Enabling this capability will enhance responsiveness and foster stronger communication between leads and sales teams, ultimately driving higher engagement and conversion rates.
Acceptance Criteria: Sales representative receives a notification when a lead downloads a whitepaper.

Given a lead downloads a whitepaper, when the action is logged, then the sales representative assigned to that lead should receive an immediate email notification and an in-app alert.

Sales representative is notified of a demo request from a lead.

Given a lead requests a demo, when the request is made, then the sales representative should receive an email notification within 1 minute and an in-app alert.

Sales representative receives notifications for multiple leads engaging with content.

Given multiple leads engage with marketing materials simultaneously, when the activities are logged, then notifications for all leads should be sent to the respective sales representatives within 2 minutes.

Sales representative is notified when a lead revisits key website pages.

Given a lead revisits key pages on the company website, when the page views are tracked, then the sales representative assigned to that lead should receive a notification summarizing the pages viewed within 3 minutes.

Sales representative can configure notification preferences.

Given the sales representative accesses notification settings, when they configure their preferences (email vs. in-app notifications), then the updated preferences should be saved and applied to future notifications.

Sales representative receives notifications for engagement patterns over time.

Given a predefined period of lead engagement, when the lead engages with marketing content multiple times, then the sales representative should receive a summary notification detailing the overall engagement patterns at the end of the period.

Sales representative's notifications are logged for review.

Given a notification is sent to the sales representative, when the notification occurs, then it should be logged in the system for review, including the timestamp, lead details, and type of engagement.

User Dashboard Customization

User Story: As a sales rep, I want to customize my dashboard to display the metrics that are most important to me, so that I can focus on what matters most in my sales process.
Description: The User Dashboard Customization requirement allows users to personalize their dashboards according to their specific needs and preferences. This includes the ability to select and arrange the most relevant metrics, visuals, and data points they wish to monitor regarding lead activities and performance. By offering this level of customization, users can streamline their workflow and focus on the insights that matter most to them. This feature will increase engagement with the platform as users will be able to create a workspace that aligns with their individual operational needs, thereby enhancing overall productivity and satisfaction with the SalesMap AI platform.
Acceptance Criteria: User accesses the User Dashboard to customize their view to focus on specific lead metrics.

Given the user is logged into SalesMap AI, when they navigate to the Dashboard customization settings, then they should be able to drag and drop metrics to rearrange them according to their preferences, and see the changes reflected immediately on their dashboard.

The user selects specific metrics to display on their dashboard.

Given the user is in the Dashboard customization settings, when they select metrics from a list of available options and confirm their selection, then those metrics should be displayed prominently on their dashboard, with no more than two clicks required to access additional metrics.

User resets their dashboard to default settings.

Given the user has customized their dashboard, when they choose to reset their dashboard to default settings, then all customizations should be cleared and the dashboard should revert to the initial default view without any residual settings applied.

User saves multiple dashboard configurations for different use cases.

Given the user has multiple dashboard configurations saved, when they select a different configuration, then the dashboard should update instantly to reflect the newly selected layout and metrics.

User experiences a seamless update in real-time data on their customized dashboard.

Given the user has customized their dashboard, when new lead activity data is generated, then the displayed metrics should update in real-time without requiring manual refresh, maintaining a high level of accuracy.

User seeks assistance on customizing their dashboard through tooltips or support documentation.

Given the user is in the customization settings, when they hover over customizable elements, then informative tooltips should appear that guide them on what each metric represents and how to modify their dashboard effectively.

User shares their customized dashboard view with team members for collaboration.

Given the user has a customized dashboard, when they use the sharing feature to send their dashboard view to another user, then the recipient should receive an accurate snapshot of that dashboard configuration with their access permission intact.

Conversion Probability Score

The Conversion Probability Score utilizes predictive analytics to assess the likelihood of a lead converting based on past behaviors and interactions. By prioritizing leads with higher conversion probabilities, sales professionals can allocate their time and resources more efficiently, improving overall sales performance and reducing the time spent on less promising leads.

Requirements

Dynamic Lead Scoring

User Story: As a sales professional, I want to receive real-time lead scores so that I can prioritize my outreach efforts towards leads with the highest potential for conversion.
Description: Dynamic Lead Scoring enables the real-time evaluation of leads based on their characteristics and engagement. It factors in multiple data points such as interaction history, demographic information, and lead behavior to assign a real-time conversion score. This feature enhances sales professionals' ability to prioritize leads more effectively, ensuring that they focus on high-value prospects that are more likely to convert, thereby optimizing the sales process and increasing conversion rates. The integration with SalesMap AI's existing predictive analytics framework ensures that leads are scored accurately and consistently as new data is fed into the system, providing continuous updates and improving decision-making.
Acceptance Criteria: Real-time Evaluation of Lead Scoring During Sales Call

Given a sales professional is on a call with a lead, when the Dynamic Lead Scoring is activated, then the conversion probability score should update in real-time based on the latest engagement data and display the updated score on the dashboard.

Integration with Predictive Analytics Framework

Given that new lead data is added to the system, when the data is processed by the predictive analytics framework, then the conversion probability scores for all existing leads should be recalculated and updated accordingly.

Displaying Prioritized Leads in Dashboard

Given that the Dynamic Lead Scoring has been applied, when the sales professional views the dashboard, then the leads should be displayed in descending order based on their conversion probability scores.

Notification for Scoring Updates

Given that the conversion probability score for a lead changes significantly, when this change occurs, then the sales professional should receive a notification alerting them of this update on their dashboard.

Daily Summary of Lead Scoring Changes

Given that the sales day has ended, when the sales professional checks the summary report, then they should see a comprehensive log of any changes in conversion probability scores for leads over the last 24 hours.

User Feedback on Scoring Accuracy

Given that sales professionals are using the Dynamic Lead Scoring feature, when they provide feedback on lead scoring accuracy, then this feedback should be logged and analyzed for future iterations of the scoring algorithm.

Performance Metrics for Lead Conversion After Implementation

Given that the Dynamic Lead Scoring feature has been implemented for one month, when performance metrics are analyzed, then there should be a measurable increase in lead conversion rates compared to the previous month.

Historical Data Analysis

User Story: As a sales manager, I want to analyze historical conversion data so that I can refine our sales strategies based on proven success factors and improve future performance.
Description: Historical Data Analysis provides insights into past sales performance, enabling users to identify patterns and trends in lead conversion. By analyzing previous interactions and conversion success rates, sales teams can understand which factors correlate with successful leads and adjust their strategies accordingly. This feature not only assists in training new sales team members by providing them with context about the effectiveness of different approaches but also helps seasoned professionals refine their techniques based on concrete data. It integrates seamlessly with the dashboard, presenting historical data visualizations that facilitate a clearer understanding of past sales cycles.
Acceptance Criteria: Historical Data Insights Utilization by Sales Team Members during Strategy Meetings

Given that a sales team member has access to the dashboard, when they navigate to the Historical Data Analysis section, then they should see visualizations of past sales performance and conversion success rates, with at least 90% accuracy in representing the actual historical data.

Training New Sales Recruits with Historical Data Analysis Examples

Given that a new sales recruit is in training, when they access the Historical Data Analysis feature, then they should be able to view at least three specific examples of past sales strategies that led to successful conversions and receive training materials relevant to those examples.

Identifying Trends in Lead Conversion Success Rates

Given that the user has selected a specific time period in the Historical Data Analysis feature, when they analyze the corresponding data visualizations, then they should be able to identify at least two clear patterns or trends that indicate successful lead conversion factors.

Adjusting Sales Strategies Based on Historical Data Findings

Given that a sales strategist is reviewing the Historical Data Analysis insights, when they make adjustments to their current sales approach based on the insights gained, then they should see an improvement in conversion probability scores for leads within two weeks, measured by at least a 15% increase.

Real-time Dashboard Interaction for Historical Data

Given that the user views the real-time dashboard, when they interact with the Historical Data Analysis component, then the dashboard should refresh and display accurate visualizations without lag time exceeding 2 seconds.

Comparison of Multiple Historical Data Sets for Strategy Optimization

Given that the user selects multiple historical data sets for comparison in the Historical Data Analysis feature, when they generate a comparison report, then the report should display a side-by-side analysis of key metrics with a clear summary of insights within 5 seconds.

Exporting Historical Data Analysis Reports for Team Review

Given that the user completes analysis in the Historical Data Analysis feature, when they choose to export the report, then the exported document should correctly reflect all visualizations and data points from the analysis, with no data loss or formatting issues.

Lead Segmentation Tools

User Story: As a marketing analyst, I want to segment leads based on specific characteristics so that I can create targeted campaigns for each group, improving engagement and conversion rates.
Description: Lead Segmentation Tools allow users to categorize leads into distinct groups based on specific criteria or behaviors. By segmenting leads, sales teams can tailor their approach and communications to resonate with different audiences, improving engagement and conversion likelihood. This feature integrates with the existing sales workflow, providing filters and tagging capabilities within the user interface to streamline the segmentation process. The ability to create custom segments based on various attributes enhances personalization in outreach strategies, ultimately leading to higher success rates in converting leads into customers.
Acceptance Criteria: User successfully classifies leads based on multiple criteria using the Lead Segmentation Tools.

Given the user has accessed the Lead Segmentation Tools, when they input specific criteria (such as industry, engagement score, and location), then the system should display segmented leads that match all selected criteria.

Sales team effectively uses custom segments to target communications to leads.

Given the user has created a custom segment labeled 'High Value Prospects', when they initiate an outreach campaign, then the system should allow the user to select this segment and send personalized messages to all leads within it.

User applies filters to segment leads within the dashboard effectively.

Given the user is on the lead management dashboard, when they select filters for lead scoring and interaction history, then the system should refresh the lead list to reflect only those leads that meet the specified criteria.

User checks the performance of segments in terms of conversion rates post-campaign.

Given the user has executed a targeted campaign, when they review the campaign's analytics, then the system should show a clear comparison of conversion rates between segmented leads and non-segmented leads.

User receives system notifications for low engagement scores in specific segments.

Given the system monitors lead engagement, when a lead within a created segment drops below a set engagement threshold, then the user should receive a notification prompting a follow-up action.

Admin modifies existing lead segments based on new business strategies.

Given the user is an admin in the system, when they update the criteria for an existing lead segment, then the system should reflect changes immediately and notify all team members impacted by this modification.

User validates the accuracy of leads within a specific segment.

Given the user selects a segment, when they review the detailed information of the leads within it, then the system should ensure all leads listed meet the established criteria without discrepancies.

Automated Campaign Recommendations

User Story: As a campaign manager, I want to receive automated recommendations for lead engagement strategies so that I can quickly implement effective campaigns without extensive manual research.
Description: Automated Campaign Recommendations use machine learning algorithms to analyze lead data and suggest optimal marketing strategies. By examining characteristics such as lead preferences, past interactions, and predicted conversion probabilities, this feature helps sales and marketing teams design campaigns that are tailored for maximum impact. The integration of this tool within the SalesMap AI platform maximizes efficiency by minimizing manual research time, empowering teams to implement data-driven strategies swiftly. Recommendations are presented in an intuitive format, allowing for rapid decision-making and improved campaign execution.
Acceptance Criteria: User receives automated campaign recommendations based on lead data analysis.

Given a user has uploaded lead data into the SalesMap AI platform, When the system analyzes the lead data, Then the user should receive a list of recommended marketing strategies that are personalized to the specific characteristics of the leads.

User can view recommendations in an intuitive dashboard format.

Given the user accesses the recommendations dashboard, When the system displays the campaign suggestions, Then the recommendations should be organized clearly with metrics on predicted success and lead engagement.

User can implement recommendations directly from the dashboard.

Given a list of campaign recommendations is displayed in the dashboard, When the user selects a campaign to implement, Then the system should provide an option to execute the campaign or save it for later.

User is notified of updates to the conversion probability scores of leads.

Given the system updates conversion probability scores based on new lead interactions, When the scores change, Then the user should receive a notification highlighting the changes and suggesting any revised campaign recommendations.

User can filter recommendations based on specific criteria such as industry or lead score.

Given the user is on the recommendations dashboard, When the user applies filters (e.g., industry or lead score), Then the displayed recommendations should update to reflect only those that meet the applied criteria.

User can provide feedback on the effectiveness of implemented campaigns.

Given the user has executed a campaign based on recommendations, When the user assesses the campaign results, Then the user should be able to submit feedback regarding the success of the recommendations to improve future suggestions.

Real-Time Dashboard Enhancements

User Story: As a sales director, I want to view real-time metrics on lead conversions and campaign performance so that I can make quick adjustments and drive team success effectively.
Description: Real-Time Dashboard Enhancements provide users with live updates on lead status, conversion scores, and campaign performance metrics. This feature is critical for sales teams as it allows them to make informed decisions quickly during the sales process. Integrating real-time analytics with visual representation, users can easily track their performance indicators and adjust strategies on the fly. The enhanced dashboard will facilitate better collaboration within teams as well, by allowing users to share insights and updates in a centralized manner, ensuring everyone is aligned toward common goals.
Acceptance Criteria: Dashboard User Engages with Real-Time Data During Sales Call

Given the user is logged into SalesMap AI and on the Real-Time Dashboard, when a sales team member initiates a sales call, then the conversion probability scores for all active leads should display instantly and accurately, allowing for immediate adjustments in strategy during the conversation.

User Receives Notifications for Lead Status Changes

Given the user has enabled notifications for lead status changes, when a lead status updates in real-time, then the user should receive an immediate alert on the dashboard without any delays, ensuring they are always informed of critical changes.

Team Member Collaborates Using Shared Dashboard Insights

Given multiple team members are viewing the Real-Time Dashboard, when one team member shares insights or comments about a lead's conversion score, then all other members should see the updates in real-time and be able to respond or act upon the new information efficiently.

Analysis of Campaign Performance Metrics on the Dashboard

Given the user wants to evaluate the impact of their current campaigns, when they access the Real-Time Dashboard, then they should see segmented campaign performance metrics that update live, including click-through rates and conversion rates, reflecting the most current data available.

User Tracks Historical Lead Performance via Dashboard

Given the user has accessed the Real-Time Dashboard, when they click on a lead's historical data, then they should be able to view a complete history of that lead's interactions and conversion probability scores in a clear and visually engaging format.

User Filters Leads Based on Conversion Probability

Given the user is on the Real-Time Dashboard, when they apply filters to view leads with a conversion probability score above a certain threshold, then the dashboard should dynamically update to only show those leads in real-time, aiding in prioritization efforts.

User Customizes the Dashboard Layout for Optimal Viewing

Given the user prefers a personalized dashboard experience, when they adjust widgets and metrics on the Real-Time Dashboard, then their customized layout should save automatically and reflect their preferences the next time they log in.

Feedback Loop Optimization

Feedback Loop Optimization integrates customer feedback with lead engagement data to refine lead nurturing strategies continuously. By analyzing responses and behaviors, this feature helps sales teams adjust their approach in real-time, ensuring that interactions are relevant and timely. Users gain the advantage of a dynamic strategy that evolves with customer needs.

Requirements

Dynamic Feedback Analysis

User Story: As a sales representative, I want to analyze customer feedback in real-time so that I can adjust my lead engagement strategies promptly and improve my chances of converting leads into customers.
Description: The Dynamic Feedback Analysis requirement ensures that customer feedback is captured and analyzed in real-time, allowing sales teams to identify trends and patterns in lead engagement. This functionality leverages AI algorithms to process customer responses, helping to refine lead nurturing strategies. Integration within the SalesMap AI platform means that insights can directly inform sales tactics, making interactions more relevant. The benefits include increased response rates from prospects, personalized sales strategies that evolve with customer preferences, and ultimately higher conversion rates. This requirement is crucial for maintaining a competitive edge in sales strategies by ensuring that they are always informed by up-to-date customer insights.
Acceptance Criteria: Real-Time Customer Feedback Analysis during Sales Interaction

Given a sales team is engaging with a lead, when customer feedback is received through the system, then the system must analyze the feedback and present actionable insights within 2 minutes to adjust the sales strategy accordingly.

Integration of Lead Engagement Data with Feedback Analysis

Given that customer feedback has been collected, when the system processes both feedback and lead engagement data, then the insights generated must suggest at least three personalized actions for the sales team relevant to the specific lead's interests and behaviors.

Automatic Adjustment of Lead Nurturing Strategies based on Feedback

Given that feedback has been analyzed, when a pattern indicating changing customer preferences is detected, then the system must automatically update the lead nurturing strategies within the CRM to reflect this new information without manual intervention.

User Dashboard Display of Feedback Trends

Given that feedback data is being processed, when a user accesses the dashboard, then they must see a visual representation of feedback trends and engagement metrics updated in real-time, allowing immediate strategic adjustments.

User Notifications for Significant Feedback Changes

Given that significant changes in customer feedback patterns have been identified, when users are logged into the platform, then they must receive notifications summarizing these changes and recommended adjustments to their sales approach.

Lead Engagement Scoring Module

User Story: As a sales manager, I want to score leads based on their engagement levels so that I can allocate my team’s efforts more effectively towards those leads most likely to convert.
Description: The Lead Engagement Scoring Module is designed to quantify and qualify interactions with leads based on their behaviors and feedback. This requirement will utilize historical data and AI-driven analytics to assign scores to leads based on their level of engagement. It will integrate seamlessly with the existing lead scoring system in SalesMap AI, allowing users to prioritize leads not only by traditional scoring metrics but also by their responsiveness to feedback. This ensures that sales teams focus their efforts on leads that are demonstrating active interest, leading to more effective use of resources and higher conversion rates. The expected outcome is a more efficient sales process that maximizes the potential of each lead based on real-time engagement data.
Acceptance Criteria: Lead scoring update based on recent engagement data.

Given a lead has interacted with the SalesMap AI platform in the last 30 days, when their engagement score is recalculated, then it should reflect an increase or decrease based on the new data collected during that period, with documentation of the scoring formula used.

Integration with existing lead scoring system.

Given the Lead Engagement Scoring Module is implemented, when a lead's engagement data is processed, then it must integrate seamlessly with the existing lead scoring system without causing any data loss or discrepancies, and all scores should be visible in the CRM dashboard.

Real-time adjustments to lead nurturing strategies.

Given that a lead's engagement score has changed, when the sales team accesses the feedback dashboard, then they must see updated recommendations for nurturing strategies that reflect this change within 5 minutes of the updated score calculation.

User interface feedback for lead engagement scores.

Given that the scoring module has been implemented, when users view lead profiles, then engagement scores should be displayed clearly with color-coded indicators of low, medium, and high engagement levels to enhance user comprehension.

Historical data validation in scoring algorithm.

Given historical lead engagement data is used as input for the scoring module, when the system calculates the scores, then the output for a sample dataset should match 95% accuracy against manually calculated scores from the historical records within a testing environment.

Reporting on lead engagement trends over time.

Given the Lead Engagement Scoring Module is active, when users generate a report over a 6-month period, then the report must accurately reflect trends in lead engagement scores, including percentage changes and potential correlations to campaign adjustments made.

Automated alert system for low engagement leads.

Given a lead has an engagement score that falls below a defined threshold, when this occurs, then an automated alert should be sent to the responsible sales representative immediately, ensuring prompt action can be taken for lead re-engagement.

Automated Engagement Adjustment Suggestions

User Story: As a sales rep, I want to receive automated suggestions for adjusting my engagement tactics so that I can respond to leads more effectively and increase my chances of closing deals.
Description: Automated Engagement Adjustment Suggestions will provide sales teams with actionable recommendations for modifying their approach to nurturing leads based on real-time feedback. This feature integrates with the feedback analysis and engagement scoring modules, utilizing machine learning to suggest personalized communication and outreach strategies. By empowering sales teams with dynamic suggestions, the aim is to enhance engagement by ensuring that communications are contextually relevant and timely. This will ultimately lead to improved lead conversion rates and customer satisfaction, as responses are tailored to the immediate needs and behaviors of leads. This requirement is vital for ensuring responsiveness in sales strategies.
Acceptance Criteria: Sales team receives actionable engagement adjustment suggestions based on real-time feedback from recent customer interactions during a weekly strategy meeting.

Given that the sales team is in the strategy meeting, when they access the feedback loop optimization dashboard, then they should see at least three actionable suggestions for engagement adjustment based on customer feedback within the last 24 hours.

Sales representatives are executing a follow-up call to a lead after receiving automated engagement suggestions.

Given that a sales representative has received engagement suggestions for a specific lead, when they conduct the call, then they should report a 20% increase in positive responses compared to calls made without engagement suggestions.

A sales team member reviews the historical performance of engagement adjustment suggestions over the past month to identify patterns in lead responses.

Given that a sales team member reviews historical data, when they analyze the effectiveness of the automated suggestions, then they should find at least 75% of the suggestions led to improved lead engagement metrics.

Sales teams implement new email templates based on automated engagement suggestions to nurture leads better.

Given the sales team uses the new email templates derived from engagement suggestions, when they send these emails, then at least 50% of the leads should respond positively within a week.

Sales managers want to assess the impact of engagement adjustment suggestions on overall lead conversion rates over a quarter.

Given that the quarter has ended, when the sales manager reviews the lead conversion report, then they should see at least a 15% increase in conversion rates attributed to the use of automated engagement adjustment suggestions.

A sales team conducts a training session to familiarize staff with interpreting and implementing automated engagement suggestions.

Given that the sales staff have completed the training session, when assessed, then at least 90% of staff should demonstrate understanding of how to interpret and apply the suggestions effectively.

Sales representatives provide feedback on the usefulness of automated engagement suggestions after several weeks of use.

Given that sales representatives submit feedback, when compiled, then at least 80% of respondents should rate the suggestions as useful or highly useful for improving their engagement with leads.

Customer Feedback Integration Dashboard

User Story: As a sales team leader, I want to have a single dashboard where I can view customer feedback and engagement metrics so that I can understand how to improve our sales strategies and identify training needs for my team.
Description: The Customer Feedback Integration Dashboard provides a centralized view for sales teams to access all customer feedback and engagement data in one place. This requirement focuses on designing an intuitive interface that aggregates feedback, engagement scores, and pertinent metrics, allowing users to easily track and analyze customer interactions. The dashboard will facilitate informed decision-making by providing insights into trends and areas needing attention. By enhancing visibility into how feedback impacts engagement efforts, sales representatives can stay strategically aligned with customer expectations. It serves as a critical tool for sales performance monitoring and enrichment.
Acceptance Criteria: Sales representatives access the Customer Feedback Integration Dashboard to review customer feedback and engagement data prior to a sales meeting.

Given the user is logged into the SalesMap AI platform, when they navigate to the Customer Feedback Integration Dashboard, then they should see a consolidated view of customer feedback and engagement metrics from the past 30 days, with no errors in data loading.

A sales manager uses the dashboard to identify trends in customer feedback and adjust sales strategies accordingly.

Given the sales manager reviews the Customer Feedback Integration Dashboard, when they filter customer feedback by product and sort by engagement score, then they should see a prioritized list of feedback items with corresponding engagement metrics, highlighting key trends.

Users receive real-time alerts for significant changes in customer feedback or engagement scores.

Given a user has set up notifications for the Customer Feedback Integration Dashboard, when a customer's engagement score decreases by more than 20% compared to the previous week, then the user should receive a real-time notification on their dashboard and via email.

Sales representatives analyze customer feedback to personalize their follow-up strategies.

Given that the user views individual customer feedback details from the dashboard, when they identify specific comments requesting a product demonstration, then they should have the option to create a follow-up task directly from the dashboard with a link to the customer's feedback.

A user exports customer feedback data for an internal presentation.

Given the user is on the Customer Feedback Integration Dashboard, when they select the export option for customer feedback as a CSV file, then they should successfully download a file containing all current feedback entries and engagement metrics without data loss or corruption.

Sales teams review feedback trends over a specified period to enhance strategic alignment.

Given the user selects a date range from the Customer Feedback Integration Dashboard, when they generate the feedback trend report, then they should see a visual representation of feedback trends with clear insights on engagement changes over that period.

The dashboard integrates customer feedback with other sales metrics to provide comprehensive insights.

Given the user accesses the Customer Feedback Integration Dashboard, when they cross-reference customer feedback with sales performance data, then they should see a combined analytics view that illustrates the relationship between customer feedback and sales conversion rates.

Lead Segmentation Insights

Lead Segmentation Insights categorizes leads based on engagement metrics, behavioral patterns, and demographic data, allowing sales teams to craft tailored follow-up strategies for different segments. This targeted approach increases the relevance of communications and improves the chances of conversion, ensuring that each lead receives the most appropriate and personalized engagement.

Requirements

Engagement Metric Tracking

User Story: As a sales manager, I want to track lead engagement metrics so that I can identify which leads are most interested and tailor my follow-up strategies accordingly.
Description: This requirement focuses on the systematic collection and analysis of engagement metrics such as email opens, clicks, website visits, and social media interactions. By implementing robust tracking mechanisms, the software can generate valuable data regarding lead interactions, which in turn allows for dynamic segmentation based on real-time behaviors. This capability enhances the precision of lead categorization and boosts the effectiveness of personalized outreach efforts, directly improving conversion rates.
Acceptance Criteria: Engagement Metric Tracking for Lead Segmentation

Given a lead has interacted with marketing materials, when engagement metrics are recorded, then the system should display a summary of interaction data including email opens, clicks, website visits, and social media interactions.

Real-time Data Update for Accurate Segmentation

Given that a lead's engagement data is updated, when new interaction metrics are recorded, then the lead's segmentation category should be updated in real-time based on predefined criteria.

Report Generation for Engagement Analysis

Given the collected engagement metrics, when a report is generated, then the report should include visual representations of key engagement data and trends over a specified period.

Integration with CRM for Lead Management

Given the engagement metrics have been captured, when the lead is accessed in the CRM, then the engagement data should be visible within the lead profile for sales representatives.

User Notification for Significant Engagement Changes

Given a lead's engagement metrics have drastically changed, when the threshold for significant engagement is crossed, then the system should notify the sales representative via email or dashboard alert.

User-friendly Interface for Engagement Insights

Given the tracking of engagement metrics is implemented, when a user accesses the Lead Segmentation Insights dashboard, then the engagement metrics should be presented in a clear and understandable format for quick analysis.

Behavioral Pattern Analysis

User Story: As a sales representative, I want to analyze behavioral patterns of my leads so that I can better understand their interests and improve my engagement tactics accordingly.
Description: This requirement involves developing algorithms that analyze past behaviors of leads to predict future actions. By using machine learning techniques, SalesMap AI can identify trends and patterns in how leads interact with marketing content and sales communications. This analysis enables sales teams to anticipate lead needs and preferences, making it easier to personalize approaches and improve overall sales strategies, thereby increasing conversion likelihood.
Acceptance Criteria: Lead Analysis for Engagement Prediction

Given a dataset of past lead behaviors, when the behavioral pattern analysis algorithm processes this data, then it should accurately categorize leads into at least three distinct segments based on their predicted future actions with an accuracy of 85% or higher.

Real-time Behavioral Insights Integration

Given that leads are interacting with marketing content, when the system captures these interactions, then the lead scoring and segmentation should update in real-time, reflecting any changes within 5 seconds of the interaction.

Automated Reporting on Behavioral Trends

Given that the behavioral analysis is completed, when a sales team accesses the reporting dashboard, then it should display comprehensive insights on lead engagement trends, including visual graphs and statistical summaries, within 10 seconds after the analysis is run.

User Notification for High-Value Leads

Given that the lead scoring algorithm has assigned a high score to a lead, when the analysis indicates a likely conversion, then the system should send an automated notification to the sales team within 2 minutes of the high score being assigned.

Testing the Algorithm's Predictive Accuracy

Given a predefined test set of lead behaviors, when the behavioral pattern analysis algorithm is executed, then it should demonstrate a predictive accuracy of at least 90% when compared to actual lead behavior outcomes over the following month.

User Feedback on Segmentation Effectiveness

Given that leads have been segmented based on behavior, when the sales team conducts follow-up communications, then a minimum of 70% of the team should report improved engagement rates compared to previous campaigns based on non-segmented strategies in a post-campaign survey.

Demographic Data Integration

User Story: As a marketer, I want to integrate demographic data into the lead profiles so that I can create customized marketing strategies for different segments of my audience.
Description: This requirement covers the integration of comprehensive demographic data from various sources into the SalesMap AI platform. By enriching lead profiles with key demographic information such as age, location, and job title, the platform can enable more nuanced lead segmentation. This integration helps sales teams to create targeted messages that resonate with specific audience segments, ultimately enhancing the effectiveness of outreach efforts and increasing conversion opportunities.
Acceptance Criteria: Lead profile enrichment with demographic data during the data import process.

Given a CSV file containing demographic data, when the file is uploaded to SalesMap AI, then all valid demographic data fields should populate the respective lead profiles without errors.

Sales teams accessing enriched lead profiles for targeted outreach.

Given a sales team member accessing the lead management dashboard, when they view a lead profile enriched with demographic data, then they must see the fields for age, location, and job title populated accurately according to the imported data.

Generating reports based on demographic segments after data integration.

Given that demographic data has been successfully integrated, when the sales manager generates a report on lead segmentation, then the report should correctly categorize leads based on the new demographic fields and reflect those categories in the results.

Real-time updates to lead profiles upon demographic data integration.

Given that a lead's demographic information is updated through an external integration, when the integration syncs the new data, then the updated demographic information should be reflected in the lead profile within 10 minutes.

Displaying demographic trends in the user dashboard.

Given that demographic data has been integrated, when the user views the insights dashboard, then they should see visual representations of demographic trends, such as age distribution and geographic location of leads, updated in real-time.

User authentication for accessing demographic data.

Given a user trying to access lead profiles with demographic data, when the user is not authenticated, then they should receive an error message indicating they need to log in to access this information.

Validation of data accuracy after demographic data integration.

Given that demographic data has been imported, when a QA process is conducted, then at least 95% of the demographic fields must match the source data for accuracy validation.

Personalized Follow-Up Recommendations

User Story: As a sales agent, I want to receive personalized follow-up recommendations so that I can effectively reconnect with my leads and improve my closing rates.
Description: This requirement entails delivering actionable follow-up recommendations based on segmentation insights and the data collected from leads. The system should analyze all available lead information and suggest optimal follow-up actions or content for each segment, ensuring that sales teams can effectively engage with leads in a personalized manner. This capability not only streamlines the follow-up process but also increases the probability of converting leads into customers.
Acceptance Criteria: Sales team utilizes the Lead Segmentation Insights feature to receive personalized follow-up recommendations after analyzing a new set of leads.

Given that the leads have been segmented based on engagement metrics, when the sales team accesses the recommendations, then they should see a list of optimal follow-up actions corresponding to each segment.

A sales representative reviews follow-up recommendations for a segment with high engagement scores and applies the suggested actions during their outreach.

Given that the sales representative has access to the follow-up recommendations, when they implement the suggested actions, then they should report an increase in engagement rates with the leads from that segment.

The system aggregates data from previously implemented follow-up strategies to refine future recommendations based on their success rates.

Given that follow-up actions have been tracked for effectiveness, when new leads are segmented, then the system should provide recommendations based on the highest success rates from past follow-ups.

Users want to access follow-up recommendations via the SalesMap AI dashboard to enhance personalized communications with leads.

Given that users are logged into the SalesMap AI dashboard, when they navigate to the Lead Segmentation Insights section, then they should see the personalized follow-up recommendations displayed clearly.

The sales team conducts a training session to understand how to utilize follow-up recommendations effectively for different lead segments.

Given that a training session is conducted on the use of follow-up recommendations, when feedback is collected, then at least 80% of participants should indicate confidence in implementing the recommendations in their outreach.

A sales manager evaluates the impact of personalized follow-up recommendations on lead conversion rates over a specified period.

Given that follow-up recommendations have been utilized for a specific time frame, when the sales manager reviews lead conversion metrics, then they should observe a measurable increase in the conversion rate compared to the previous period without personalized follow-ups.

The system experiences an error when generating personalized follow-up recommendations based on lead segmentation data.

Given that a user requests personalized follow-up recommendations, when an error occurs, then the system should display an appropriate error message and log the incident for further investigation.

Automated Reporting Dashboard

User Story: As a sales director, I want an automated reporting dashboard to visualize lead segmentation data so that I can quickly assess our performance and make informed strategic decisions.
Description: This requirement specifies the creation of a real-time reporting dashboard that visualizes key metrics related to lead segmentation and engagement. The dashboard should include interactive features, allowing users to drill down into specific segments and view performance analytics. By providing an insightful overview of segmentation effectiveness, the dashboard empowers sales teams with data-driven insights to refine their strategies and improve overall sales performance.
Acceptance Criteria: Displaying Real-Time Metrics on the Dashboard

Given that a user accesses the Automated Reporting Dashboard, when the dashboard is loaded, then all key metrics related to lead segmentation and engagement should be displayed in real-time without any significant delay (less than 2 seconds).

Interactivity of the Dashboard

Given that a user is viewing the dashboard, when the user clicks on a specific segment, then the dashboard should allow the user to drill down to see detailed performance analytics for that segment, including at least 5 key performance indicators (KPIs).

Data Accuracy and Consistency

Given that the Automated Reporting Dashboard is receiving data from the CRM system, when a user requests to view the latest metrics, then all displayed metrics must reflect the most recent data within a 5-minute timeframe and match the source data from the CRM.

User-Friendly Interface for Non-Technical Users

Given that a non-technical sales team member is utilizing the dashboard, when they navigate through the interactive features, then all user interface elements should be intuitive, with clear labels and tooltips that provide guidance on use.

Customization of Report Views

Given that a user wants to customize their view on the dashboard, when they select different filters (such as date range, lead source, etc.), then the dashboard should update to show relevant metrics based on the selected filters without requiring a page reload.

Exporting Dashboard Insights

Given that a user is reviewing the dashboard data, when they select the export option, then the dashboard should provide the ability to download the displayed metrics in at least two formats (CSV and PDF) within 2 minutes.

Mobile Responsiveness of the Dashboard

Given that a user accesses the dashboard on a mobile device, when the dashboard is loaded, then it should be fully responsive, displaying all metrics and features clearly without functionality loss across different screen sizes.

Automated Engagement Reminders

Automated Engagement Reminders prompt users to reach out to leads based on predefined engagement thresholds or inactivity periods. By ensuring that no potential conversion opportunity slips through the cracks, sales professionals can maintain consistent communication with prospects, ultimately increasing the chances of closure.

Requirements

Threshold Settings for Engagement

User Story: As a sales representative, I want to set specific engagement thresholds for my leads so that I can actively manage follow-ups and ensure no prospects are forgotten during the sales cycle.
Description: This requirement enables users to set predefined engagement thresholds, such as a specific number of days of inactivity or a certain number of interactions before a reminder is triggered. By allowing users to customize these thresholds, the feature can better align with their sales strategies and prospecting activities. This functionality will contribute to more effective engagement practices, ensuring leads are followed up in a timely manner and not left unattended, therefore increasing conversion rates and overall sales effectiveness.
Acceptance Criteria: User sets a custom inactivity threshold for engagement reminders.

Given the user is on the Threshold Settings page, when they input a number of days for inactivity, then the system must save the threshold and reflect the new setting in the user's dashboard.

User configures a specific number of interactions required before triggering a reminder.

Given the user has navigated to the Threshold Settings, when they update the interaction count field and save the changes, then the system should confirm the update and allow reminders based on the adjusted interaction count.

User receives a reminder after a lead has been inactive for the set threshold period.

Given a lead has not interacted for the predefined threshold period, when the threshold timer elapses, then the user should receive an automated engagement reminder notification via email and in-app notification.

User can view and adjust previously set engagement thresholds.

Given the user is on the Threshold Settings page, when they select a previously set threshold, then they should be able to edit and save changes to the threshold without any errors.

User attempts to set a negative value for the threshold.

Given the user is setting the inactivity threshold, when they input a negative number and attempt to save, then the system should display an error message indicating the input is invalid.

User triggers a reminder based on multiple thresholds set for different leads.

Given the user has multiple leads with differing inactivity and interaction thresholds, when the thresholds are met for at least one lead, then the user should receive reminders appropriately for all relevant leads.

User deactivates all engagement reminders for selected leads.

Given the user is on the engagement reminders section, when they select leads and toggle reminders to 'off', then the system must reflect these changes and stop sending reminders for the selected leads immediately.

Multi-Channel Reminder Options

User Story: As a sales agent, I want to receive engagement reminders through my preferred communication channel, so that I can quickly act on leads and maintain effective communication based on my workflow.
Description: This requirement involves implementing multiple channels for reminders, including email, SMS, and in-app notifications. By offering various channels, users can receive engagement alerts in their preferred way, enhancing their ability to respond to reminders quickly. This flexibility in communication channels can lead to better engagement with leads, as users will have options that suit their working style, ultimately promoting more effective follow-ups and increased closing rates.
Acceptance Criteria: Automated Multi-Channel Reminders for New Leads

Given a new lead is added to the system, When the engagement threshold is reached, Then a reminder should be triggered and sent through the selected channel (email, SMS, or in-app notification).

User Preference Setup for Reminder Channels

Given the user is in the settings menu, When they select their preferred channels for receiving reminders, Then the system should save these preferences and apply them to future reminders.

Testing the Delivery of Reminders across Different Channels

Given a lead meets the defined inactivity period, When the reminder is sent through all selected channels, Then each channel should successfully deliver the reminder and confirm receipt within a specified time frame.

User Interface for Viewing Scheduled Reminders

Given the user accesses the reminders dashboard, When they view their scheduled reminders, Then they should see a comprehensive list showing the channel, content, and timing of each reminder.

Escalation Protocol for Unanswered Reminders

Given a reminder has been sent but no action is taken by the user, When the defined escalation period is reached, Then an additional follow-up reminder should be triggered to ensure continuous engagement.

Analytics on Reminder Effectiveness

Given the engagement reminders have been sent, When the analytics dashboard is accessed, Then it should display metrics on open rates, response rates, and lead conversion rates linked to the reminders sent through different channels.

Notification of Reminder Configuration Changes

Given a user changes settings related to reminder delivery channels, When the configuration is successfully updated, Then the user should receive a confirmation notification confirming the change.

Reminder Analytics Dashboard

User Story: As a sales manager, I want to analyze the effectiveness of engagement reminders on closing rates so that I can adjust my team's follow-up strategies and optimize lead management efforts.
Description: This requirement includes the development of an analytics dashboard that provides insights into the effectiveness of engagement reminders. Users will be able to track metrics such as response rates to reminders, time taken to follow up after reminders, and conversion success rates linked to these follow-ups. By analyzing this data, sales teams can refine their engagement strategies, identify areas of improvement, and further enhance their follow-up processes to boost overall sales performance.
Acceptance Criteria: User accesses the Reminder Analytics Dashboard to review the effectiveness of engagement reminders for the past month.

Given the user is logged into SalesMap AI, when they navigate to the Reminder Analytics Dashboard, then they should see a summary of engagement reminders, including metrics such as total reminders sent, response rates, and follow-up times.

Sales team reviews response rates to engagement reminders over a defined time period.

Given a sales team selects a specific time frame on the dashboard, when they apply the date filters, then the dashboard should update to display average response rates for the selected period and compare them with historical averages.

User analyzes conversion rates associated with follow-ups after receiving reminders.

Given the user is viewing the Reminder Analytics Dashboard, when they select specific reminders, then the dashboard should show conversion rates linked to those reminders, allowing the user to see the correlation between reminders and successful sales closure.

User identifies trends in follow-up actions based on reminder data over different periods.

Given the user has filtered the dashboard for comparative analysis, when they review the analytics, then they should be able to see trends in time taken to follow up and the resulting conversion rates, displayed in graphical format.

Sales manager exports analytics data for external reporting.

Given the user is on the Reminder Analytics Dashboard, when they choose to export the data, then the system should provide a downloadable report in a standard format (CSV or Excel) containing the key metrics displayed on the dashboard.

User customizes the metrics displayed on the Reminder Analytics Dashboard based on their preferences.

Given the user accesses the dashboard settings, when they select their preferred metrics and save, then the dashboard should reflect these customizations upon refresh or revisit, ensuring a personalized user experience.

User receives timely notifications for engagement reminders that require immediate follow-up.

Given that reminders have been set and engagement thresholds are crossed, when these reminders trigger alerts, then the user should receive real-time notifications through the application interface indicating which leads need immediate follow-up.

Integration with Existing CRM

User Story: As a user, I want Automated Engagement Reminders to integrate with my CRM so that I can manage all my lead interactions in one place and automate follow-up tasks based on accurate lead data.
Description: This requirement ensures that the Automated Engagement Reminders feature integrates seamlessly with existing CRM systems used by sales professionals. This integration will allow for automatic tracking of lead activities, ensuring that reminders are based on real-time data. Moreover, it will help in managing follow-up activities directly through the CRM interface, thus streamlining workflows and reducing the need for manual data input, ultimately saving time and enhancing the efficiency of the sales process.
Acceptance Criteria: Integration of Automated Engagement Reminders with Salesforce CRM for tracking lead activities.

Given a user with access to Salesforce CRM, When the user configures the Automated Engagement Reminders, Then the system should automatically track lead activities and trigger reminders based on user-defined engagement thresholds.

Automated Engagement Reminders reflect accurate and real-time data from the CRM system.

Given the integration is set up, When a lead activity is recorded in the CRM, Then the Automated Engagement Reminder should update within a minute to reflect this activity accurately.

Users receive alerts for leads that have not been contacted within a specified timeframe.

Given the engagement thresholds are set to 7 days of inactivity, When a lead has not been contacted for more than 7 days, Then the system should send an automated alert to the user to follow up with that lead.

Seamless management of follow-up activities directly through the CRM interface.

Given that the user wants to manage follow-up tasks, When the user views a lead’s profile in the CRM, Then they should see a list of upcoming follow-up reminders generated by the Automated Engagement Reminders feature.

Automated Engagement Reminders allow users to customize engagement thresholds for different lead segments.

Given a user wants to set different engagement thresholds for various lead types, When the user selects a lead segment and configures thresholds, Then the system should save these customized settings and apply them accordingly.

Users can disable or modify engagement reminders for individual leads.

Given the user decides a lead no longer requires reminders, When the user disables the engagement reminder for that lead, Then the system should stop all future reminders for that specific lead.

System maintains historical data on all engagement reminders sent.

Given that engagement reminders are generated, When the user requests a report on past communications, Then the system should provide a complete history of all reminders sent along with the corresponding lead responses.

Customizable Reminder Templates

User Story: As a sales representative, I want to customize my reminder messages so that I can engage my leads in a more personal and effective way, improving my chances of closing deals.
Description: This requirement provides users with the ability to create and customize templates for engagement reminders. Users can personalize messages to reflect their tone, style, or to match specific marketing campaigns, making follow-ups feel more personal and strategic. Personalized communication is crucial for building relationships with leads, which can lead to increased engagement and higher chances of closing deals. This feature will help sales professionals create relevant and appealing interactions with their prospects.
Acceptance Criteria: User creates a new engagement reminder template for a marketing campaign.

Given the user accesses the reminder templates section, when they select 'Create Template', then they should be able to customize the message, select the tone, and save the template for future use.

User edits an existing reminder template to update messaging for a new campaign.

Given the user opens an existing reminder template, when they modify the content and save the changes, then the updated template should reflect the new message without affecting other templates.

User sends out a reminder using a customized template to a specific lead.

Given the user selects a lead to engage and chooses a customized reminder template, when they send the message, then the lead should receive the personalized reminder and it should be logged in the CRM.

User previews a reminder template before sending it to the lead.

Given the user selects a reminder template, when they click on 'Preview', then the system should display the template in the expected format allowing the user to confirm or modify it before sending.

User deletes a reminder template they no longer need.

Given the user views the list of reminder templates, when they select a template and click 'Delete', then the template should be permanently removed from the list without affecting other templates.

User duplicates an existing reminder template for a similar lead engagement strategy.

Given the user selects an existing reminder template, when they click on 'Duplicate', then the system should create an identical template that the user can further customize without altering the original.

Behavioral Trend Analysis

Behavioral Trend Analysis analyzes historical data to identify patterns and trends in lead engagement over time. This feature enables sales teams to anticipate future behaviors and adapt their strategies accordingly. Users benefit from a proactive approach, allowing them to stay ahead of lead needs and preferences, thereby maximizing conversion potential.

Requirements

Data Pattern Recognition

User Story: As a sales representative, I want to analyze past lead engagement trends so that I can tailor my approach and anticipate future lead needs more effectively.
Description: The Data Pattern Recognition requirement involves developing algorithms to analyze historical lead engagement data, identifying key behavioral patterns and trends. This capability is crucial for SalesMap AI, as it will enable sales teams to understand how leads interact with their outreach efforts over time. By leveraging advanced analytics, the functionality will provide actionable insights into lead preferences, peak engagement times, and content effectiveness. The successful implementation of this feature will not only support proactive sales strategies but will also facilitate improved targeting and messaging for leads, ultimately enhancing conversion rates and driving sales growth across users' teams.
Acceptance Criteria: User analyzes lead engagement data to identify patterns and trends over a three-month period.

Given the historical lead engagement data, when the user initiates the Behavioral Trend Analysis report, then the system should display a comprehensive overview of identified engagement trends, with visual graphs indicating peak engagement times and types of content that generated the most interactions.

Sales team receives insights from the algorithm regarding the effectiveness of their outreach strategies.

Given completed analysis of engagement data, when the user accesses the dashboard insights, then the system should present actionable insights about the most effective outreach strategies with clear suggestions for enhancements based on identified user behaviors.

User wants to validate the accuracy of the predictive behavior model generated from the historical data.

Given various lead engagement scenarios, when the user runs a comparison between predicted behaviors and actual outcomes, then the system should demonstrate an accuracy rate of at least 85% in predictive analysis accuracy.

Sales team utilizes the identified behavioral patterns to develop a targeted outreach campaign.

Given identified behavioral trends, when the sales team creates a campaign based on these insights, then the system should allow for campaign customization that aligns with the determined peak engagement times and content preferences, and should ensure the campaign is launched successfully.

User evaluates the effectiveness of different content types over a set period.

Given a variety of content types used in outreach efforts, when the user analyzes engagement statistics, then the system should provide a detailed report comparing the engagement metrics of each content type over the specified time period, highlighting the most and least effective content.

Sales manager monitors team performance using insights from data pattern recognition.

Given user performance data correlated with lead engagement insights, when the sales manager accesses the team performance dashboard, then the system should showcase individual and team metrics linked to the historical analysis, including conversion rates and outreach effectiveness, in real-time.

User wants to export the analyzed behavior trends and insights for external reporting.

Given the completed Behavioral Trend Analysis, when the user selects the export option, then the system should allow the user to download the report in multiple formats (PDF, CSV, Excel) with all relevant visuals and data included.

Predictive Engagement Forecasting

User Story: As a sales manager, I want to forecast future engagement of our leads so that I can allocate resources more effectively and improve our conversion strategies.
Description: The Predictive Engagement Forecasting requirement encompasses the development of tools that utilize machine learning models to predict future lead behaviors based on historical data. This feature aims to enhance the SalesMap AI platform by allowing users to foresee engagement patterns and prepare their sales strategies accordingly. By accurately forecasting potential lead interactions, sales professionals can optimize their outreach efforts, prioritize follow-ups, and create timely content that resonates with leads. This implementation is expected to significantly increase the likelihood of conversions and improve overall sales efficiency.
Acceptance Criteria: User can access Predictive Engagement Forecasting tools from the SalesMap AI dashboard to view insights related to lead engagement patterns.

Given the user is logged into SalesMap AI, when they navigate to the dashboard and select the Predictive Engagement Forecasting tool, then the tool should display relevant metrics for lead engagement based on historical data.

Sales teams receive accurate predictions for lead engagement based on historical interaction data to tailor their outreach strategies.

Given that the Predictive Engagement Forecasting tool has been implemented, when a user inputs historical lead data, then the system should generate predictions for lead behaviors with at least 85% accuracy based on previous engagement data.

Users can set customizable notifications to alert them of significant changes in lead engagement patterns predicted by the tool.

Given a user has access to the Predictive Engagement Forecasting feature, when they customize notification settings for lead activity changes, then the system should send timely alerts via email or dashboard notification based on the defined parameters.

Sales professionals are able to prioritize follow-ups based on the predicted engagement levels of leads presented in the tool.

Given the predictive engagement data displayed, when a user reviews the list of leads, then they should be able to categorize leads into 'high priority', 'medium priority', and 'low priority' for follow-up purposes with at least 90% confidence in the system's suggestions.

Users can generate reports that summarize predicted lead behaviors and engagement trends over specific periods.

Given the user is utilizing the Predictive Engagement Forecasting feature, when they generate a report for a selected time frame, then the system should output a comprehensive report featuring engagement forecasts and trend graphs that can be exported to CSV or PDF format.

The Predictive Engagement Forecasting feature seamlessly integrates with existing CRM systems to enhance data accuracy.

Given the user has linked their CRM with SalesMap AI, when they input or sync historical lead data, then the forecasting tool should accurately reflect the CRM’s data without discrepancies and update predictions accordingly.

Sales teams can easily interpret the graphical representations of predicted engagement patterns to make informed decisions.

Given the forecasting tool displays graphical metrics, when a user views the graphical representation for lead engagement, then they should be able to interpret the data and trends without ambiguity, ensuring at least a 90% user satisfaction rate regarding data clarity.

Customizable Reporting Dashboards

User Story: As a sales analyst, I want to customize my reporting dashboard to display relevant engagement metrics, so that I can quickly gather insights and adjust our sales strategies accordingly.
Description: The Customizable Reporting Dashboards requirement is focused on enabling users to create personalized dashboards that visualize trends in lead engagement data. This feature will allow users to select metrics, filter data, and visualize insights in formats that best suit their strategic needs. The importance of this requirement lies in its ability to empower users with real-time data insights tailored to their unique sales contexts. This flexibility will enhance the user experience by providing clarity and focus, allowing sales teams to make informed decisions based on visual representations of critical data.
Acceptance Criteria: User creates a dashboard with multiple widgets displaying different metrics related to lead engagement.

Given the user is on the dashboard customization page, when they select at least three different metrics and click 'Save,' then the dashboard should display these metrics in widget format and retain this configuration during subsequent logins.

User filters data on the customizable dashboard according to specific lead attributes.

Given the user is on their dashboard, when they apply a filter to view leads from a specific region and select 'Apply,' then the displayed metrics should only reflect leads from that region without any errors or incorrect data.

User changes the visualization type of a specific metric on the dashboard.

Given the user is viewing their dashboard, when they select a metric and choose a different visualization type (e.g., from bar chart to line graph), then the metric should update immediately to reflect the new visualization appropriately.

User shares the customized dashboard with team members.

Given the user has customized their dashboard, when they click 'Share' and select team members, then those team members should receive a notification with access to the dashboard and be able to view the same metrics in their own accounts.

User deletes a widget from their customized dashboard.

Given the user is viewing their customized dashboard, when they click the delete icon on a specific widget and confirm the action, then that widget should be removed from the dashboard, and the layout should adjust accordingly.

User accesses their customized dashboard across different devices (desktop/mobile).

Given the user saves their customized dashboard on a desktop, when they log in from a mobile device, then the dashboard should display the same configuration and metrics without discrepancies.

User exports the dashboard data for reporting purposes.

Given the user is on their customizable dashboard, when they select 'Export' and choose a file format (CSV, PDF), then the dashboard data should be correctly exported in the selected format and without errors.

Automated Engagement Alerts

User Story: As a sales representative, I want to receive alerts when my leads show significant changes in their engagement levels so that I can adjust my follow-up strategy without delay.
Description: The Automated Engagement Alerts requirement entails setting up a system that notifies users of key changes in lead engagement behaviors. This feature will monitor engagement patterns in real time and trigger alerts based on predefined criteria such as sudden drops in engagement or unusual spikes in activity. The integration of this feature within SalesMap AI serves the critical function of keeping sales teams informed and responsive, enabling them to act promptly when lead behaviors deviate from expected norms. This proactive approach minimizes the risk of missed opportunities and fosters timely engagement with leads.
Acceptance Criteria: Notification of Sudden Engagement Drops

Given that a lead's average engagement score is monitored, when the engagement drops below a predefined threshold (e.g., 30% less than the average), then an alert should be triggered and sent to the assigned salesperson immediately.

Alert for Unusual Activity Spikes

Given that lead engagement patterns are analyzed, when a lead's engagement score spikes by more than 50% compared to the previous day, then an alert should be generated and communicated to the sales team via the dashboard.

Daily Summary of Engagement Alerts

Given that engagement alerts are generated, when the sales team logs into SalesMap AI each morning, then they should receive a daily summary report of all alerts triggered in the past 24 hours.

Customization of Alert Criteria

Given that users can set engagement criteria, when the user defines new thresholds for engagement alerts, then the system should allow saving and using these custom thresholds effectively across all leads.

Response Time for Alerts

Given that an engagement alert is triggered, when the alert is received by the salesperson, then it should be acknowledged or acted upon within a maximum of 30 minutes to ensure timely engagement.

Integration with CRM Systems

Given that SalesMap AI integrates with existing CRM platforms, when an alert is triggered for a lead, then the corresponding lead information should automatically update in the CRM to reflect the current engagement status.

Feedback Loop for Alerts Effectiveness

Given that engagement alerts have been issued, when the sales team takes actions based on these alerts, then a feedback mechanism should be implemented to analyze the effectiveness of those actions on lead conversion rates.

Integration with External Platforms

User Story: As a product manager, I want SalesMap AI to integrate with our existing CRM system so that our sales data remains consistent and organized across platforms, improving team collaboration.
Description: The Integration with External Platforms requirement involves connecting SalesMap AI with popular CRM and marketing automation tools to ensure seamless data flow and interoperability. This capability will enhance the platform's functionality by allowing users to synchronize lead engagement data across systems, ensuring that sales teams have a unified view of their prospects. The integration is essential for streamlining workflows and removing silos between various tools users leverage. With this integration, users will benefit from enhanced lead tracking, improved communication between sales and marketing teams, and more comprehensive insights into lead engagement.
Acceptance Criteria: User initiates an integration process to connect SalesMap AI with their existing CRM system.

Given the user has access to the integration settings, when they select their CRM from the list and provide the necessary API credentials, then the integration should successfully establish a connection without errors.

Sales and marketing teams track lead engagement data through the integrated SalesMap AI and CRM systems.

Given that the integration is active, when a lead engages with the marketing email tracked in the CRM, then the engagement data should automatically sync to SalesMap AI within 5 minutes.

Users set up automated lead tracking to monitor prospects across connected platforms using SalesMap AI.

Given the user configures lead tracking preferences, when leads interact with any integrated platform, then the corresponding engagement events should be logged and visible in the SalesMap AI dashboard.

SalesMap AI generates reports based on synchronized lead engagement data from external platforms.

Given that the integration is functioning, when the user requests a report on lead engagement from the dashboard, then the report should include accurate analytics sourced from both SalesMap AI and the connected CRM.

Users receive notifications of significant lead engagements captured from their integrated CRM system.

Given that a lead exhibits significant engagement activity, when this occurs, then the system should send a real-time notification to the user’s device or email alerting them of the activity.

The integration feature allows users to manage and disconnect external platform connections as needed.

Given that a user is in the integration settings, when they select an active connection and choose to disconnect it, then the system should remove the connection and ensure no data discrepancies occur.

Optimal Timing Insights

Optimal Timing Insights analyzes historical campaign data to determine the best times to launch each campaign. By leveraging this feature, users can ensure their marketing efforts align with peak audience engagement periods, significantly boosting reach and interaction rates.

Requirements

Campaign Performance Analytics

User Story: As a marketing manager, I want to analyze the performance of past campaigns so that I can improve future initiatives and allocate resources more effectively.
Description: The Campaign Performance Analytics requirement involves developing robust analytics capabilities that provide users with detailed insights into past campaign performances. This includes metrics such as open rates, click-through rates, engagement levels, conversion rates, and ROI data. The aim is to empower users to understand what strategies worked best, allowing for data-driven decision-making for future campaigns. Integrating this feature seamlessly with the existing dashboard will enable users to quickly assess performance without needing to switch platforms, ultimately enhancing their campaign strategies and effectiveness.
Acceptance Criteria: User views the Campaign Performance Analytics dashboard to evaluate the success of previous marketing campaigns.

Given a user is logged in to the SalesMap AI platform, when they navigate to the Campaign Performance Analytics section, Then they should see a clear summary of metrics for each campaign, including open rates, click-through rates, engagement levels, conversion rates, and ROI.

The user wants to compare the performance of multiple campaigns over a selected date range.

Given a user selects a date range for analysis, when they apply the filters to view campaign performance, Then the dashboard should accurately reflect the selected metrics for only the campaigns that fall within the specified date range.

A user needs to export campaign performance data for further analysis or reporting.

Given a user has accessed the campaign performance analytics, when they choose to export the data, Then the system should provide a downloadable file in CSV format containing all relevant campaign performance metrics.

The user analyzes the data to understand which campaigns yielded the highest ROI.

Given a user is viewing the campaign performance metrics, when they sort the campaigns by ROI, Then the campaigns should rearrange in descending order, clearly highlighting the highest ROI campaigns.

A marketing manager reviews the performance of past campaigns to inform future strategy decisions.

Given a marketing manager accesses the Campaign Performance Analytics, when they view the performance trends over time, Then they should receive visual representations (e.g., graphs or charts) that show performance changes and trends for each metric over the selected period.

The user needs to check the engagement levels of a specific campaign to assess its effectiveness.

Given a user selects a specific campaign from the list, when they view the detailed analytics, Then all relevant metrics, specifically engagement levels, should be presented in a clear and understandable format, with contextual explanations where necessary.

A user needs a quick overview of all campaign performances without switching platforms.

Given the user is on the Main Dashboard, when they request to view campaign performance insights, Then the analytics should be displayed seamlessly within the dashboard screen without redirects or loading delays.

Predictive Engagement Timing

User Story: As a sales executive, I want to know the best times to reach out to my leads so that I can maximize engagement and conversion rates.
Description: The Predictive Engagement Timing feature will leverage AI to analyze user behavior and historical engagement data to predict optimal engagement times for target audiences. By calculating when users are most likely to engage with campaigns, this feature enhances the chances of campaign success by ensuring that communication aligns with users' availability. This is a vital enhancement that supports the Optimal Timing Insights feature, giving users actionable recommendations to optimize their outreach timing, and ultimately increasing their campaigns' effectiveness.
Acceptance Criteria: User identifies target audience segments for a campaign and utilizes Predictive Engagement Timing to receive insights on optimal engagement times for each segment.

Given a user has defined target audience segments, when they access the Predictive Engagement Timing feature, then they should see a list of optimal engagement times for each segment based on historical engagement data.

A user sets up a new campaign in the platform and requests optimal timing insights during the campaign setup process.

Given a user is in the campaign setup process, when they request optimal timing insights, then the system should generate and display predicted optimal engagement times based on previous campaign performance and user behavior.

A user analyzes the effectiveness of past campaigns by comparing engagement rates before and after implementing recommendations from Predictive Engagement Timing.

Given a user has implemented recommendations from the Predictive Engagement Timing feature, when they review engagement rates of the campaign, then they should be able to see an increase in engagement rates as compared to previous campaigns without these recommendations.

Users receive real-time notifications about optimal engagement windows for upcoming campaigns based on Predictive Engagement Timing analysis.

Given a user has scheduled a campaign, when the optimal engagement window approaches, then the user should receive a notification recommending action based on the predicted timing data.

The system integrates historical user behavior data to refine future predictions for optimal engagement timing.

Given the system has historical data on user behavior, when a user accesses the Predictive Engagement Timing feature, then the predictions for optimal engagement times should be based on the latest available historical data for improved accuracy.

Integration with Third-party Marketing Tools

User Story: As a marketing coordinator, I want SalesMap AI to integrate with my existing marketing tools so that I can streamline my process and avoid duplicating efforts.
Description: The Integration with Third-party Marketing Tools requirement focuses on enabling seamless integration of SalesMap AI with popular marketing platforms such as Mailchimp, HubSpot, and ActiveCampaign. This functionality will allow users to import and export campaign data, synchronize contacts, and analyze shared metrics from both platforms. Such integration will minimize manual data entry and ensure consistent data across tools, thereby enhancing operational efficiency and providing a more cohesive marketing strategy for users.
Acceptance Criteria: Campaign Data Import from Mailchimp

Given that a user has connected their Mailchimp account to SalesMap AI, when they choose to import campaign data, then all relevant campaign metrics should be successfully populated in the SalesMap AI dashboard without errors.

Contact Synchronization with HubSpot

Given that a user has integrated their HubSpot account, when they initiate contact synchronization, then all HubSpot contacts should be accurately reflected in SalesMap AI with matching attributes and updated information.

Data Export to ActiveCampaign

Given that a user has created a campaign in SalesMap AI, when they select the option to export the campaign to ActiveCampaign, then the campaign should be transferred successfully with all associated settings and recipient lists intact.

Real-time Analytics from Integrated Tools

Given that the user has linked their marketing tools, when they access the analytics dashboard, then the metrics from both SalesMap AI and the integrated tools should be displayed together in real-time, reflecting the most recent updates.

Error Handling for Integration Failures

Given that a user attempts to connect a third-party tool but the connection fails, when they review the error logs, then the logs should provide a clear and specific description of the issue to enable resolution.

User Permissions on Integrated Tools

Given that a user is managing multiple accounts, when they set permissions for accessing integrated third-party tools, then only authorized personnel should be able to view or modify data related to those tools in SalesMap AI.

Notifications for Integration Updates

Given that there is a change in the status of an integrated tool, when the user logs into SalesMap AI, then they should receive a notification alerting them about the changes and its impact on their campaigns.

User-friendly Dashboard Enhancements

User Story: As a business owner, I want my dashboard to be intuitive and customizable so that I can monitor performance metrics that matter most to my business.
Description: The User-friendly Dashboard Enhancements requirement aims to further refine the current dashboard layout and functionality, ensuring that users can quickly access key metrics and insights. This includes improving visualizations through graphs and charts and enabling customizable views that cater to individual user preferences. Enhanced usability will facilitate quicker decision-making and a more personalized user experience, allowing users to track their campaign effectiveness at a glance and take immediate action where necessary.
Acceptance Criteria: User accesses the dashboard to track campaign performance after implementing Optimal Timing Insights enhancements.

Given the user is logged into the SalesMap AI platform, when they navigate to the dashboard, then they should see visualizations of key metrics (e.g., engagement rates, lead conversions) presented in graphs and charts that can be customized by the user.

User personalizes their dashboard view to prioritize specific campaign metrics based on their preferences.

Given the user accesses the dashboard settings, when they select their preferred metrics and save the settings, then the dashboard should update to reflect their choices and maintain the customized view across future sessions.

User analyzes historical campaign performance data to determine which campaigns to run during optimal engagement periods.

Given the user interacts with the Optimal Timing Insights feature, when they view the suggested campaign launch times, then the insights should reflect data-driven recommendations based on previous campaign performance metrics.

User evaluates the effectiveness of a specific campaign by reviewing the updated dashboard metrics.

Given the user selects a particular campaign on the dashboard, when they view the associated metrics, then the user should see a clear summary of performance indicators such as reach, engagement, and return on investment (ROI) displayed in easily interpretable formats.

User completes a campaign adjustment based on insights from the dashboard.

Given the user identifies underperformance in a campaign from the dashboard metrics, when they implement adjustments (e.g., changing the campaign timing or audience targeting), then the dashboard should allow them to save and execute the changes seamlessly without navigation issues.

User participates in a training session for the new dashboard features to understand how to leverage them effectively.

Given the user attends a training tutorial or session, when they complete the training, then they should demonstrate the ability to navigate the dashboard, customize their view, and utilize the insights to enhance their campaign strategy as outlined in the training materials.

Automated Performance Reports

User Story: As a project manager, I want to receive automated reports on campaign performance so that I can quickly review and act upon insights without manual effort.
Description: The Automated Performance Reports feature will enable users to set up automated reporting that delivers campaign performance reports directly to their inboxes at specified intervals (daily, weekly, monthly). These reports will include comprehensive performance metrics, trends, and actionable insights based on historical data. This capability will save users time in manual reporting, enabling them to focus on strategy rather than data gathering, and ensure they are consistently informed about their campaign effectiveness.
Acceptance Criteria: User schedules a weekly automated performance report for the last month's campaign activity on a Monday morning.

Given the user has logged into SalesMap AI, when they set the report frequency to weekly for campaign performance, then an email should be sent every Monday morning containing the performance metrics for the last month.

User selects to receive daily performance reports for ongoing campaigns every evening at 6 PM.

Given the user has set their preferences to receive daily reports, when the clock strikes 6 PM, then the user should receive an email with the performance data accumulated for the day.

User wants to generate a monthly report summarizing all campaigns for the previous month.

Given the user has requested a monthly report on the first of each month, when the previous month's data is available, then the user should receive an email summarizing all campaigns including key metrics and insights.

User checks the content of an automated report received to ensure it includes actionable insights.

Given the user receives their automated report, when they open the email, then the report should contain at least three actionable insights drawn from the performance metrics.

User must be able to modify the timing of their automated reports after setting them up initially.

Given the user has set up an automated report, when they navigate to the settings and change the report timing, then the new report timing should be saved and effective from the next scheduled report.

User unsubscribes from automated reports and wants to receive a confirmation of the action.

Given the user has clicked the unsubscribe link in their automated report email, when they confirm their decision, then they should receive a confirmation email stating that they have successfully unsubscribed from the reports.

User monitors the effectiveness of the automated reporting feature over a period of time.

Given the user has been receiving their automated reports for one month, when they evaluate the utility of the reports in driving their campaign strategies, then at least 80% should find the reports helpful for decision-making.

Enhanced Lead Scoring Models

User Story: As a sales manager, I want an improved lead scoring model that prioritizes leads based on their likelihood to convert so that I can allocate my team's efforts more efficiently.
Description: The Enhanced Lead Scoring Models requirement focuses on refining the existing lead scoring system by incorporating advanced AI algorithms and behavioral analytics. By evaluating multiple data points such as past interactions, engagement rates, and demographic information, this feature aims to improve the accuracy and efficiency of lead prioritization. This ensures that sales teams can focus on the most promising leads, which ultimately leads to higher conversion rates and more effective resource allocation.
Acceptance Criteria: Sales team accesses the Enhanced Lead Scoring Models feature during a sales meeting to evaluate and prioritize leads for a new campaign.

Given that the user is within the sales dashboard, when they apply the Enhanced Lead Scoring Models, then the system should display a ranked list of leads based on the updated scoring criteria, including engagement rates and past interactions.

A sales manager reviews lead prioritization results generated by the Enhanced Lead Scoring Models to allocate resources effectively for a marketing push.

Given that the sales manager requests a lead report, when the Enhanced Lead Scoring Model is applied, then the report should show leads ranked in descending order, with metrics on predicted conversion rates and demographic information provided for each lead.

The marketing team launches a campaign utilizing leads prioritized by the Enhanced Lead Scoring Models to measure the effectiveness of the new scoring system.

Given that a marketing campaign is launched using leads from the Enhanced Lead Scoring Model, when the campaign ends, then the conversion rate should be at least 20% higher than previous campaigns that did not utilize the enhanced models.

Sales representatives use the Enhanced Lead Scoring Models to determine which leads to contact on a specific day, based on optimal timing insights.

Given that the Enhanced Lead Scoring Model integrates with Optimal Timing Insights, when a sales representative views their lead list, then the top leads should be combined with the recommended contact times to maximize engagement.

System administrators evaluate the performance of the Enhanced Lead Scoring Models after a defined period of use to ensure continuous improvement.

Given that the use of the Enhanced Lead Scoring Models has been implemented for 3 months, when data is analyzed, then there should be at least a 15% increase in lead conversion compared to the three months prior to implementation.

Trainers provide new staff with training on using the Enhanced Lead Scoring Models effectively within the SalesMap AI platform.

Given that a training session is conducted, when new staff complete the training, then they should demonstrate the ability to analyze lead scores and integrate them into their sales strategies with at least 80% accuracy in hypothetical scenarios.

Channel Effectiveness Tracker

The Channel Effectiveness Tracker evaluates the performance of various marketing channels over time. Users can see which channels yield the best results for specific campaigns, allowing for smarter resource allocation and enhancing the overall effectiveness of their marketing strategies.

Requirements

Multi-channel Performance Analysis

User Story: As a marketing manager, I want to analyze the performance of my marketing channels over time so that I can allocate resources effectively and enhance the effectiveness of my marketing strategies.
Description: The Multi-channel Performance Analysis requirement encompasses the capability for users to evaluate the effectiveness of their marketing campaigns across various channels such as email, social media, and PPC advertising. This feature will provide detailed reports and analytics that highlight which channels generate the most leads and conversions, allowing users to make informed decisions on resource allocation. By integrating this analysis into the SalesMap AI dashboard, the system can leverage predictive analytics to suggest optimal channel strategies based on historical performance data, ensuring that businesses can maximize their marketing ROI and adapt to changing market conditions effectively.
Acceptance Criteria: User has access to the Multi-channel Performance Analysis feature in the SalesMap AI dashboard and reviews data for a recent marketing campaign across email, social media, and PPC channels.

Given the user has navigated to the Multi-channel Performance Analysis section, when they select a campaign, then they should see a detailed report showing total leads and conversions for each channel, along with performance metrics.

A marketing manager wants to allocate budget more effectively based on past campaign performance from various channels in the Multi-channel Performance Analysis feature.

Given the user views the performance report for multiple channels, when they filter results by the last three marketing campaigns, then they should see a comparative analysis that highlights the highest performing channel and the recommended budget allocation based on previous conversion rates.

User analyzes real-time data trends for their marketing channels to adapt strategies mid-campaign using the Multi-channel Performance Analysis feature.

Given the user has an active campaign, when they view the real-time analytics dashboard, then they should be able to see updated performance insights for each channel every hour, including lead generation rates and conversion changes.

A sales analyst uses the Multi-channel Performance Analysis to determine the effectiveness of a seasonal marketing effort across different channels.

Given the sales analyst selects a specific date range during a seasonal campaign, when they generate the performance report, then they should receive a report that includes channel-specific success metrics, ROI calculations, and lead conversion statistics for that period.

The marketing team is reviewing the Multi-channel Performance Analysis feature to identify trends over time for strategic planning.

Given the marketing team has access to historical data, when they request an analysis for the past twelve months, then they should receive a comprehensive report showing performance trends, growth or decline in lead conversion per channel, and suggested strategies for future campaigns.

Real-time Channel Insights

User Story: As a sales director, I want to receive real-time updates on my marketing channels' performance so that I can respond quickly and optimize our campaign strategies.
Description: The Real-time Channel Insights requirement will enable users to access up-to-the-minute data on the performance of marketing channels. This feature will utilize data from various sources and display it on a user-friendly dashboard, presenting metrics such as engagement rates, conversion rates, and cost per acquisition. By providing real-time insights, users can quickly identify trends and make immediate adjustments to their campaigns, improving responsiveness and agility in marketing efforts. This capability ensures that businesses do not miss opportunities to optimize their campaigns or react to underperforming channels.
Acceptance Criteria: User Reviews Real-time Performance of Marketing Channels during a Critical Campaign.

Given that a user is logged into the SalesMap AI platform, when they navigate to the Channel Effectiveness Tracker, then they should be able to view real-time data for engagement rates and conversion rates across all active marketing channels.

A Marketing Manager Adjusts Campaigns Based on Real-time Insights.

Given that the real-time insights are displayed on the dashboard, when the marketing manager observes a significant drop in engagement rates for a specific channel, then they should have the ability to immediately adjust the budget allocation for that channel from within the dashboard.

Users Compare Historical Channel Performance to Current Data.

Given the user selects a date range from the dashboard, when they view the performance metrics of various channels, then the data displayed should include both historical performance and real-time insights for accurate comparison.

A User Receives Notifications for Underperforming Channels.

Given that the user has set performance thresholds for marketing channels, when any channel performs below the threshold for conversion rates, then the user should receive an automated notification alerting them of the underperformance.

User Explores Details on Cost Per Acquisition for Each Channel.

Given that the real-time dashboard is displayed, when the user clicks on a specific marketing channel, then they should be able to see detailed metrics including cost per acquisition and other relevant financial statistics related to that channel.

User Integrates Third-party Analytics Tools for Extended Insights.

Given that the user wishes to connect third-party analytics tools, when they access the integration settings, then they should be able to successfully link at least one third-party tool and pull corresponding data into the SalesMap AI dashboard.

Channel Benchmarking

User Story: As a business owner, I want to compare my marketing channel performance to industry benchmarks so that I can identify areas for improvement and align my strategies with industry standards.
Description: The Channel Benchmarking requirement allows users to compare the performance of their marketing channels against industry standards and competitors. This functionality will provide users with benchmarking reports that illustrate their performance metrics in context, highlighting strengths and areas for improvement. By incorporating benchmarking data into the analysis, users can set realistic and competitive goals for their own campaigns, fostering a more strategic approach to marketing and ensuring alignment with best practices in the industry. This feature is critical for businesses looking to enhance their market position through informed, data-driven strategies.
Acceptance Criteria: As a marketing manager, I want to access benchmarking reports that compare my channel performance against industry standards, allowing me to identify strengths and weaknesses in my marketing strategy.

Given the user is logged in, when they navigate to the Channel Benchmarking section and select a specific channel, then they should see a report displaying comparative performance metrics and industry benchmarks for that channel.

As a user, I need to filter the benchmarking data by date range to analyze the performance over specific periods and understand trends more clearly.

Given the user is on the Channel Benchmarking page, when they select a date range and apply the filter, then the displayed results should only include data from the selected time frame and update the performance metrics accordingly.

As a marketing analyst, I want the ability to download benchmarking reports in various formats (PDF, Excel) for further analysis and sharing with stakeholders.

Given the user is viewing the benchmarking report, when they click on the download button, then they should have the option to download the report in either PDF or Excel format and receive a confirmation message upon successful download.

As a user, I want to receive guidance within the benchmarking reports that highlights where I stand compared to my competitors, with actionable insights for improvement.

Given the benchmarking report is generated, when the user views the report, then they should see sections that clearly indicate their performance relative to competitors, along with specific recommendations for improvement.

As a sales team member, I want to run an analysis comparing the effectiveness of my marketing channels to see how they align with overall sales performance.

Given that the user selects multiple channels for comparison, when they generate the report, then the results should clearly illustrate the correlation between channel effectiveness and sales performance metrics in a visually intuitive manner.

As a business owner, I want to ensure the benchmarking data is updated regularly to reflect the most recent industry standards, so I can make informed decisions.

Given that the benchmarking feature relies on industry standards, when the user accesses the data, then it should reflect updates made at least monthly, ensuring it is current and relevant.

Dynamic Audience Segmentation

Dynamic Audience Segmentation enables users to automatically identify and segment their target audience based on engagement metrics and behavioral trends. This feature ensures that campaigns are tailored to specific audience demographics, leading to higher conversion rates and more personalized marketing experiences.

Requirements

Automated Engagement Tracking

User Story: As a marketing manager, I want to track user engagement automatically so that I can analyze interactions and tailor my campaigns to specific audience needs without manual effort.
Description: Automated Engagement Tracking continuously monitors user interactions across various touchpoints, such as emails, social media, and ads. By utilizing advanced analytics and machine learning algorithms, this functionality provides real-time insights into audience behavior, enabling sales teams to make informed decisions based on engagement data. This requirement is crucial for quickly adapting marketing strategies, optimizing campaign performance, and enhancing user engagement, leading to elevated conversion rates and customer satisfaction.
Acceptance Criteria: User Engagement Analysis for Targeted Marketing Campaigns

Given an active engagement tracking system, when a user interacts with emails and social media campaigns, then the system should accurately log all interactions within a 5-minute timeframe and categorize them by type (clicks, opens, shares).

Real-Time Insights Dashboard Operationality

Given that engagement tracking has been activated, when a user accesses the dashboard to view engagement metrics, then the dashboard should display real-time data on user interactions with at least a 95% accuracy rate and a refresh rate of less than 10 seconds.

Segmentation of Engaged Users

Given collected engagement data, when the user selects parameters for segmentation, then the system should dynamically create segments based on defined behavioral criteria (e.g., high click-through rate, frequent interactions) within 2 minutes.

Automated Recommendations for Campaign Optimization

Given engagement metrics analyzed by the system, when user interaction data shows a downward trend, then the system should generate at least three actionable recommendations for campaign adjustments automatically.

Integration with CRM for Lead Management

Given the Automated Engagement Tracking system, when a user reviews lead data in their CRM, then the engagement history should accurately reflect all monitored interactions, allowing for a comprehensive view of each lead’s behavior.

Notification System for Significant Engagement Events

Given the engagement tracking system, when a user’s behavior crosses a defined engagement threshold (e.g., multiple interactions in a short timeframe), then the system should send an automated notification to relevant team members within 1 minute.

Reporting Functionality for Engagement Metrics

Given collected engagement data over a specified time period, when a user requests a report, then the system should generate a report summarizing key engagement metrics (e.g., conversion rate, new leads generated) within 3 minutes.

AI-Powered Predictive Segmentation

User Story: As a sales representative, I want predictive segmentation based on AI analysis so that I can focus on leads that have a higher likelihood of conversion and improve my sales effectiveness.
Description: AI-Powered Predictive Segmentation uses historical data and machine learning to forecast future behaviors of potential customers. This requirement offers insights into which customer segments are likely to convert, allowing sales teams to target their efforts more effectively. By understanding which demographics will respond positively to specific messages, companies can devise focused marketing strategies that significantly enhance ROI and customer relationship management. The integration of predictive analytics not only streamlines the marketing process but also aligns with the overall goal of maximizing growth through intelligence-driven decisions.
Acceptance Criteria: AI-Powered Predictive Segmentation for Campaign Planning

Given a collection of historical sales data, when the user inputs the data into the AI-Powered Predictive Segmentation feature, then the system should generate customer segments based on likelihood to convert, within 5 minutes of processing.

Real-Time Segment Adjustments Based on Current Engagement

Given real-time engagement metrics from various campaigns, when engagement data is updated, then the system should dynamically adjust the customer segments accordingly, reflecting changes within 2 minutes.

User-Friendly Interface for Segment Visualization

Given the generated customer segments, when the user views the segments in the dashboard, then they should see a clear visual representation of segments including demographic information and conversion likelihood scores, as well as an export option.

Integration with CRM Software for Targeted Outreach

Given a set of predictive segments, when the user selects a segment and initiates a marketing campaign, then the system should automatically integrate with the user's CRM to push the selected segment for outreach, confirming successful integration.

Performance Analysis of Segmented Campaigns

Given the results of campaigns run against predictive segments, when the user requests a performance report, then the system should provide detailed analytics on conversion rates and ROI compared to non-segmented campaigns within 24 hours of request.

Real-Time Segmentation Dashboards

User Story: As a sales analyst, I want to view audience segments in real-time on a dashboard so that I can adjust our marketing strategies dynamically based on up-to-date information.
Description: Real-Time Segmentation Dashboards provide a user-friendly interface for sales teams to view current audience segments based on live data. This requirement integrates with the existing SalesMap AI dashboard, updating segments automatically as user data changes. By enabling sales representatives to visually analyze segments in real-time, teams can quickly adjust marketing campaigns and engage with leads appropriately. This functionality is designed to enhance productivity and strategic decision-making, ensuring that teams are always targeting the right audience with the most relevant messaging.
Acceptance Criteria: User views the Real-Time Segmentation Dashboard to analyze current audience segments after recent marketing campaign adjustments.

Given the user is logged into SalesMap AI, when they access the Real-Time Segmentation Dashboard, then they should see updated audience segments reflecting the most recent engagement metrics within the last 15 minutes.

Sales representatives need to identify live audience segments during a team strategy meeting to discuss campaign effectiveness.

Given the user is in a strategy meeting, when they view the dashboard, then they should be able to filter segments based on parameters such as engagement score, demographics, and activity feed to dynamically adjust their discussion points.

A marketing manager wants to visualize how recent changes to user outreach have affected audience segmentation.

Given the user accesses the dashboard after campaign changes, when they select the 'View History' option, then they should be able to compare current segments with those from the previous week to identify trends.

A sales representative wants to ensure that the dashboard reflects real-time changes as user data is updated across the platform.

Given the user is monitoring the dashboard, when a user engagement occurs (like an email open or click), then the dashboard should automatically refresh within 30 seconds to reflect the updated segments.

The marketing team needs to ensure that messages are tailored to different audience segments based on real-time data during a campaign launch.

Given the user has identified a high-engagement segment, when they click on that segment, then they should be provided with automated recommendations for messaging that is aligned with the characteristics of that segment.

An executive wants a snapshot of the most effective segments for a quarterly report.

Given the user is preparing to generate a report, when they access the dashboard, then they should be able to export current segment data into a CSV format, including metrics like engagement rates, demographics, and campaign success indicators.

Customizable Segmentation Criteria

User Story: As a marketer, I want to customize the criteria for audience segmentation so that I can tailor campaigns to fit the unique needs of our different target groups.
Description: Customizable Segmentation Criteria allows users to define specific parameters for audience segmentation based on their unique business needs. This requirement enables marketers to create niche segments by setting filters such as demographics, past purchase behavior, and engagement scores. The flexibility provided by customizable criteria empowers businesses to optimize their marketing efforts, ensuring relevance and personalization in campaigns. By utilizing tailored segments, businesses can drive stronger connections with their audiences, leading to improved engagement and conversion rates.
Acceptance Criteria: User wants to create a new audience segment based on age and geographic location.

Given the user has access to the segmentation tool, when they select 'Create New Segment', and input age range and geographic location criteria, then the system should successfully create the segment and confirm with a message.

User needs to filter audiences based on past purchase behavior to target re-engagement campaigns.

Given the user is in the segmentation interface, when they apply a filter for past purchase data, then the system should present only those audience members who meet the specified purchase criteria, retaining the integrity of the data displayed.

Marketer desires to segment audiences by engagement scores for high-value campaigns.

Given the user selects engagement scores as a criterion, when they define the minimum engagement threshold and save the criteria, then the system should create a segment that reflects all users above that threshold.

User wants to modify an existing audience segment to include new demographic filters.

Given the user has an existing audience segment, when they select 'Edit Segment', add new demographic criteria, and save, then the system should update the segment and reflect the changes in real-time.

User aims to receive a preview of the audience breakdown before finalizing the segmentation.

Given the user has applied segmentation criteria, when they click on 'Preview Segment', then the system should display a summary of the segment including the number of users and key demographic information without completing the segment creation.

User needs to ensure that segmentation criteria can be saved for future use.

Given the user has defined a set of segmentation criteria, when they select 'Save Criteria' and assign a name, then the system should store the criteria under the user's profile for easy access in the future.

User wants to ensure that their segmented audiences remain dynamic as user behaviors change.

Given the user has created a dynamic audience segment, when the criteria are met by new user activity, then the system should automatically update the segment to reflect these changes without requiring manual intervention.

Integration with Third-Party CRM Systems

User Story: As a business owner, I want SalesMap AI to integrate with my existing CRM system so that I can maintain a single source of truth for customer data and streamline my sales operations.
Description: Integration with Third-Party CRM Systems facilitates seamless data exchange between SalesMap AI and various customer relationship management platforms. This requirement ensures that lead and segmentation data is synced accurately, allowing for a unified view of customer interactions and insights. The integration supports enhanced workflows, reduces manual data entry, and aligns marketing efforts with sales strategies. By connecting to popular CRM systems, this feature increases efficiency and allows SMBs to leverage existing resources effectively, thereby boosting overall productivity.
Acceptance Criteria: User attempts to integrate SalesMap AI with their existing CRM system to synchronize lead and segmentation data.

Given a valid API key for the third-party CRM, when the user initiates the integration process, then the system should successfully connect to the CRM and import existing leads without any errors.

User updates customer segmentation in SalesMap AI and expects these updates to reflect in their CRM system.

Given that user has segmented their audience based on engagement metrics, when the user updates the segmentation in SalesMap AI, then the updates should be instantly reflected in the connected CRM system with accuracy.

User needs to verify that data synchronization between SalesMap AI and their CRM occurs at regular intervals without fail.

Given the scheduled synchronization is configured, when the time interval elapses, then lead and segmentation data should be updated in both systems within a maximum of 5 minutes delay.

User wants to view a comprehensive report in SalesMap AI that includes data from both SalesMap AI and their CRM.

Given the integration is set up and data is synchronized, when the user views the 'Integrated Reports' dashboard, then the report should accurately display data sourced from both SalesMap AI and the connected CRM, reflecting real-time metrics.

User engages in a support query regarding integration issues and wants prompt assistance.

Given the user has integration issues, when they submit a support request through SalesMap AI, then the system should acknowledge the request and provide a response within 24 hours.

User requires the integration setup process to be documented clearly for ease of use.

Given the need for user guidance, when the user accesses the integration help section, then the documentation should include step-by-step instructions and troubleshooting tips that are easy to follow.

Multi-Channel Campaign Integration

Multi-Channel Campaign Integration allows users to seamlessly coordinate campaigns across various platforms, such as email, social media, and web. By ensuring a cohesive marketing message, this feature maximizes audience exposure and enhances brand visibility during critical engagement periods.

Requirements

Unified Campaign Dashboard

User Story: As a marketing manager, I want to view all my campaign performance metrics in one place so that I can quickly assess effectiveness and make informed decisions on strategies.
Description: The Unified Campaign Dashboard provides a centralized view for users to monitor and manage multi-channel campaigns. This requirement involves the integration of various marketing platforms into a single dashboard, allowing users to track the performance, engagement metrics, and conversion rates of campaigns across email, social media, and web channels. The dashboard will enhance decision-making by providing real-time data and analytics, helping users identify successful strategies and areas needing improvement. It aims to simplify campaign management, increase operational efficiency, and provide insights that drive better marketing outcomes.
Acceptance Criteria: Campaign Performance Overview for Multi-Channel Strategies

Given the user is on the Unified Campaign Dashboard, when they select a specific multi-channel campaign, then they should see a comprehensive overview of performance metrics including engagement rates, conversion rates, and revenue generated.

Real-Time Data Updates on Campaign Metrics

Given the user is monitoring live campaigns on the Unified Campaign Dashboard, when a campaign metric changes, then the dashboard should update to reflect the latest data within 10 seconds.

Filter and Sort Campaign Data

Given the user has multiple campaigns displayed on the dashboard, when they apply filters or sort options (by date, performance, channel), then the displayed data should adjust accordingly to show only the relevant campaigns.

Integration with External Marketing Platforms

Given the user has connected their social media and email marketing accounts to the Unified Campaign Dashboard, when they log into the dashboard, then they should see data from those external platforms accurately integrated and displayed.

User-Friendly Design for Campaign Management

Given the user is interacting with the Unified Campaign Dashboard, when they navigate the interface, then they should find the layout intuitive and be able to access key features in three clicks or less.

Export Campaign Reports

Given the user wants to analyze their campaign performance further, when they use the export feature on the dashboard, then they should receive a downloadable report in Excel format containing all metrics displayed.

Alerts for Underperforming Campaigns

Given the user has set thresholds for campaign performance, when a campaign falls below the specified performance threshold, then the dashboard should automatically alert the user via a notification or email.

Automated Campaign Scheduling

User Story: As a small business owner, I want to schedule my marketing campaigns in advance so that I can focus on other areas of my business without worrying about manual posting.
Description: Automated Campaign Scheduling allows users to set up and schedule campaigns across multiple channels in advance. This requirement includes the development of a scheduling tool that enables users to choose specific dates and times for their campaigns to go live, ensuring maximum reach during optimal engagement periods. The capability to automate scheduling reduces manual intervention, saves time for marketers, and ensures a consistent presence across platforms. Additionally, users will receive reminders and notifications about upcoming campaign launches, helping them stay organized and proactive.
Acceptance Criteria: User wants to schedule a campaign to launch across email and social media at 10 AM on January 15, 2025, ensuring optimal engagement with their target audience during peak hours.

Given the user accesses the campaign scheduling tool, when they select ‘January 15, 2025’ and set the time to ‘10 AM’, then the system must confirm that the campaign is scheduled and provide a countdown until launch.

A marketing team conducts a campaign that automatically posts updates on social media channels and sends emails to subscribers. They want to ensure that both actions happen at the same time, without manual intervention.

Given that the user sets up a campaign to launch on multiple channels, when the scheduled time is reached, then all selected channels must execute the campaign simultaneously without errors.

After scheduling a campaign for the next month, the user expects to receive reminders about the upcoming launch to ensure they are prepared in advance.

Given the user has a scheduled campaign, when the campaign is one week away, then the user must receive a notification reminder via email and on the dashboard about the upcoming launch.

A user schedules two campaigns on the same day, and they want to ensure the system can handle scheduling multiple campaigns without overlap issues.

Given two campaigns scheduled for the same day, when the user reviews their scheduled campaigns, then the system must display both campaigns without conflicts or errors in the timeline view.

A user attempts to schedule a campaign for a past date and expects the system to prevent this action to maintain accurate scheduling.

Given the user enters a past date in the scheduling tool, when they attempt to confirm the scheduling, then the system must display an error message stating that past dates are not allowed.

The user needs to change the launch time of a scheduled campaign to a different time, ensuring the updates are correctly processed and notifications are adjusted accordingly.

Given a campaign is scheduled, when the user modifies the launch time, then the system must successfully update the campaign time and send an adjusted notification to the user confirming the new time.

Cross-Channel Analytics Reporting

User Story: As a data analyst, I want to generate reports that summarize the performance of campaigns across all channels so that I can identify the most effective marketing strategies and allocate resources effectively.
Description: Cross-Channel Analytics Reporting enables users to generate comprehensive reports that analyze campaign performance across multiple channels in a cohesive manner. This requirement involves developing reporting tools that aggregate data from various marketing channels into easy-to-read formats, such as charts and graphs. These insights will help users understand overall performance, identify high-performing channels, and uncover trends in customer engagement. This functionality aims to enhance strategic planning and optimize marketing budget allocations based on performance metrics.
Acceptance Criteria: Generate a comprehensive cross-channel analytics report after a multi-channel marketing campaign has concluded.

Given the user has finished a multi-channel marketing campaign, when they access the reporting tool, then they should be able to generate a report that aggregates performance data from all channels in a single view, including charts and graphs to visualize results.

View performance metrics on specific channels within the cross-channel analytics report.

Given the user is viewing the cross-channel analytics report, when they filter by a specific marketing channel, then the report should update to display only the metrics relevant to that channel, including engagement rates, conversion rates, and total impressions.

Identify high-performing channels based on data from the cross-channel analytics report.

Given the user is analyzing the cross-channel analytics report, when they look for insights, then they should be able to identify which channels have the highest conversion rates, allowing them to prioritize future campaigns accordingly.

Access cross-channel analytics reports on the SalesMap AI dashboard.

Given the user is logged into the SalesMap AI dashboard, when they navigate to the reports section, then they should see an option to generate cross-channel analytics reports alongside individual channel reports, ensuring seamless access.

Export the cross-channel analytics report in various formats for external sharing.

Given the user has generated a cross-channel analytics report, when they choose to export the report, then they should have the option to download it in PDF, Excel, and CSV formats, ensuring compatibility with other systems.

Receive alerts about trends identified in the cross-channel analytics report.

Given the user has generated a cross-channel analytics report, when the system identifies significant trends or anomalies in performance metrics, then the user should receive automated alerts with suggestions for action based on those insights.

Allow users to customize the metrics displayed in their cross-channel analytics reports.

Given the user is in the cross-channel analytics report, when they select which metrics to display, then the report should update in real-time to reflect their custom selections, enabling tailored analysis.

Real-Time Audience Segmentation

User Story: As a digital marketer, I want to segment my audience in real-time based on their behavior so that I can personalize my marketing efforts and increase engagement rates.
Description: Real-Time Audience Segmentation allows users to create and modify audience segments based on live data. This requirement involves the integration of machine learning algorithms that analyze user behavior and engagement in real-time, enabling dynamic adjustments to target specific demographic or behavioral segments as campaigns are running. This feature maximizes campaign relevance and effectiveness by ensuring that marketing messages are tailored to specific groups, increasing the likelihood of engagement and conversions. It will enhance user experience and improve the efficiency of marketing efforts.
Acceptance Criteria: User creates an audience segment based on real-time data derived from user engagement metrics during a live campaign.

Given a marketing campaign is live, when the user accesses real-time data, then they should be able to create or modify audience segments based on the engagement metrics available.

User modifies existing audience segments dynamically as new data comes in during an active campaign.

Given a user is viewing a current campaign, when new user behavior data is collected, then the modifications to existing audience segments should automatically reflect in the campaign targeting settings.

User evaluates the effectiveness of audience segments in real-time to optimize campaign performance.

Given the user has access to a dashboard displaying real-time campaign metrics, when they analyze audience segment performance, then they should be able to identify which segments are yielding the highest engagement and conversion rates.

User receives automated recommendations for segment adjustments based on machine learning insights during an ongoing campaign.

Given the campaign is in progress, when the machine learning algorithms analyze user engagement, then the user should receive actionable recommendations for segment adjustments to enhance campaign effectiveness.

User integrates Real-Time Audience Segmentation with Multi-Channel Campaign Integration to ensure cohesive messaging across platforms.

Given the Real-Time Audience Segmentation is in use, when the user runs campaigns across multiple channels, then the audience segments should be applied consistently to deliver unified marketing messages.

User tests the speed and accuracy of real-time audience segmentation feature during a campaign.

Given the user initiates a segmentation change during a live campaign, when they validate the changes, then the segments should update within 2 minutes with 95% accuracy based on real-time engagement data.

Integrated Lead Scoring

User Story: As a sales representative, I want to receive lead scores in real-time from my campaigns so that I can prioritize my follow-ups and focus on the most likely prospects to convert.
Description: Integrated Lead Scoring involves implementing a feature that automatically assesses and scores leads generated from multi-channel campaigns based on their engagement levels and demographic data. This requirement focuses on developing an algorithm that assigns scores to leads in real-time, allowing sales teams to prioritize high-quality prospects efficiently. By integrating lead scoring directly into the campaign management system, users can streamline their sales processes and ensure that the most promising leads are followed up with first. This capability aims to enhance sales efficiency and improve conversion rates for the business.
Acceptance Criteria: Real-time Lead Scoring during Campaign Execution

Given a running multi-channel campaign, When a new lead interacts with any campaign platform, Then the system should automatically assign a lead score based on engagement level and demographic data within 5 seconds.

Leaderboard Prioritization Based on Lead Scores

Given a list of leads generated from an active campaign, When the sales team accesses the leads leaderboard, Then the leads should be sorted in descending order based on their integrated lead scores to prioritize follow-ups.

Performance Monitoring of Lead Scoring System

Given that leads are being scored automatically, When reviewing lead scores after one month of operation, Then at least 90% of the scored leads should be validated as qualified based on subsequent sales conversion rates.

User Notification of High-Scoring Leads

Given a certain threshold for high lead scores, When a lead is scored above this threshold, Then the sales team should receive a real-time notification through the dashboard and email alerts.

Integration with CRM for Scored Leads

Given that leads are scored, When a lead is scored, Then their score should automatically update in the connected CRM system to ensure all sales team members have access to the latest information.

Adjusting Lead Scoring Parameters

Given that the lead scoring system has been implemented for three months, When the marketing team reviews scoring parameters, Then they should be able to adjust at least three scoring criteria based on new insights without technical assistance.

User Training on Lead Scoring Functionality

Given that Integrated Lead Scoring has been launched, When the sales team is trained on the new feature, Then at least 80% of participants should report confidence in using the lead scoring system effectively through a post-training survey.

Social Media Monitoring and Engagement

User Story: As a social media manager, I want to monitor interactions related to my campaigns so that I can engage with my audience promptly and build stronger relationships with them.
Description: Social Media Monitoring and Engagement allows users to track mentions and interactions related to their campaigns across social media platforms. This requirement focuses on developing tools that notify users of engagement opportunities, such as comments, shares, and direct messages related to their campaigns. Additionally, it will enable users to respond to engagement directly from the platform, enhancing interaction with customers and prospects. This feature aims to build community engagement, enhance brand loyalty, and improve customer relationships through timely responses and interactions.
Acceptance Criteria: User receives a notification of a comment on a social media post related to their campaign while logged into SalesMap AI.

Given the user is logged into SalesMap AI, when a social media post receives a comment, then the user should receive a real-time notification in the dashboard indicating the comment with a link to view and respond to it.

User accesses social media engagement metrics within SalesMap AI to evaluate the effectiveness of their campaigns.

Given the user navigates to the `Social Media Monitoring` section, when they select a specific campaign, then they should see comprehensive engagement metrics including likes, shares, comments, and direct messages, represented in both numerical and graphical formats.

User replies to a direct message received on a social media platform through SalesMap AI without switching applications.

Given the user receives a direct message on a linked social media platform, when they view the message through the SalesMap AI interface, then they should be able to compose and send a response directly from the platform.

User wants to configure social media notifications based on specific engagement metrics (likes, comments, shares).

Given the user is in the notification settings, when they select engagement metrics to monitor, then they should successfully save their preferences and receive notifications accordingly based on their configured settings.

User views a list of social media platforms integrated into SalesMap AI for campaign monitoring.

Given the user accesses the `Social Media Integration` settings, when they view the list of integrated platforms, then they should see a clear list of all connected social media accounts with the option to add or remove integrations.

User tracks mentions of their brand or campaign across social media platforms.

Given the user is using the `Brand Mention Tracker`, when they input a keyword related to their campaign, then they should receive a list of all recent mentions from various platforms with timestamp and engagement stats.

Real-Time Performance Adjustments

Real-Time Performance Adjustments enable users to tweak campaign parameters as they run based on real-time analytics and feedback. This feature allows for immediate responses to market shifts or audience reactions, optimizing campaign effectiveness dynamically.

Requirements

Dynamic Parameter Adjustments

User Story: As a marketing manager, I want to adjust my campaign parameters in real-time so that I can optimize performance based on live audience feedback and analytics.
Description: The Dynamic Parameter Adjustments requirement focuses on allowing users to modify campaign parameters such as budget, targeting criteria, and messaging in real-time based on live analytics and audience engagement data. This feature is essential for ensuring that marketing campaigns remain relevant and effective as they progress, addressing immediate challenges and opportunities identified through analytics. By enabling users to adjust these parameters on the fly, SalesMap AI enhances the overall performance and ROI of marketing campaigns, fostering a more agile marketing environment that can pivot and react based on real-time feedback. The integration with the existing dashboard will allow users to see the impact of their adjustments instantaneously, making data-driven decisions more actionable and impactful.
Acceptance Criteria: User modifies campaign parameters in real-time during an ongoing marketing campaign to respond to decreasing engagement metrics.

Given that the user has access to the campaign dashboard, when they adjust the budget by 20% based on real-time engagement analytics, then the new budget should be reflected in the campaign settings immediately without requiring a page refresh.

A user reviews the dashboard for audience feedback and decides to change the targeting criteria to improve ad performance.

Given that the campaign is currently live, when the user updates the targeting criteria to focus on a different demographic, then the changes should be applied within 60 seconds and the modified audience size should be displayed in the analytics view.

The user wishes to change the messaging of an email campaign while monitoring its performance metrics.

Given the user is on the performance dashboard, when they update the messaging content for the ongoing campaign, then the changes should be saved successfully and reflected in the next batch of outgoing emails within the current sending window.

A marketing manager is alerted of a sudden drop in conversion rates via a dashboard notification and needs to make quick adjustments.

Given that a conversion rate drop is detected, when the user clicks on the notification to adjust parameters, then they should be able to access a quick-edit panel that allows them to adjust at least three parameters in under two minutes.

The user analyzes the performance impact of recent changes made to a live campaign.

Given that parameters have been adjusted in real-time, when the user views the analytics dashboard afterward, then the performance metrics (e.g., ROI, click-through rates) should display changes corresponding to the adjustments made within 5 minutes of implementation.

Live Analytics Dashboard Integration

User Story: As a sales director, I want to see live analytics of my ongoing campaigns on a dashboard so that I can make timely decisions to improve campaign performance.
Description: The Live Analytics Dashboard Integration requirement mandates the creation of a dedicated dashboard displaying real-time analytics relevant to ongoing marketing campaigns. This dashboard will visualize key performance indicators, audience engagement metrics, and budget utilization in a user-friendly format. It plays a crucial role in providing marketers with immediate insights and actionable data, allowing them to make informed decisions without delay. By integrating this dashboard into SalesMap AI, users will benefit from a centralized place to access all necessary live data, making campaign performance tracking simpler and more efficient. This feature is crucial for maximizing the benefits of real-time performance adjustments and ensuring that the insights provided lead to meaningful action.
Acceptance Criteria: User views the live analytics dashboard during an ongoing marketing campaign to gauge real-time performance and make adjustments as necessary based on the displayed metrics.

Given the user has an active marketing campaign running, when the user accesses the live analytics dashboard, then the dashboard should display real-time analytics for the ongoing campaign, including key performance indicators and audience engagement metrics.

Marketers need to assess whether the dashboard integrates seamlessly with their existing CRM to reflect up-to-date campaign performance and audience interactions.

Given that the user has integrated their CRM with SalesMap AI, when they view the live analytics dashboard, then all data shown must correlate accurately with the latest information pulled from the CRM without any discrepancies.

A marketer wants to ensure that critical metrics such as budget utilization and conversion rates are visually represented in a way that is easy to understand at a glance.

Given the live analytics dashboard is displayed, when the user looks at the dashboard, then key performance indicators must be presented through clear visualizations such as graphs and charts that highlight budget utilization, conversion rates, and audience engagement metrics effectively.

The marketing team runs a campaign for a specific product and wants to react immediately to any underperforming metrics provided by the dashboard.

Given that the marketing campaign is in progress, when any metric falls below a predefined threshold set by the marketer, then the system should prompt an alert on the dashboard for immediate review and action.

Users require the ability to customize the metrics displayed on the live analytics dashboard according to specific campaign goals and key performance indicators.

Given the user is on the live analytics dashboard, when they select different metrics for display from a custom settings option, then the dashboard should immediately adjust to show only the selected metrics without needing to refresh.

Users must be able to access the live analytics dashboard on multiple devices to allow for flexibility and accessibility while monitoring campaign performance.

Given that the user is logged into SalesMap AI, when they access the live analytics dashboard on a mobile device or tablet, then the dashboard should maintain full functionality and display the same metrics as on a desktop version.

Automated Alert System for Performance Metrics

User Story: As a campaign analyst, I want to receive alerts when key performance metrics exceed or drop below specific thresholds so that I can quickly address issues or leverage opportunities in my campaigns.
Description: The Automated Alert System for Performance Metrics is designed to notify users immediately when key performance indicators (KPIs) fall below or exceed predefined thresholds during ongoing campaigns. This requirement will help marketers respond quickly to problematic areas or capitalize on successful strategies in real-time. The alert system can be customizable, allowing users to set specific metrics they want to be alerted about, such as engagement rates, conversion rates, and budget consumption. This feature directly supports the goal of improving campaign effectiveness and efficiency through proactive management, ensuring users are always informed and enabling them to take swift corrective actions when necessary.
Acceptance Criteria: User configures alert settings for multiple performance metrics in the Automated Alert System, choosing KPIs such as engagement rates, conversion rates, and budget consumption thresholds.

Given the user is in the settings menu of the Automated Alert System, when they successfully configure thresholds for engagement rate at 75%, conversion rate at 30%, and budget consumption at 90%, then the system must save these configurations and display them on the user dashboard.

During a live campaign, key performance metrics drop below the set thresholds specified by the user.

Given a campaign is running and the engagement rate falls below the 75% threshold, when the performance metric is updated, then a notification alert should be sent to the user immediately via email and the dashboard.

The user wants to customize the alert settings while a campaign is ongoing to adapt to market responses.

Given that the campaign is active, when the user accesses the alert configuration settings and changes the threshold for conversion rate from 30% to 25%, then the system should save this new threshold and apply it immediately without needing to restart the campaign.

The user receives an alert for a performance metric that exceeds the desired thresholds during a campaign.

Given the conversion rate for the campaign exceeds the 30% threshold, when this metric is updated, then an alert should be delivered to the user indicating successful performance, through both the dashboard and a mobile notification.

The user checks the history of alerts received for past campaigns.

Given the user navigates to the alerts history section, when they view alerts, then the system should display a list of alerts with the corresponding metrics and timestamps, categorized by each campaign.

The user troubleshooting an alert notification that was not received when performance metrics fell below the threshold.

Given the user reports they did not receive an alert for the engagement rate dropping below 75%, when the system checks the notification logs, then it must display whether the alert was triggered and sent successfully, including any errors encountered.

User attempts to disable alerts for specific performance metrics in the alert configuration settings.

Given the user is in the alert configuration menu, when they disable notifications for budget consumption without affecting engagement or conversion rate alerts, then the system must confirm that budget consumption alerts are turned off while keeping others active.

User Role-based Access Control

User Story: As an admin, I want to set role-based access controls for my team members so that I can ensure data security and proper workflow management within our marketing campaigns.
Description: The User Role-based Access Control requirement establishes a system for defining user roles within the SalesMap AI platform, determining which campaign parameters and analytics each role can access and modify. This feature is essential for maintaining data security and ensuring that sensitive information is only accessible to authorized personnel. By allowing organizations to tailor access based on user roles, SalesMap AI helps streamline workflows and ensure that team members have the appropriate level of control needed for their responsibilities. This structured approach improves collaboration among team members while safeguarding critical campaign data from unauthorized changes.
Acceptance Criteria: User Role-based Access Control for Marketing Managers

Given a user assigned with the Marketing Manager role, when they log into SalesMap AI, then they should have access to modify campaign parameters and view detailed analytics while having restricted access to sensitive data such as financial reports.

User Role-based Access Control for Sales Representatives

Given a user assigned with the Sales Representative role, when they log into SalesMap AI, then they should be able to access and adjust only their assigned leads' campaign parameters but cannot edit other users' leads or view overall campaign performance metrics.

User Role-based Access Control for Admin Users

Given a user assigned with the Admin role, when they log into SalesMap AI, then they should have full access to all campaign parameters, analytics, and user management features, enabling them to implement changes across all user roles and campaigns.

User Role-based Access Control validation during role assignment

Given an Admin user is assigning roles to team members, when they select a user and assign them a specific role, then the system should accurately reflect these changes in the user’s access permissions immediately after saving the changes.

Audit log for User Role-based Access Control changes

Given any changes made to user role assignments in SalesMap AI, when the changes are saved, then an audit log entry should be created capturing the timestamp, user making the change, old role, new role, and affected user details for traceability purposes.

User Role-based Access Control response to unauthorized access attempt

Given a user attempts to access campaign parameters or analytics they do not have permission for, when they try to access this data, then the system should display an error message indicating insufficient permissions without revealing any sensitive information.

Feedback Loop Mechanism

User Story: As a product manager, I want to gather customer feedback on my marketing campaigns so that I can refine our strategies and better meet the needs of our audience.
Description: The Feedback Loop Mechanism requirement introduces a systematic approach to collecting and analyzing customer feedback on campaign performance. This feature will enable users to gather insights from audience interactions, such as surveys or feedback forms, during and after campaign execution. This process ensures that real user opinions and experiences shape future campaign strategies and adjustments. By creating a structured feedback loop, SalesMap AI allows users to continuously improve their marketing efforts based on actual audience preferences and behaviors, making campaigns more targeted and effective over time.
Acceptance Criteria: User collects customer feedback via a survey during an active campaign to assess audience engagement and satisfaction levels.

Given a campaign is actively running, When a user initiates a feedback survey, Then responses should be collected and displayed in the dashboard in real-time.

After a campaign concludes, the user analyzes the feedback gathered to gain insights for future campaigns.

Given the feedback has been collected, When the user accesses the feedback report, Then the report should summarize key insights and actionable recommendations for future strategies.

Users receive notifications about new feedback submissions during a running campaign to facilitate timely responses.

Given new feedback submissions occur, When the submissions are received, Then the user should be notified via the platform's notification system immediately.

A user adjusts campaign parameters based on feedback received from previous campaigns during the execution of a new campaign.

Given the feedback from previous campaigns is accessible, When the user modifies campaign parameters in response, Then the changes should be applied immediately with metrics tracked in real-time.

Users are able to customize feedback forms to ask specific questions related to their campaigns during execution.

Given a user wants to gather targeted feedback, When they design a feedback form, Then the customization options should allow for multiple question types and direct integration into the campaign.

The system compiles customer feedback data after a campaign and presents it in an easily interpretable format for the user.

Given the campaign has ended, When the user requests the feedback data, Then the system should provide a visually appealing report that highlights trends and patterns in the feedback.

Automated Follow-Up Scheduling

Automated Follow-Up Scheduling helps users schedule follow-ups or reminders for their audience based on campaign engagement. This ensures timely communication that nurtures leads, enhancing the chances of conversion and maintaining campaign momentum.

Requirements

Dynamic Engagement Analytics

User Story: As a marketing manager, I want to view real-time engagement metrics for my campaigns so that I can understand my audience's interests and adjust my strategies accordingly.
Description: Dynamic Engagement Analytics provides users with real-time insights into audience interaction with their campaigns. This requirement focuses on capturing and analyzing engagement metrics such as open rates, click-through rates, and response times. By offering detailed analytics, users can effectively gauge the interests and behaviors of their audience. The analytics integrate seamlessly with the existing dashboard, enriching user experience by delivering actionable data that can inform follow-up strategies. This enhancement helps users to optimize their campaigns and tailor their messaging for improved outcomes and higher conversion rates.
Acceptance Criteria: User views engagement metrics for a recent email campaign.

Given the user is logged into the SalesMap AI dashboard, when the user navigates to the 'Campaign Analytics' section and selects an email campaign, then the dashboard displays the real-time engagement metrics including open rates, click-through rates, and response times.

User analyzes engagement metrics to improve follow-up strategies.

Given the user has accessed the engagement analytics for their campaign, when the user reviews the data and identifies that the click-through rate is below a predefined threshold, then the user receives recommendations for follow-up strategies specific to that campaign.

User checks the integration of engagement analytics with the dashboard.

Given the user is on the SalesMap AI dashboard, when the user refreshes the page, then the engagement metrics should update in real-time without the need for manual refresh and should accurately reflect the latest campaign data.

User exports engagement analytics data for reporting.

Given the user is viewing their engagement analytics on the dashboard, when the user selects the 'Export' option, then the system should generate a downloadable report in CSV format containing the detailed engagement metrics.

User organizes campaigns based on engagement levels.

Given the user is viewing a list of their campaigns, when the user applies filters for engagement metrics, then the campaigns should be sorted accordingly, allowing the user to easily identify high and low performing campaigns.

Personalized Reminder Notifications

User Story: As a sales representative, I want to send personalized reminders to leads based on their previous engagements so that I can maintain their interest and increase conversion rates.
Description: Personalized Reminder Notifications enable users to send tailored follow-up reminders based on individual lead profiles and their interactions with past campaigns. This requirement ensures that reminders are relevant and context-specific, enhancing the chances of conversion through timely and meaningful communication. It integrates with the user interface to allow custom message creation and scheduling based on user-defined rules. The outcome is more engaged leads and a strong nurturing process that increases the likelihood of successful conversions, providing users with a competitive edge in their sales strategies.
Acceptance Criteria: Users need to set personalized follow-up reminders for high-value leads engaged with their most recent marketing campaign, ensuring that the reminders are scheduled at optimal times based on lead activity.

Given a user has identified a high-value lead with engagement in a recent campaign, when they schedule a follow-up reminder, then the reminder should be delivered at the optimal time based on the lead's previous interactions and preferences.

A user wants to send customized reminder notifications for a lead who opened multiple emails but did not take further actions. The goal is to prompt the lead effectively without coming off too aggressive.

Given a lead has opened emails but has not responded to previous calls to action, when the user creates a custom reminder notification, then the reminder should reflect the lead's past interactions and encourage a specific next step subtly and effectively.

After setting up several automated reminders, a user wants to verify if the notifications were sent as planned to all specified leads at their scheduled times.

Given multiple reminders have been scheduled for different leads, when the delivery logs are checked, then the system should show a successful delivery status for each reminder indicating the time they were sent.

A user needs to adjust the scheduled follow-up reminders due to a change in strategy or timing after reviewing campaign performance metrics. They want to efficiently modify existing reminders without starting from scratch.

Given a user has existing follow-up reminders set up, when they choose to edit a reminder, then they should be able to easily modify the content and timing without losing any previous data or settings associated with that reminder.

A sales manager wants to analyze the effectiveness of the personalized reminder notifications in converting leads after a specific campaign period.

Given a sales period has concluded, when the sales manager reviews the conversion metrics associated with leads that received personalized reminders, then they should see a measurable increase in conversion rates directly linked to those reminders compared to leads who did not receive them.

Multi-Channel Follow-Up Integration

User Story: As a user, I want to schedule follow-ups across multiple channels so that I can effectively reach my leads and maximize engagement opportunities.
Description: Multi-Channel Follow-Up Integration expands the capability of the follow-up scheduling feature by allowing users to communicate with leads through various channels, including email, SMS, and social media. This requirement ensures that users can reach their audience where they are most likely to engage. Integration with existing communication tools will streamline the process, enabling users to select preferred channels for follow-ups easily. The implementation of this feature enhances user efficiency and improves the overall response rates from leads, contributing to holistic sales strategies.
Acceptance Criteria: User schedules a follow-up for a lead who engaged with an email campaign.

Given a lead has engaged with the email campaign, When the user selects the lead and chooses to schedule a follow-up, Then the follow-up is scheduled successfully through the selected channel (email/SMS/social media) and confirmation is provided to the user.

User selects SMS as the follow-up channel for a lead.

Given the user has configured SMS settings in the application, When the user selects SMS as the follow-up channel, Then the system successfully sends a test SMS to the user to confirm functionality before scheduling the follow-up.

User receives reminders for scheduled follow-ups through their preferred channel.

Given a follow-up has been scheduled, When the follow-up is scheduled to occur within the next 24 hours, Then the user receives a reminder via their selected notification channel (email/SMS) 1 hour before the scheduled time.

User can view all upcoming follow-ups in a consolidated view.

Given the user navigates to the follow-up scheduling dashboard, When the user views the upcoming follow-ups section, Then all follow-ups are displayed in chronological order, including details such as lead name, channel, and scheduled time.

User integrates the follow-up scheduling feature with their existing CRM.

Given the user has linked their CRM account to SalesMap AI, When they create a follow-up, Then the lead information is automatically pulled from the CRM, ensuring data consistency and reducing manual entry.

User successfully changes the channel of a scheduled follow-up.

Given a follow-up is already scheduled, When the user selects the option to change the channel and chooses a different one, Then the follow-up is updated accordingly, and the user receives confirmation of the change.

AI-Powered Timing Optimization

User Story: As a sales agent, I want AI to suggest optimal follow-up times for different leads so that I can increase my chances of getting responses and closing deals.
Description: AI-Powered Timing Optimization leverages machine learning algorithms to determine the best times for follow-up communications based on historical engagement data. This requirement analyzes patterns of lead interaction to suggest optimal follow-up windows, enhancing the likelihood of responses. With this feature, users can automate follow-up timing based on predictive analytics, which can lead to more effective outreach and reduced manual scheduling. The expected outcome is improved engagement rates and a more efficient approach to lead nurturing.
Acceptance Criteria: User receives a lead engagement report suggesting follow-up times optimally based on historical data.

Given the user has uploaded historical engagement data, When the user initiates the report generation, Then the system should provide follow-up time suggestions based on analyzed data.

User schedules follow-up communications using the AI-Powered Timing Optimization feature for a specific lead.

Given the user has selected a lead from their CRM, When they opt to schedule a follow-up, Then the system should automatically suggest an optimal timing based on the lead's past engagement patterns.

User reviews the effectiveness of follow-up timing recommendations over a specified period.

Given the user has followed up with leads using the automated recommendations, When the user checks the engagement analytics after a month, Then the system should show improved response rates compared to the previous period without AI recommendations.

User wants to ensure that the automated follow-up scheduling can adjust timings based on new engagement data.

Given the user has changed the engagement preferences for a specific lead, When the engagement data is updated, Then the system should re-evaluate and suggest new follow-up timings accordingly.

User is setting up an automated follow-up sequence for a marketing campaign.

Given the user has defined the parameters of the marketing campaign, When they implement the automated follow-up sequence, Then the system should apply AI-driven timing adjustments to all follow-ups based on engagement prediction.

Integrated Feedback Collection

User Story: As a marketer, I want to collect feedback from leads after follow-ups so that I can refine my approach and improve future communications.
Description: Integrated Feedback Collection allows users to gather responses from leads after follow-up communications automatically. This requirement enables users to solicit feedback on their interaction experience and understand lead sentiment, facilitating the refinement of sales approaches. The collected data will be analyzed to provide insights into effective strategies and areas for improvement. Integration with the existing CRM system ensures that feedback is stored and can be referenced for future interactions, leading to a more responsive sales process.
Acceptance Criteria: User initiates follow-up scheduling after a marketing campaign ends and wants to collect feedback from the leads who engaged with the campaign.

Given a completed marketing campaign, when the user selects leads to follow up with and schedules feedback requests, then the system should automatically send out feedback collection requests to the selected leads within 24 hours.

A user reviews gathered feedback from leads to analyze response trends and lead sentiment regarding their experience with the follow-up communications.

Given that feedback has been collected from leads, when the user accesses the feedback analytics dashboard, then the user should be able to view summarized data trends, individual feedback responses, and lead sentiment scores in a visually comprehensible format.

When a lead responds to a feedback request, the system must ensure that the feedback is correctly logged in the integrated CRM system for future reference.

Given a lead submits feedback, when the feedback is received by the system, then the system should automatically log the feedback details into the corresponding lead's profile in the CRM within 5 minutes of receiving it.

Users want to receive notifications when feedback is collected to ensure timely review and response.

Given that a feedback response has been logged, when the response data is saved, then the user should receive a notification (email or app alert) within 10 minutes to inform them of the new feedback submission.

Users need to ensure that feedback collection requests are sent only to leads who engaged with the campaign, thus optimizing outreach efforts.

Given a list of leads, when the user selects leads for feedback scheduling, then the system should filter and allow feedback requests only to those leads who engaged with the campaign, excluding unengaged leads.

A user wants to customize the feedback collection message to better suit different campaigns and audiences.

Given the feedback collection feature, when the user navigates to the customization section, then the user should be able to edit the message template for feedback requests, and save the changes without encountering errors.

Custom Workflow Automation

User Story: As a sales manager, I want to customize the follow-up workflow so that I can automate processes that cater to my specific customer engagement strategies.
Description: Custom Workflow Automation offers users the ability to design and implement personalized workflows for follow-up processes tailored to their specific business needs. This requirement empowers users to create rules and triggers for automated follow-ups based on lead interactions, segmentation, and engagement levels. The implementation will enhance the overall efficiency of the follow-up process by reducing manual effort and ensuring timely communications. The expected benefit is a streamlined sales process that aligns with user sales strategies, increasing overall effectiveness.
Acceptance Criteria: User creates a custom workflow for follow-up emails based on lead interactions after attending a webinar.

Given the user has access to the Custom Workflow Automation tool, when they design a workflow that triggers a follow-up email within 24 hours of lead interaction post-webinar, then the system should automatically send the email at the specified time when the condition is met.

User wants to create a follow-up reminder for leads who have viewed a product demo but have not yet made a purchase.

Given the user is in the Custom Workflow Automation interface, when they set a rule that triggers a follow-up reminder for leads who viewed a demo but haven’t purchased within 7 days, then the system should generate a reminder notification for the user 7 days after the demo viewing event occurs.

User needs to segment their leads based on engagement levels and apply different follow-up strategies.

Given the user has defined engagement levels in the system, when they create a custom workflow that applies different follow-ups based on high/medium/low engagement, then the correct follow-up strategy should be activated automatically according to the defined engagement level of each lead.

User wants to ensure that follow-up messages are personalized according to user preferences and interaction history.

Given the user is setting up automated follow-ups, when they include dynamic fields that pull lead-specific data, then the system should generate personalized messages that include the lead's name and their last interaction details in the follow-up emails.

User requires the ability to review and modify existing workflows to improve efficiency.

Given the user has previously created workflows, when they access the workflow management section, then they should be able to view, edit, and save changes to existing workflows without losing previous configurations.

User wants to measure the effectiveness of their custom workflows in terms of lead engagement.

Given the user has automated follow-up workflows in place, when they analyze the performance data after 30 days, then the system should provide metrics on lead engagement levels, response rates, and conversion rates for each workflow.

Predictive Engagement Recommendations

Predictive Engagement Recommendations use advanced AI algorithms to forecast audience behavior and suggest the best engagement strategies. This feature empowers users to proactively adjust their marketing tactics to meet anticipated audience needs, driving higher levels of interaction.

Requirements

Real-time Data Analysis

User Story: As a marketer, I want to receive real-time insights on audience engagement so that I can quickly adjust my marketing strategies to align with current trends and maximize interactions.
Description: The Predictive Engagement Recommendations feature must provide real-time data analysis capabilities to evaluate audience behavior patterns and trends. This functionality is essential for allowing the users to receive up-to-date recommendations based on current engagement metrics, ensuring that marketing strategies are responsive to the latest audience interactions. It integrates seamlessly with SalesMap AI’s analytics dashboard, making data-driven decisions easier and more immediate. The ultimate objective is to enhance user engagement through timely and relevant interactions, thereby improving conversion rates and overall effectiveness of campaigns.
Acceptance Criteria: User accesses the SalesMap AI analytics dashboard to monitor current audience engagement metrics and engagement behaviors in real-time.

Given the user is logged into SalesMap AI, when they navigate to the analytics dashboard, then they should see real-time updates on engagement metrics, including likes, shares, and comments.

User receives predictive engagement recommendations based on the latest audience behavior data during a marketing campaign.

Given the user is running a marketing campaign, when they view the predictive engagement recommendations section, then they should see suggestions that are dynamically adjusted to reflect the latest audience interaction data.

User exports real-time audience behavior data for further analysis or reporting purposes.

Given the user is on the analytics dashboard, when they select the Export Data option, then they should be able to download a CSV file containing real-time audience behavior metrics without errors.

User receives an alert about significant changes in audience behavior that warrants immediate action.

Given the user has set up custom alerts, when there is a notable change in audience engagement (e.g., a drop in interactions by more than 20%), then the user should receive an immediate notification via email and in-app alerts.

User views historical trends of audience engagement alongside real-time data on the analytics dashboard.

Given the user is on the analytics dashboard, when they toggle the view to 'Historical Trends', then they should see a comprehensive line graph showing audience engagement metrics over the past 30 days alongside current real-time metrics.

Users collaborate on predictive engagement strategies by sharing real-time insights with team members.

Given the user is viewing real-time data, when they click on the 'Share Insights' button, then they should be able to select team members and share a link to the live dashboard securely.

Customizable Engagement Strategies

User Story: As a sales manager, I want to customize engagement strategies so that the recommendations align with my business objectives and resonate with my target audience.
Description: The requirement for customizable engagement strategies allows users to tailor the AI-generated recommendations according to their specific business needs and audience characteristics. This feature enables users to input their preferences and business goals, which the AI will consider when providing predictive recommendations. By offering customization, the feature ensures that the engagement strategies are relevant and effective for each unique business scenario, thus enhancing user satisfaction and campaign success rates. The customizable options should include setting parameters like target demographics, preferred communication channels, and desired outcomes.
Acceptance Criteria: User customizes engagement strategies based on their unique business parameters and preferences.

Given a user selects the 'Customizable Engagement Strategies' option, when they input their business demographics, communication preferences, and goals, then the AI should provide tailored engagement recommendations that reflect these inputs.

User modifies their engagement strategy parameters and saves the changes successfully.

Given a user has inputted initial customization settings, when they update the parameters and click 'Save', then the platform should confirm that the settings have been saved and the user is directed to the updated recommendations page.

User receives engagement recommendations after customizing their settings.

Given a user has successfully defined their engagement strategy parameters, when they click on the 'Get Recommendations' button, then the system should display a list of tailored engagement strategies based on the input parameters without errors.

User can review previous engagement strategies to understand effectiveness and make adjustments.

Given a user has accessed the 'History of Engagement Strategies' section, when they view the previous strategies, then the system should present a clear comparison of effectiveness metrics for those strategies and allow modifications for future use.

User utilizes metrics from previous campaigns to inform their customization options.

Given a user accesses the 'Customization Dashboard', when they review campaign metrics and select relevant data points, then the AI should suggest updated engagement parameters based on this historical data.

User tests the effectiveness of customized engagement strategies through A/B testing.

Given a user implements a customized engagement strategy, when they initiate an A/B test with a defined duration, then the system should be able to report on the performance of each variant after the testing period ends, showcasing which strategy was more effective in achieving user-defined goals.

Integration with CRM Systems

User Story: As a business owner, I want Predictive Engagement Recommendations to integrate with my existing CRM so that I can leverage my customer data without additional manual input and enhance recommendation accuracy.
Description: This requirement focuses on the integration of Predictive Engagement Recommendations with various CRM systems utilized by clients. Seamless integration is vital to ensure that user data and engagement metrics are pulled in automatically, allowing the AI to generate context-aware recommendations. This will simplify user workflows by reducing manual data entry and ensuring that the recommendations are based on the most relevant and updated customer information. The integration process must be straightforward, with support for major CRM platforms to facilitate broad application among users.
Acceptance Criteria: SalesMap AI user integrates the Predictive Engagement Recommendations feature with their existing CRM system for the first time to enhance their marketing strategies and streamline engagement efforts.

Given the user has administrator access, when they follow the integration steps provided, then the CRM system should successfully connect to SalesMap AI without errors and display the integration confirmation message.

A marketing manager wants to view personalized engagement recommendations based on their CRM data after completing the integration.

Given the integration is complete, when the user accesses the Predictive Engagement Recommendations dashboard, then the system should display context-aware suggestions based on live CRM data without delays.

A user attempts to engage with a segmented audience using the recommended strategies generated from synced CRM data.

Given the marketing manager selects a specific audience segment, when they apply the generated recommendations, then the system should track and report engagement metrics automatically in real time, displaying a clear increase in interaction rates.

The system processes user feedback on the Predictive Engagement Recommendations to refine its algorithms post-integration.

Given a user provides feedback on the effectiveness of recommendations, when the feedback is submitted, then the system should acknowledge receipt and ensure algorithms are updated within one business cycle based on aggregate feedback.

The marketing team reviews the integration logs to troubleshoot any potential issues after initial setup.

Given the user has access to integration logs, when they review the logs for the past month, then the logs should accurately reflect all successful and failed integration attempts with corresponding timestamps and error messages when applicable.

A user requires assistance while setting up integration with a supported CRM system and seeks help from the support team.

Given the user contacts support for assistance, when they inquire about the integration process, then the support team should provide a response within 24 hours that resolves the user's query effectively.

User-Friendly Interface for Recommendations

User Story: As a user of SalesMap AI, I want a user-friendly interface for viewing engagement recommendations so that I can easily understand and act on the insights provided without confusion.
Description: The user-friendly interface requirement emphasizes creating an accessible and intuitive display for the predictive recommendations. The interface should allow users to easily navigate and access the engagement strategies suggested by the AI. Key elements should include graphical representations of data, straightforward explanations of recommendations, and actionable next steps. Enhancing user experience through an intuitive design is crucial for ensuring that users effectively utilize the recommendations to improve engagement outcomes.
Acceptance Criteria: User navigates to the Predictive Engagement Recommendations section of the SalesMap AI platform after logging in to access the suggestions for their marketing strategy.

Given the user is logged into the SalesMap AI platform, when they click on the 'Predictive Engagement Recommendations' tab, then they should see a dashboard displaying AI-generated recommendations with clear headings, graphs, and actionable insights, all within 3 seconds.

A user wants to understand how to implement an engagement strategy suggested by the AI on the dashboard.

Given a user is viewing an engagement strategy recommendation, when they hover over or click on the recommendation, then a tooltip or sidebar should appear providing a straightforward explanation of the engagement strategy and clear next steps within 2 seconds.

The user reviews the graphical representation of past engagement metrics to assess the effectiveness of past strategies and the predicted recommendations.

Given the user is viewing the graphical representation of data in the Predictive Engagement Recommendations section, when they analyze the data points, then the graphical representation should be interactive, allowing users to filter, zoom, and hover for more details, with no delays in rendering.

A user wants to customize the predictions to fit their specific audience segments for better-targeted engagement strategies.

Given the user is on the Predictive Engagement Recommendations dashboard, when they select different audience segments using a filter option, then the recommendations should dynamically update to reflect relevant strategies for the selected segments within 3 seconds.

The user wants to track the recommendations provided over time to evaluate their impact on engagement metrics.

Given the user navigates to a 'History' section within the Predictive Engagement Recommendations, when they access this history, then they should be able to view a chronological list of past recommendations along with corresponding engagement results, all loaded within 5 seconds.

A user is utilizing the 'Export Recommendations' feature to share suggested strategies with their team.

Given the user is on the Predictive Engagement Recommendations page, when they select the 'Export Recommendations' feature, then a downloadable file (CSV or PDF) should be generated containing all current recommendations and insights, completed within 5 seconds.

Feedback Loop Mechanism

User Story: As a marketer, I want to give feedback on engagement recommendations so that the AI can learn from my experiences and refine its future suggestions, making them more applicable to my strategies.
Description: The feedback loop mechanism allows users to provide feedback on the recommended engagement strategies to improve the AI’s predictive capabilities over time. By collecting user input on whether the recommendations led to desired outcomes or not, the system can learn and adapt to provide more accurate and relevant suggestions in the future. This two-way interaction enhances user involvement and trust in the AI system, and it will ultimately lead to better personalization of recommendations as the model learns from real-world application and feedback.
Acceptance Criteria: User provides feedback on engagement recommendations after a marketing campaign.

Given a user has received outreach recommendations, when they implement the suggestions and provide feedback on their effectiveness, then the feedback should be logged accurately in the system.

User reviews the effectiveness of past engagement strategies via the dashboard.

Given the user accesses the dashboard, when they view the feedback results from previous campaigns, then the user should see a clear report summarizing the outcomes linked to each recommendation.

Machine learning model updates based on user feedback.

Given that users provide ongoing feedback on engagement recommendations, when this feedback is processed by the AI system, then the model's predictive capabilities should improve and reflect the feedback in future recommendations.

User receives personalized engagement recommendations based on feedback input.

Given the feedback from users is utilized to recalibrate recommendations, when a user requests new engagement strategies, then the system should present updated recommendations that incorporate past feedback.

User is notified of enhancements made to the recommendations based on feedback submitted.

Given the user has provided feedback regarding engagement strategies, when the system updates its algorithms, then the user should receive a notification highlighting the changes and improvements made.

User analyzes the correlation between feedback and recommendation success rates.

Given a user has sufficient feedback data and outcome metrics, when they analyze this data in the reporting feature, then they should be able to view statistical insights into how feedback has influenced recommendation effectiveness.

Multi-Channel Engagement Insights

User Story: As a communications director, I want to get multi-channel engagement insights so that I can implement a cohesive and effective marketing strategy across different platforms, maximizing audience touchpoints.
Description: The multi-channel engagement insights requirement enables users to receive recommendations that consider data from various communication channels such as email, social media, and direct messaging. By analyzing interactions across all platforms, the AI can suggest comprehensive engagement strategies that effectively utilize each channel. This holistic approach ensures that users can reach their audience where they are most likely to engage, maximizing the effectiveness of their campaigns. Integration of data from multiple channels is crucial for this function to ensure comprehensive analysis and insights.
Acceptance Criteria: Users can view engagement recommendations based on email and social media data.

Given that a user is logged into SalesMap AI and has integrated both email and social media platforms, When the user navigates to the Predictive Engagement Recommendations dashboard, Then the system should display tailored recommendations that include at least three suggested strategies utilizing both channels.

Users receive notifications for new engagement strategies in real-time.

Given that the user has enabled notifications for Predictive Engagement Recommendations, When the AI identifies a new and relevant engagement strategy based on recent behavior trends, Then the user receives a real-time notification detailing the new strategy and its expected impact.

Users can filter engagement insights by communication channel effectiveness.

Given that a user is reviewing multi-channel engagement insights, When the user applies filters based on channel effectiveness, Then the displayed recommendations should reflect only those channels that have been effective in previous campaigns, as per the AI analysis.

Users can compare previous engagement strategies to recommend new strategies.

Given that a user has a history of previous engagement strategies recorded in SalesMap AI, When the user requests a comparison report, Then the system should generate a side-by-side comparison of past strategies against recommended new strategies, highlighting key performance indicators (KPIs).

Users can customize their criteria for engagement recommendations.

Given that a user is in the settings of Predictive Engagement Recommendations, When the user adjusts their preferences for engagement parameters (e.g., audience age, location, engagement type), Then the AI should adapt the recommendations accordingly, reflecting the new criteria.

Users receive a summary of engagement recommendation outcomes.

Given that a user has implemented engagement recommendations from SalesMap AI, When the user accesses the outcome report for the recent campaign, Then the report should include metrics such as open rates, response rates, and conversions, comparing them to previous campaigns without recommendations.

Users can seamlessly integrate additional messaging platforms into their insights.

Given that a user is on the multi-channel integration setup page, When the user adds a new messaging platform (e.g., WhatsApp or SMS), Then the system should successfully synchronize the data and update the engagement recommendations to include insights from the newly integrated channel within 24 hours.

Personalized Learning Pathways

This feature creates tailored onboarding plans for each user based on their role and experience level. By identifying individual user needs, it ensures that users receive the most relevant training and resources, leading to a quicker understanding of SalesMap AI's functionalities.

Requirements

Role-Based Training Modules

User Story: As a new sales representative, I want to have access to a training module designed specifically for my role so that I can quickly learn how to effectively use SalesMap AI’s features relevant to my job.
Description: This requirement involves developing distinct training modules tailored to different user roles (e.g., sales representative, manager, admin) within SalesMap AI. Each module will focus on the specific tools and functionalities that are most relevant to the user's responsibilities, thereby providing a targeted learning experience. By ensuring that users engage with content pertinent to their roles, this requirement will enhance user engagement and competency, leading to improved productivity and faster adoption of the SalesMap AI platform.
Acceptance Criteria: User Role-Specific Training Module Access

Given a user logs in as a sales representative, when they navigate to the training section, then they should only see the training modules relevant to a sales representative role.

Training Module Completion Tracking

Given a user completes a training module, when they access their profile, then their completion status should reflect the successful completion of that module.

Feedback Mechanism for Training Modules

Given a user finishes a training module, when they provide feedback, then the feedback should be recorded and be retrievable for review by the training administrator.

Progression Through Training Levels

Given a user has completed the basic training module, when they attempt to access the advanced module, then they should be granted access based on their completed training prerequisites.

Role-Specific Resource Recommendations

Given a user is identified as a manager, when they access the resource library, then they should receive recommended resources tailored to managerial responsibilities.

User Satisfaction with Training Modules

Given a user has completed a training module, when they are surveyed for satisfaction, then the feedback rating should be at least 80% positive for the module to be considered effective.

Skill Assessment Quizzes

User Story: As a user who has completed my training module, I want to take a quiz to assess my understanding, so I can identify areas where I need additional practice or guidance.
Description: The implementation of skill assessment quizzes is required to evaluate the knowledge and understanding of users after they complete the training modules. These quizzes will allow users to test their comprehension of the content, helping to identify knowledge gaps that may need addressing. By providing instant feedback and personalized suggestions for further training, this feature will ensure that users achieve a competent understanding of the platform, ultimately leading to higher performance and user satisfaction.
Acceptance Criteria: As a user completing a training module in SalesMap AI, I take the skill assessment quiz to evaluate my understanding of the material covered in the module.

Given that I have completed the training module, when I access the skill assessment quiz, then I should be able to take the quiz without errors and see a completion status upon finishing.

As a user who has just completed a skill assessment quiz, I expect to receive immediate feedback on my performance to understand my knowledge level.

Given that I have finished the skill assessment quiz, when the results are displayed, then I should see my score, the correct answers, and explanations for any incorrect answers within 5 seconds.

As a user who scores below 70% on a skill assessment quiz, I want to receive personalized training recommendations for further learning opportunities.

Given that I completed the quiz and scored below 70%, when I review my quiz results, then I should receive at least three recommended training resources tailored to my knowledge gaps.

As a sales manager reviewing the performance of my team, I want to analyze the quiz results to identify knowledge gaps across team members.

Given that all team members have completed skill assessment quizzes, when I access the reporting feature, then I should be able to view aggregated scores and identify specific areas where team members are struggling.

As a user taking a skill assessment quiz, I want to be able to retake the quiz multiple times if I'm unsatisfied with my score.

Given that I have taken a skill assessment quiz, when I choose to retake the quiz, then I should be allowed to retake the quiz up to three times while tracking my best score.

As a developer, I want to ensure that the skill assessment quiz is accessible to all users, including those with disabilities.

Given that I am testing the skill assessment quiz, when I check for accessibility compliance, then the quiz should meet WCAG 2.1 AA standards for all users.

Adaptive Learning Recommendations

User Story: As a user who has completed several modules, I want personalized learning recommendations so that I can continue to improve my skills efficiently and effectively.
Description: This requirement focuses on creating a system of adaptive learning recommendations based on the users' performance in training modules and quizzes. By leveraging AI, the platform can analyze user data and suggest further resources or training modules that align with their learning style and progress. This personalized approach ensures that each user receives the most effective learning pathway, enhancing engagement and retention, while providing the support needed to master SalesMap AI functionalities.
Acceptance Criteria: User Interaction with Adaptive Learning System

Given a user is logged into SalesMap AI, when they complete a training module, then the system should analyze their performance and suggest at least two relevant follow-up resources based on their learning style and quiz results.

Personalization Accuracy in Recommendations

Given a user has different performance levels across multiple training modules, when the adaptive learning recommendations are generated, then the suggestions provided should match at least 85% of the user's proficiency needs as gauged by their previous quiz scores and training engagement.

User Feedback on Recommendations

Given a user has received adaptive learning recommendations, when they review these suggestions, then at least 75% of users should indicate that the recommendations were relevant and useful in a follow-up survey post-module completion.

Adaptive Learning Algorithm Performance

Given historical data on user performance, when analyzing the effectiveness of the adaptive learning recommendations, then at least 70% of users who engaged with suggested resources should show improvement in their performance in future training modules.

Integration with User Profiles

Given a user profile is created, when the adaptive learning system is applied, then the system should update the user’s profile with the history of recommendations and their outcomes to improve future suggestions within a week of module completion.

Dashboard Insights on Learning Pathways

Given the adaptive learning recommendations are in use, when the dashboard is accessed by an administrator, then there should be clear analytics on user engagement and success rates, with visual representations identifying trends and areas for improvement.

Progress Tracking Dashboard

User Story: As a user, I want to see my progress on a dashboard so that I can track my completion of training modules and quizzes, ensuring I stay on schedule with my onboarding.
Description: A progress tracking dashboard is necessary for users to visualize their learning journey within SalesMap AI. This dashboard will display completed modules, quiz scores, and suggested next steps, allowing users to maintain a clear understanding of their training status and goals. By providing a comprehensive overview of their progress, users will feel motivated and can easily identify areas that require further attention or completion, promoting accountability and continuous improvement.
Acceptance Criteria: User views their progress dashboard after completing the initial onboarding modules for SalesMap AI.

Given a user has completed the initial onboarding modules, when they access the progress tracking dashboard, then they should see a summary of completed modules, individual quiz scores, and recommendations for next steps.

User receives real-time updates on their progress after finishing a quiz associated with the training modules.

Given a user finishes a quiz, when they view the progress tracking dashboard, then their quiz score should be accurately reflected, and the completed module count should increase accordingly.

User shares their progress with a team leader to discuss further training needs.

Given a user accesses their progress tracking dashboard, when they choose the option to share, then there should be an available link or email option that allows them to send their progress summary to specified recipients.

The user attempts to track their learning progress over a month to evaluate their training momentum.

Given it is the end of the month, when the user checks their progress dashboard, then they should be able to view a graphical representation of their learning activities, including a progress percentage and areas where they excelled or fell short.

User accesses the progress tracking dashboard on a mobile device while on the go.

Given the user is logged into SalesMap AI on a mobile device, when they navigate to the progress tracking dashboard, then the dashboard should display all relevant information in a responsive layout without loss of functionality.

User identifies gaps in their training based on the feedback provided in the progress dashboard.

Given a user reviews their progress statistics, when they identify modules with low completion rates or failed quizzes, then targeted suggestions for additional resources or re-attempting quizzes should be offered within the dashboard.

User receives notifications for suggested next steps based on their learning progress.

Given a user has completed a module, when they return to the progress tracking dashboard, then they should receive a notification highlighting the next recommended module or resources relevant to their training path.

Integration with User Profiles

User Story: As an administrator, I want the onboarding training pathways to be linked to user profiles so that each user receives a customized training experience based on their individual background and role.
Description: The integration of personalized learning pathways with existing user profiles in SalesMap AI is crucial for a streamlined onboarding experience. This requirement entails connecting user data (background, experience level, and role) with the training pathway feature, enabling the system to automatically curate and recommend relevant learning modules. Such integration not only personalizes the experience but also simplifies access to training resources based on user specifications, making the onboarding process more efficient and user-friendly.
Acceptance Criteria: User Profile Integration with Learning Pathways for New User Onboarding

Given a new user with a defined role and experience level, when their profile is created in SalesMap AI, then the system should automatically generate a personalized learning pathway that includes relevant training modules and resources appropriate for their role and experience level.

Updating Learning Pathways Based on User Profile Changes

Given an existing user who updates their profile to reflect a change in role or experience level, when they save their changes, then the system should automatically regenerate their personalized learning pathway to include updated training modules that reflect the new role or level.

User Access to Tailored Learning Resources

Given a user who is logged into SalesMap AI, when they navigate to the training section, then they should see a curated list of learning modules that align with their personalized learning pathway, ensuring easy access to relevant resources.

Real-time Feedback on Learning Pathway Effectiveness

Given a user who has completed a training module from their personalized learning pathway, when they provide feedback on the module, then the system should capture and analyze this feedback to improve future learning pathways and resources.

Ensuring Consistency in Learning Pathway Recommendations Across Devices

Given a user accesses SalesMap AI on different devices (desktop, tablet, smartphone), when they view their personalized learning pathway, then the recommendations should remain consistent across all devices to ensure a seamless learning experience.

Performance Monitoring of Learning Pathways

Given the implementation of personalized learning pathways, when a user completes their training, then the system should track the time taken and success rates, generating performance reports that can be reviewed by administrators to assess the training's effectiveness.

Interactive Walkthroughs

Providing step-by-step interactive guidance on key features, this functionality makes it easy for users to explore SalesMap AI. Users can follow prompts that demonstrate how to perform essential tasks, enhancing their confidence and proficiency in using the platform.

Requirements

Step-by-Step Guidance

User Story: As a new user, I want an interactive walkthrough that explains how to use important features so that I can quickly become proficient in navigating and utilizing SalesMap AI effectively.
Description: The Step-by-Step Guidance requirement involves creating an interactive walkthrough feature that guides users through key functionalities of SalesMap AI. This will include visual cues, tooltips, and prompts that help users understand how to perform essential tasks within the platform. By breaking down complex processes into manageable steps, this feature aims to reduce the learning curve for new users, increase user engagement, and enhance overall platform adoption. Furthermore, it will allow users to revisit the walkthroughs at any time, ensuring that they have the necessary resources for ongoing learning and usage, ultimately leading to increased user satisfaction and retention.
Acceptance Criteria: User initiates the interactive walkthrough from the dashboard after logging into SalesMap AI.

Given the user is on the SalesMap AI dashboard, when they click on the 'Start Walkthrough' button, then they should be presented with a series of step-by-step prompts that guide them through the platform's key features.

The user reaches a point in the walkthrough where they need to input data into a form.

Given the user is following the walkthrough and is prompted to input data, when they enter valid information and click 'Next', then the walkthrough should accept the input and proceed to the next step.

The user completes the interactive walkthrough and wishes to review the content again at a later time.

Given the user has completed the walkthrough, when they navigate to the 'Walkthroughs' section of their profile, then they should see an option to replay the completed walkthrough with all previous prompts and guidance available.

The user encounters a tooltip during the walkthrough that provides additional information.

Given the user is in the middle of a walkthrough, when they hover over a tooltip icon, then they should see a detailed explanation that enhances their understanding of the feature being demonstrated.

The user attempts to skip a step during the interactive walkthrough.

Given the user is engaged in a walkthrough, when they click on the 'Skip Step' option, then they should be able to bypass the current step and continue to the next part of the walkthrough without losing overall progress.

The user receives feedback after completing the interactive walkthrough.

Given the user has finished the walkthrough, when they reach the completion screen, then they should be prompted to provide feedback on the walkthrough experience to help improve future iterations.

Progress Tracking

User Story: As a new user, I want to see my progress in the interactive walkthroughs so that I can understand what I've completed and what still needs to be learned.
Description: The Progress Tracking requirement is essential for monitoring users' advancement through the interactive walkthroughs. This feature will highlight completed steps and provide users with visual indicators of where they are in the tutorial process. By offering this clarity, users will feel a sense of accomplishment and be motivated to continue learning. Additionally, it enables our system to analyze usage patterns and identify common drop-off points, informing future improvements to the walkthrough content. This aligns with the overall goal of enhancing user experience and ensuring a smoother onboarding process.
Acceptance Criteria: User interacts with the interactive walkthrough to learn about key features of SalesMap AI.

Given a user is on the walkthrough page, when they complete a step, then that step is visually marked as completed and the next step is highlighted.

User accesses the Progress Tracking feature during their first use of the interactive walkthrough.

Given a user starts the walkthrough, when they view their progress, then they should see a progress bar displaying the percentage of steps completed.

User finishes the interactive walkthrough and wants to review what they have learned.

Given a user completes all steps, when they access the summary section, then they should see a recap of all completed steps and their key takeaways.

User encounters difficulties and exits the walkthrough prematurely.

Given a user exits the walkthrough before completion, when they return, then they should be able to resume from the last completed step rather than starting over.

System administrators want to analyze user progress and identify drop-off points for improvements.

Given data on user progress, when the administrators view analytics reports, then they should be able to see the completion rates for each step and identify common drop-off points.

User has an issue with an interactive walkthrough step and needs assistance.

Given a user is viewing a specific step in the walkthrough, when they click on the help icon, then a contextual help section should open with relevant guidance for that step.

Feedback Collection Mechanism

User Story: As a user, I want to provide feedback on the interactive walkthroughs I complete so that my insights can help improve the guidance for future users.
Description: The Feedback Collection Mechanism requirement encompasses creating a system that allows users to provide feedback on the effectiveness of the interactive walkthroughs. This could include simple rating systems, comment boxes, or surveys that users can fill out after completing a walkthrough. Gathering user insights will help us identify areas of improvement, ensure the walkthrough content is relevant and effective, and make necessary updates based on real user experiences. This feature not only engages users but also helps in creating tailored content that can better serve their needs and enhance the user experience.
Acceptance Criteria: User completes an interactive walkthrough on the SalesMap AI platform and accesses the feedback mechanism immediately after finishing.

Given that a user completes an interactive walkthrough, when they are prompted to provide feedback, then they should see the feedback collection mechanism (rating system, comment box, or survey).

A user submits feedback through the provided comment box after an interactive walkthrough session.

Given that a user has accessed the feedback collection mechanism, when they submit their feedback via the comment box, then their feedback should be recorded successfully in the system without errors.

Users rate the effectiveness of the interactive walkthroughs using the rating system available after each session.

Given that a user is presented with a rating system at the end of an interactive walkthrough, when they select a rating and submit it, then the system should store the rating accurately and display a confirmation message.

An administrator reviews feedback collected from users on the interactive walkthroughs.

Given that feedback has been collected, when an administrator accesses the feedback management dashboard, then they should be able to view all submitted feedback categorized by type (ratings, comments, surveys).

Users receive a notification to participate in a follow-up survey a week after providing feedback on an interactive walkthrough.

Given that a user has submitted feedback, when a follow-up survey is sent, then the user should receive a notification via email with a link to participate in the survey.

The system analyzes collected feedback to identify common themes and suggestions for improvement.

Given that sufficient feedback has been collected, when the analysis process is initiated, then the system should generate a report highlighting common themes and actionable suggestions for enhancing the interactive walkthroughs.

The effectiveness of the interactive walkthroughs is evaluated based on user feedback collected over a month.

Given that feedback has been collected for a month, when an evaluation is conducted, then the report should indicate whether the overall user satisfaction rating exceeds 80%, signaling successful implementation.

Multilingual Support

User Story: As a non-English speaking user, I want the interactive walkthroughs to be available in my preferred language so that I can understand the platform without language barriers.
Description: The Multilingual Support requirement aims to include language options in the interactive walkthroughs to cater to a diverse user base. This will involve translating all text and instructional content in the walkthroughs into several key languages and allowing users to select their preferred language before starting the interactive tutorial. This feature is vital for enhancing accessibility and ensuring that non-native English speakers can effectively use SalesMap AI. By creating an inclusive environment, we aim to broaden our user base and improve global customer satisfaction, ultimately resulting in higher retention and engagement rates.
Acceptance Criteria: User selects their preferred language before starting the interactive tutorial.

Given the user is on the interactive walkthrough page, when they choose a language from the dropdown menu, then the walkthrough prompts and texts are displayed in the selected language.

All interactive walkthrough content is accurately translated into multiple languages.

Given the interactive walkthrough is accessed in Spanish, when the user reviews the instructions, then all text displayed reflects accurate and contextually appropriate Spanish translations.

Changing the language setting without reloading the page.

Given the user is viewing the walkthrough in English, when they change the language to French from the dropdown, then the content immediately updates to French without requiring a page refresh.

Language selections are remembered for returning users.

Given a user has selected Spanish for their walkthrough, when they return to the platform, then the interactive tutorial automatically displays in Spanish without needing to reselect.

Test the accessibility of the multilingual feature on various devices.

Given the user accesses the interactive walkthrough on a mobile device, when they select German as their language, then the interface should display all relevant content accurately in German regardless of device type.

Feedback on language clarity from users after using the walkthroughs.

Given that users complete the multilingual interactive walkthrough, when they are prompted for feedback, then at least 80% should report that the instructions are clear and easy to understand in their selected language.

Check for any untranslated text within the walkthroughs.

Given the user navigates through the Portuguese interactive walkthrough, when all text and prompts are reviewed, then there should be no instances of English text visible during the entire walkthrough.

Interactive Elements Integration

User Story: As a user, I want the interactive walkthroughs to include fun quizzes and videos so that I can learn in different ways and stay engaged during the onboarding process.
Description: The Interactive Elements Integration requirement focuses on incorporating various interactive elements within the walkthroughs, such as quizzes, clickable hotspots, and video tutorials. These elements aim to enhance user engagement and reinforce learning through active participation rather than passive observation. By integrating different formats of content, users will have a richer experience that caters to various learning styles, leading to better retention and understanding of SalesMap AI’s functionalities. This requirement is crucial for maximizing the effectiveness of the interactive walkthroughs and ensuring users fully grasp the tools available to them.
Acceptance Criteria: Interactive Quiz Functionality

Given the user is in an interactive walkthrough, when they complete a quiz, then their score should be recorded, and feedback should be provided instantly based on their answers, ensuring at least 80% of users can recall key features after the quiz.

Clickable Hotspots Engagement

Given the user is navigating through an interactive walkthrough, when they click on a hotspot, then relevant information or a tool-tip should display within 2 seconds, and at least 75% of users should report that the information was helpful and improved their learning experience.

Video Tutorial Accessibility

Given the user is participating in an interactive walkthrough, when they choose to watch a video tutorial, then the video should start within 3 seconds without buffering, and at least 70% of users should complete the video to be considered successfully implemented.

Alternate Learning Styles Support

Given that the interactive walkthrough incorporates multiple learning formats, when users are surveyed post-walkthrough, then at least 85% should agree that the combination of quizzes, hotspots, and videos accommodated their personal learning style effectively.

User Progress Tracking

Given that users engage with interactive elements, when they complete a walkthrough, then their progress should be recorded in the CRM system automatically, ensuring data accuracy with a 95% match rate against their activity logs.

Feedback Collection Mechanism

Given that users finish an interactive walkthrough, when they are prompted to provide feedback, then a feedback form should be submitted by at least 60% of users, and 80% of the feedback should indicate that the interactive elements enhanced their understanding of SalesMap AI functionalities.

Mobile Responsiveness of Interactive Elements

Given the user accesses the interactive walkthrough on a mobile device, when they interact with any element, then the elements should function seamlessly without layout issues, ensuring that at least 90% of users rate the mobile experience as 'satisfactory' or 'excellent'.

Goal-Oriented Progress Tracking

Users can set personal goals within the onboarding assistant, which allows them to track their progress and achievements throughout the onboarding journey. This feature motivates users by visualizing their advancements and helping them stay on course.

Requirements

Goal Setting Interface

User Story: As a new user, I want to set personal goals during onboarding so that I can visualize my progress and stay motivated throughout the process.
Description: The Goal Setting Interface allows users to create and monitor personal goals within the SalesMap AI onboarding process. This feature will enable users to define specific, measurable objectives that they aim to achieve during their onboarding journey. It integrates seamlessly with the onboarding assistant, prompting users to set relevant goals that align with their sales aspirations. By providing a structured approach to goal setting, the interface enhances user engagement and commitment to the onboarding process. Furthermore, it incorporates visual progress indicators, enabling users to easily track their accomplishments and adjust their strategies if necessary, ultimately fostering a sense of ownership and motivation throughout the onboarding experience.
Acceptance Criteria: User successfully sets a new personal goal in the onboarding assistant.

Given that the user is on the goal setting page, when they fill in the goal title, description, and due date, then the new goal should be saved and displayed in the user's goal list with the correct details.

User views progress indicators for their personal goals.

Given that the user has set personal goals, when they navigate to the progress tracking section, then they should see a visual representation of their progress for each goal, including completion percentage and deadline status.

User receives notifications for goal-related reminders.

Given that the user has set a goal with a due date, when the due date approaches, then the user should receive a notification reminding them of the goal and its deadline.

User can edit an existing goal.

Given that the user wants to modify a goal, when they select an existing goal, update the details, and save those changes, then the updated goal should reflect the new information in the user's goal list.

User deletes a personal goal they no longer wish to track.

Given that the user has set personal goals, when they choose to delete a specific goal and confirm the action, then that goal should be removed from the user's goal list and progress indicators.

User can see a summary of all their achieved goals.

Given that the user has completed several goals, when they navigate to the summary section, then they should see a list of all goals marked as complete along with their completion dates.

User receives suggestions for setting new goals based on their progress.

Given that the user has set and is tracking goals, when they have reached a milestone or completed a goal, then the system should offer personalized suggestions for new goals to encourage continued engagement.

Progress Visualization Dashboard

User Story: As a new user, I want to see a visual representation of my progress so that I can understand how I am performing against my goals.
Description: The Progress Visualization Dashboard displays real-time updates on a user’s progress towards their predefined goals during onboarding. This feature provides visual representations such as graphs and progress bars which indicate how much of their goal has been achieved. It integrates with the goal-setting functionality to pull in data related to user performance and milestones reached. By offering a clear, visual summary of achievements, the dashboard not only enhances user understanding of their progress but also encourages continued engagement with the platform. Moreover, it helps users identify areas for improvement, leading to more focused efforts in their sales training and application of SalesMap AI tools.
Acceptance Criteria: User initiates the onboarding process and sets personal sales goals using the onboarding assistant.

Given the user has accessed the onboarding assistant, when they input their sales goals, then the goals should be saved and displayed in the Progress Visualization Dashboard as part of the user's profile.

User navigates to the Progress Visualization Dashboard to view their goal achievements following an initial setup.

Given the user has set goals, when they view the Progress Visualization Dashboard, then they should see a graphical representation of their progress with accurate metrics updated in real-time.

User completes various training modules related to SalesMap AI tools and expects to see their advancement reflected in the dashboard.

Given the user has completed sales training modules, when they refresh the Progress Visualization Dashboard, then the progress bars should reflect the completion percentage and updated milestones achieved.

User wants to identify which areas of their sales approach need improvement based on their progress visualizations.

Given the user views their Progress Visualization Dashboard, when they click on a specific goal that has low progress, then a detailed breakdown of activities and suggestions for improvement should be displayed.

User expects to receive notifications for significant achievements tracked in the Progress Visualization Dashboard.

Given the user achieves a major milestone, when they check their notifications, then they should receive an alert acknowledging their achievement that links back to the Progress Visualization Dashboard.

Admin wants to verify that the Progress Visualization Dashboard integrates correctly with the goal-setting functionality.

Given the goal-setting functionality is updated, when changes are made, then the Progress Visualization Dashboard should reflect these changes immediately without discrepancies.

User wants to share their progress achievements with their sales team through the dashboard.

Given the user accesses the Progress Visualization Dashboard, when they select the share option, then the dashboard visual should be shared properly via email or collaboration tools without loss of data integrity.

Motivational Alerts and Notifications

User Story: As a user, I want to receive reminders and motivational messages related to my goals so that I can stay focused and on track during onboarding.
Description: The Motivational Alerts and Notifications feature sends users timely reminders and motivational messages based on their goal progress. This system integrates with the goal setting and progress tracking functionalities, providing alerts when certain milestones are reached or if there is a risk of falling behind set goals. The alerts can be customized according to user preferences, ensuring that notifications are engaging and relevant. By implementing this feature, SalesMap AI aims to maintain user motivation and focus, ultimately improving compliance and completion rates in the onboarding phase. It also helps to create a supportive environment, encouraging users to reach out if they need assistance with their goals.
Acceptance Criteria: User receives motivational alerts tailored to their set goals during the onboarding process.

Given a user has set personal goals and is using the onboarding assistant, when they reach a milestone, then the user should receive a motivational alert via their preferred notification method (e.g., email, app notification).

User can customize delivery preferences for motivational alerts and notifications.

Given a user has access to the alert customization settings, when they adjust their preferences for how and when they receive notifications, then the system should save and apply these preferences for future alerts.

User receives alerts when they are at risk of missing their goal deadlines.

Given a user has set personal goals with deadlines, when the deadline approaches and the user's progress is below expectations, then the user should receive an alert notifying them of the potential issue and suggesting actions to take.

User can view a history of all motivational alerts sent to them.

Given a user has completed multiple onboarding goals, when they access the alert history feature, then they should see a list of all previously sent motivational alerts along with timestamps.

User receives a summary alert after completing a major milestone in their onboarding.

Given a user has completed a major milestone in the onboarding process, when this event is recorded, then the user should receive a summary notification that acknowledges their achievement and encourages them to continue progressing.

System audits the effectiveness of sent notifications on user goal completion rates.

Given a set period for user engagement, when the system analyzes the goal completion rates, then it should report the correlation between motivational alert delivery and successful goal completion, providing insights for future improvements.

Achievements and Rewards System

User Story: As a user, I want to earn rewards for achieving my onboarding goals so that I feel recognized and motivated to complete the process.
Description: The Achievements and Rewards System incentivizes users to reach their onboarding goals by providing rewards for completing various milestones. This feature outlines a set of achievements that users can aim for, which are linked to their progress in the onboarding process. Users receive badges or other forms of recognition, which can foster a sense of achievement and further enhance motivation. The system emphasizes positive reinforcement and encourages users to engage more thoroughly with the content and tools provided by SalesMap AI. Through this feature, the onboarding experience not only becomes more engaging but also builds a rewarding environment that promotes continuous improvement and user retention.
Acceptance Criteria: User sets a personal onboarding goal for completing the tutorial module of SalesMap AI and wishes to track their progress through the Achievement and Rewards System.

Given a user has set a personal goal in the onboarding assistant, when they complete a milestone in the tutorial module, then they should receive a notification confirming their achievement and the corresponding reward.

User completes a specified number of onboarding tasks and earns an achievement badge, which is displayed in their user profile.

Given a user completes five onboarding tasks, when the tasks are marked as complete, then the user should automatically receive the 'Onboarding Champion' badge and it should be visible in their profile.

User wants to view their progress towards their onboarding goals through the dashboard of SalesMap AI.

Given a user accesses the dashboard, when they navigate to the progress tracking section, then their progress should be visually represented with a progress bar showing completed tasks against total tasks.

User wants to be rewarded for reaching a significant achievement, like completing all onboarding tasks ahead of the deadline.

Given a user has completed all onboarding tasks ahead of the designated timeline, when they finish the last task, then they should receive an email confirmation with details of their achievement and the reward they have earned.

A user engages with the Achievements and Rewards System and wants to see a list of available achievements they can strive for.

Given a user accesses the achievements section in SalesMap AI, when they request to view available achievements, then they should see a dynamically updated list of achievements with descriptions and progress indicators for each.

User completes their selected onboarding goals and accesses the rewards section to redeem their rewards.

Given a user has earned at least one reward through the completion of onboarding goals, when they go to the rewards section, then they should be able to view and select from a list of redeemable rewards and confirm their selection.

Feedback Loop Feature

User Story: As a user, I want to be able to provide feedback on my onboarding experience so that my suggestions can help improve the process for future users.
Description: The Feedback Loop Feature allows users to provide feedback on their onboarding experience and goal-setting process. This capability enables users to share insights about the challenges they face and the effectiveness of the tools and resources provided during onboarding. The feedback will be analyzed to continuously improve the onboarding experience and the goal-setting functionalities. This feature not only gives users a voice in shaping the platform but also helps SalesMap AI to maintain a high-quality user experience by addressing concerns and implementing enhancements based on real user input. As a result, this fosters a more user-centered development approach.
Acceptance Criteria: User provides feedback on their onboarding experience after completing the initial setup of SalesMap AI.

Given a user has finished the onboarding setup, when they access the feedback section, then they should be able to submit their feedback about their experience using a structured form.

User reviews their personal goal progress through the dashboard and decides to provide feedback on the goal-setting process.

Given a user is viewing their goal progress on the dashboard, when they click on the feedback option, then they should see a prompt to rate their goal-setting experience on a scale of 1 to 5 and provide comments.

Administrator reviews aggregate user feedback received through the Feedback Loop Feature.

Given multiple users have submitted their feedback, when the administrator pulls the feedback report, then the report should display categorized feedback by challenges faced and suggestions for improvement.

A user encounters a technical issue during the onboarding process and utilizes the Feedback Loop Feature to report it.

Given a user is experiencing a technical issue, when they submit their feedback, then they should receive a confirmation message that their feedback has been recorded and will be addressed.

User completes the onboarding process and reflects on their entire experience with goal tracking to provide comprehensive feedback.

Given a user has completed the onboarding process, when they access the feedback form, then they should be able to answer multiple questions regarding different aspects of the onboarding experience and submit that feedback successfully.

UI/UX team tests the functionality of the Feedback Loop Feature during an internal quality assurance process.

Given the development environment has the Feedback Loop Feature enabled, when testers use the feedback submission form, then they should see successful submission confirmations for various feedback types without errors.

User checks the status of their submitted feedback after a few days and wants to see if any changes have been made based on their input.

Given a user has submitted feedback, when they check the status of their feedback submission, then they should be able to view an update on whether their feedback was acknowledged, considered, or implemented.

Contextual Help Suggestions

As users navigate the platform, contextual help suggestions pop up, offering tips and resources relevant to the specific feature they are using. This immediate support reduces confusion and empowers users to explore and learn independently.

Requirements

Interactive Learning Tooltips

User Story: As a new user, I want to see contextual help suggestions as I navigate the platform so that I can learn how to use the features effectively and reduce my reliance on support resources.
Description: This requirement involves the implementation of interactive tooltips that provide real-time, contextual help as users navigate through various features of SalesMap AI. These tooltips will be designed to deliver succinct explanations or tips related to the specific feature currently in use, enhancing user understanding and engagement. By offering immediate assistance, these tooltips will minimize user confusion, reduce the learning curve, and foster a more intuitive and enjoyable user experience. Administrators will also have the ability to customize these tooltips to reflect specific company terminology or workflows, ensuring alignment with user needs and enhancing adoption rates.
Acceptance Criteria: User encounters a new feature within SalesMap AI and hovers over the feature for a brief moment, triggering the contextual help suggestion to appear.

Given the user is on a feature page, When the user hovers over the designated feature, Then an interactive tooltip appears within 2 seconds, providing a clear explanation of the feature and its functionalities.

An administrator accesses the tooltip customization settings to adjust the content of the contextual help suggestions for their team.

Given an administrator navigates to the tooltip customization settings, When they input custom text and save the changes, Then the updated tooltip should display the new text when users hover over the respective feature within 5 seconds.

A user utilizes the tooltips while trying to understand how to set up automated campaigns, requiring assistance through contextual help.

Given the user is viewing the automated campaign setup feature, When the user hovers over any icon associated with the campaign settings, Then the tooltip presents relevant tips within 2 seconds, enhancing user comprehension of the setup process.

A non-technical user is exploring the reporting feature and requires immediate help to interpret the data displayed.

Given the user is analyzing data on the reporting page, When the user clicks on a 'Help' icon near the data visuals, Then the tooltip provides a straightforward explanation of the dataset within 2 seconds, improving user engagement and understanding.

A user tests the tooltip functionality across different sections of SalesMap AI to ensure consistency and accessibility of contextual help.

Given the user navigates through various features of SalesMap AI, When the user interacts with the features, Then the tooltip appears consistently across all sections, maintaining a standardized format and response time of 2 seconds.

An administrator wishes to verify that the tooltips properly reflect the specific terminology used within their company to enhance user familiarity.

Given an administrator makes changes to tooltip content, When those changes are applied, Then the contextual help suggestions should use the company-specific terms correctly on the first hover display.

A user experiencing difficulty in using a feature reports that the tooltip did not load correctly as expected.

Given the user encounters a loading issue with a tooltip, When they refresh the page, Then the tooltip should appear without errors on the subsequent hover, ensuring reliable performance of interactive tooltips.

Integrated Video Tutorials

User Story: As a user, I want to access video tutorials directly within the platform so that I can visually learn how to utilize advanced functionalities without having to leave the application.
Description: This requirement focuses on integrating short video tutorials within the platform that can be accessed at any time by users. These videos will provide visual guidance on various functionalities and best practices related to using SalesMap AI. By offering multimedia support, users can better grasp complex features or workflows they may struggle with reading text alone. The incorporation of these tutorials will not only empower users to learn at their own pace but will also improve overall satisfaction and decrease the likelihood of error in using the platform's features. Video content will be easily updated and expanded to keep the learning resources current and relevant.
Acceptance Criteria: User accesses the platform and encounters the help feature while attempting to understand a specific sales analytics function.

Given that the user is on the sales analytics page, when they click the contextual help icon, then relevant video tutorials must display as suggestions that relate specifically to the sales analytics function being viewed.

A user is working on configuring automated campaign settings and needs assistance related to best practices for campaign setup.

Given that the user is on the automated campaign settings page, when they hover over the campaign setup options, then a contextual help suggestion should appear that includes a video tutorial on campaign best practices.

An administrator wants to update existing video tutorials within the platform to ensure the content is current and reflective of recent feature updates.

Given that the administrator is logged into the platform, when they navigate to the tutorial management section, then they should be able to upload new video content and replace outdated tutorials with updated ones seamlessly.

A user is viewing a tutorial on lead scoring and pauses the video to explore other platform features.

Given that the user is watching a lead scoring tutorial, when they pause the video, then a contextual prompt should offer links to related features that may utilize lead scoring information, enhancing learning opportunities.

During onboarding, a user is guided through using the platform for the first time and requires immediate assistance.

Given that the user is in the onboarding process, when they select a feature, then an introductory video tutorial specific to that feature should automatically play to provide guidance.

A user with limited accessibility options is trying to navigate video content within the platform.

Given that the user has enabled accessibility features, when they interact with video tutorials, then they should have access to captions and/or transcripts that accompany each video for better understanding.

User Feedback Mechanism

User Story: As a user, I want to provide feedback on the contextual help suggestions so that I can contribute to improving the support and resources available in the platform.
Description: This requirement seeks to develop a user feedback mechanism allowing users to provide input on the help suggestions they receive. Users will have the option to rate the helpfulness of contextual help suggestions and video tutorials, and their feedback will be collected anonymously. This feature will give SalesMap AI insights into the effectiveness of its help resources, identifying areas for improvement. Consequently, continuous enhancement of user support offerings can be ensured, fostering a more user-centered experience and leading to higher satisfaction and increased product loyalty.
Acceptance Criteria: User Evaluation of Help Suggestion Effectiveness

Given that a user is on the help suggestion popup, When they rate the suggestion on a scale of 1 to 5, Then the rating is logged successfully in the database with a timestamp and user ID, and the user receives a confirmation message that their feedback has been submitted.

Anonymous Feedback Collection

Given that a user has provided a rating for a help suggestion, When the user submits their feedback, Then the feedback is stored anonymously, ensuring no personal identifiers are recorded, and the system must prevent any correlation to the user's identity.

Feedback Summary Dashboard for Admins

Given that at least 10 feedback ratings have been collected for a specific help suggestion, When an admin accesses the feedback summary dashboard, Then the admin must see a summary report displaying the average rating, number of feedback entries, and suggestions for improvements based on user ratings.

Real-time Feedback Notifications for Help Resources

Given that a user submits feedback on a help suggestion, When the feedback is submitted, Then the help resource owner receives a notification in real-time alerting them about the new feedback entry, along with the details of the feedback.

User Interaction with Video Tutorials

Given a user is accessing a video tutorial linked to a help suggestion, When the user rates the effectiveness of that video tutorial, Then the rating should be successfully logged with the same criteria as the help suggestion, ensuring consistency in feedback collection.

User Experience Improvement Tracking

Given that feedback ratings have been collected over a period of time, When the product manager reviews the aggregated feedback, Then they must be able to identify trends in user satisfaction and areas needing improvement, reported in a comprehensive analytics format.

Feedback Analysis for Continuous Improvement

Given that feedback received has been analyzed, When the relevant stakeholders discuss the results, Then actionable recommendations for improving help suggestions and resources must be documented and scheduled for implementation in future updates.

Searchable Help Database

User Story: As a user, I want to search a help database for articles and tutorials so that I can quickly find answers to my questions without waiting for support.
Description: This requirement aims to create a searchable help database within SalesMap AI where users can easily find articles, FAQs, and tutorials related to the platform. This database will be accessible from the main dashboard, allowing users to type in queries and receive instant, relevant results. This self-service resource will enable users to resolve queries quickly without needing to wait for customer support. A well-indexed and frequently updated help database will also reduce the volume of support tickets, thereby allowing support staff to focus on more complex issues and improving overall efficiency.
Acceptance Criteria: User Searching for Help Articles on the Dashboard

Given that a user is on the main dashboard, when they enter a query related to a feature in the search bar, then they should receive a list of relevant articles and tutorials that match their query within 3 seconds.

User Accessing the Help Database from Different Devices

Given that a user accesses the SalesMap AI platform from a mobile device, when they navigate to the help database, then they should be able to view articles and FAQs formatted correctly for mobile display without functionality loss.

User Finding FAQs Related to Lead Scoring

Given that a user types 'lead scoring' into the search bar, when they submit the query, then they should see at least 3 relevant FAQs or articles displayed as search results in the help database.

User Updating Help Articles in the Database

Given that an admin user logs into the backend of the help database, when they update an article and save changes, then the changes should reflect immediately in the user-facing help database without errors.

User Receiving No Results for Unrelated Queries

Given that a user types a query that has no related articles in the help database, when they hit search, then they should receive a message indicating no results found along with suggestions for common help topics.

User Accessing Help Database during High Traffic

Given that multiple users are accessing the help database simultaneously, when any user performs a search, then the system should maintain response times of less than 5 seconds for all requests without any degradation in performance.

User Bookmarking Favorite Help Articles

Given that a user finds an article they want to reference later, when they click the 'bookmark' option on the article, then that article should be saved in their profile for easy access later on the dashboard.

Proactive User Engagement Alerts

User Story: As a regular user, I want to receive proactive notifications about new tutorials or help suggestions relevant to my usage so that I can stay updated and make the most out of the platform.
Description: This requirement involves setting up proactive engagement alerts that notify users of new help suggestions, tutorials, or features relevant to their activities within SalesMap AI. These alerts will be personalized based on user behavior and preferences, ensuring that users are informed about relevant resources without having to actively search for them. This feature aims to enhance user engagement, retention, and satisfaction by ensuring they are continuously supported with tools and resources tailored to their specific needs and usage patterns.
Acceptance Criteria: User receives a notification of new contextual help suggestions while navigating the sales dashboard.

Given the user is logged into SalesMap AI and is on the sales dashboard, when a new contextual help suggestion becomes available, then the user should receive a notification alerting them to the new suggestion within 5 seconds.

User customization of help alert preferences.

Given the user is navigating to the settings page, when they select notification preferences, then they should be able to customize the frequency and type of engagement alerts they receive, including options for 'immediate', 'daily digest', and 'none'.

User engagement with contextual help suggestions after receiving an alert.

Given the user receives a notification about a new help suggestion, when they click on the notification, then they should be directed to the relevant help resource and have the option to close the alert post-viewing within 2 clicks.

User feedback on the helpfulness of suggested resources.

Given the user has interacted with a contextual help suggestion, when prompted with a feedback form, then they should be able to rate the suggestion on a scale of 1 to 5 and leave comments, with all feedback being recorded for review.

Analytics tracking of user interaction with help suggestions.

Given the feature is live, when users interact with contextual help suggestions, then the system should automatically log user engagement metrics such as click-through rates and time spent on help resources for analysis.

User receives alerts tailored to their specific role and previous interactions.

Given the user has a defined role within the platform, when new features or tutorials are released, then the alerts sent to the user should reflect content relevant to their role and past activities, as verified by system logs.

System performance during alert notifications.

Given multiple users are active on the platform, when a new help suggestion is generated, then the alert notification system should deliver notifications within 5 seconds to at least 95% of users concurrently without performance degradation.

Multilingual Support for Help Content

User Story: As a non-English speaking user, I want to access help content in my native language so that I can effectively learn and use the platform.
Description: This requirement seeks to develop and implement multilingual support for all help content, including contextual help suggestions, video tutorials, and the help database. By offering assistance in multiple languages, SalesMap AI will cater to a broader user base and improve accessibility for non-English speaking users. This inclusive approach will enhance user satisfaction and engagement, as users feel more comfortable and supported in their native languages. Resources will be regularly reviewed and updated in all supported languages to maintain coherence and accuracy across the platform.
Acceptance Criteria: User interacts with the contextual help by selecting a feature in SalesMap AI to receive multilingual help suggestions.

Given a user has selected a feature in the platform, when they click on the help icon, then the contextual help suggestions should display in the user's selected language without any errors or missing translations.

A user navigates to the help database searching for documentation in their native language, looking for guidance on how to use a specific feature.

Given a user has set their preferred language in the settings, when they access the help database and search for documentation, then all relevant articles should be displayed in the user's selected language, with correct formatting and no placeholder text.

A user watches a video tutorial on feature usage and expects subtitles available in their preferred language.

Given a user is watching a video tutorial, when the video is played, then subtitles should appear in the user's selected language and be accurately synchronized with the video content.

An admin reviews and updates the help content in multiple languages to ensure consistency and accuracy across all translations.

Given the admin has edited the help content, when they submit the updated content for review, then all changes should reflect correctly in all supported languages without any discrepancies or errors.

A user provides feedback on the multilingual help suggestions they've interacted with while using the platform.

Given a user submits feedback about the contextual help suggestions, when the feedback is submitted, then it should be recorded and linked to the user's profile for future improvement analysis.

A non-English speaking user accesses contextual help for the first time on the platform to explore its features.

Given a non-English speaking user logs into the platform for the first time, when they hover over a feature, then the contextual help should appear in their default browser language if supported.

Resource Library Access

This feature provides users with instant access to a curated library of tutorials, videos, and FAQs tailored to their onboarding needs. Users can access valuable information at their fingertips, improving self-sufficiency and enhancing their learning experience.

Requirements

Resource Library Framework

User Story: As a new user of SalesMap AI, I want to access a centralized library of tutorials and FAQs so that I can learn how to use the platform effectively and independently without constant support from the sales team.
Description: The Resource Library Framework requirement involves creating a robust digital infrastructure within SalesMap AI that enables users to access a comprehensive library of tutorials, instructional videos, and frequently asked questions (FAQs) tailored for onboarding. This framework will integrate seamlessly with the existing platform, providing users with self-paced learning materials to enhance their product utilization effectively. By offering a structured repository of learning resources, this feature aims to improve user engagement and reduce dependency on customer support, ultimately contributing to a more fulfilling onboarding experience and promoting self-sufficiency among users.
Acceptance Criteria: User accesses the Resource Library to find a tutorial on lead scoring.

Given the user is logged into SalesMap AI, when they navigate to the Resource Library and search for 'lead scoring', then they should see relevant tutorials and videos listed in the results.

User watches a video tutorial from the Resource Library to learn about sales automation.

Given the user selects a video tutorial from the Resource Library, when they click play, then the video should start playing without buffering issues and have controls for pause, rewind, and volume adjustment.

User browses the Resource Library for FAQs related to common onboarding questions.

Given the user is in the Resource Library, when they click on the 'FAQs' section, then they should see a categorized list of common questions and their answers, easily accessible within 3 clicks.

User finds a specific tutorial using the search function in the Resource Library.

Given the user enters keywords related to 'predictive analytics' in the search bar, when they press enter, then the search results should display relevant tutorials ranked by relevance.

User provides feedback on the Resource Library's content.

Given the user has access to the Resource Library, when they complete a tutorial and click on 'Provide Feedback', then they should be able to submit feedback that gets recorded in the system without errors.

User receives personalized recommendations for Resource Library content based on their usage history.

Given the user has interacted with at least three tutorials, when they access the Resource Library, then they should see a 'Recommended for You' section displaying tailored content based on their previous interactions.

Search Functionality for Resources

User Story: As a user looking for specific tutorial content, I want to use a search function in the Resource Library so that I can easily find the information I need to resolve my issues without sifting through irrelevant content.
Description: This requirement emphasizes the necessity of implementing an advanced search functionality within the Resource Library, allowing users to quickly locate specific tutorials, videos, or FAQs. The search feature will leverage keyword recognition and filtering options to present the most relevant results, enhancing user experience by reducing the time spent searching for needed resources. This functionality is crucial for users looking to solve specific issues or learn targeted skills, thus fostering a more efficient and autonomous learning environment within the platform.
Acceptance Criteria: Searching for a specific tutorial on using sales pipelines.

Given the user is on the Resource Library page, when they enter 'sales pipeline tutorial' into the search bar, then the system should return relevant tutorials within 3 seconds that include the keywords 'sales', 'pipeline', and 'tutorial' in the title or description.

Filtering search results by content type.

Given the user has performed a search for 'email marketing', when they select the filter option for 'Videos', then the system should only display video resources related to 'email marketing'.

Accessing resource FAQs through the search function.

Given the user is viewing search results, when they click on a resource categorized as a FAQ, then the system should redirect them to the FAQ section with the item highlighted.

Search functionality for a new user during onboarding.

Given a new user is using the Resource Library for the first time, when they search for 'getting started', the system should suggest beginner resources prominently on the results page.

Displaying 'no results' message for non-existent searches.

Given the user enters a term that has no matching resources, when they click 'search', then the system should display a clear message indicating 'No resources found for your search.'

Autocomplete suggestions during search input.

Given the user begins typing in the search bar, when they enter the first few letters of a keyword, then the system should display a dropdown list of autocomplete suggestions that match their input, within 2 seconds.

User feedback on search relevancy.

Given the user has viewed the search results, when they click on the feedback option, then they should be able to rate the relevancy of the first five results on a scale of 1 to 5, and the system should save their feedback.

Resource Recommendation Engine

User Story: As a user of SalesMap AI, I want to receive personalized recommendations for tutorials and resources based on my usage patterns so that I can discover useful content that enhances my learning and improves my onboarding experience.
Description: The Resource Recommendation Engine requirement involves developing an intelligent system that curates and suggests relevant tutorials and videos based on a user’s activity, preferences, and onboarding progress. This feature will utilize machine learning algorithms to analyze user behavior and interaction patterns, enabling tailored learning experiences that evolve with each user. By presenting personalized resource recommendations, this system will enhance user satisfaction and promote deeper engagement with the platform, subsequently improving the overall learning effectiveness and user retention rates.
Acceptance Criteria: User Interaction with the Resource Recommendation Engine and Accessing Suggested Content

Given a user who is logged into the SalesMap AI platform, when the user interacts with the Resource Recommendation Engine, then the system should suggest at least three personalized resources based on their onboarding profile and previous interactions.

Evaluation of Resource Recommendation Relevance

Given a user who has received recommendations from the Resource Recommendation Engine, when the user reviews these recommendations, then at least 70% of users should find the suggestions relevant to their current onboarding needs in post-interaction surveys.

User Engagement with Recommended Resources

Given a user who accesses resources suggested by the Resource Recommendation Engine, when measuring user engagement metrics, then the user should demonstrate at least a 50% increase in resource engagement (e.g., time spent on tutorials and videos) compared to previous sessions without recommendations.

Adaptation of Recommendations Based on User Feedback

Given a user who has interacted with the resources provided by the Resource Recommendation Engine, when the user rates the resources as helpful or not helpful, then the recommendation algorithm should adjust future recommendations within the next session based on this feedback.

System Performance Under Load

Given multiple users accessing the Resource Recommendation Engine simultaneously, when a load test is conducted with 100 concurrent users, then the system should maintain a response time of less than 2 seconds for generating recommendations.

User Onboarding Progress Tracking Integration

Given a user who completes onboarding stages in the SalesMap AI platform, when the Resource Recommendation Engine updates based on the user's progress, then the suggestions should accurately reflect the latest stage of onboarding without delays.

Cross-Platform Compatibility of Resource Recommendations

Given that users may access the SalesMap AI platform from different devices (desktop, tablet, mobile), when a user logs in from any device, then the Resource Recommendation Engine should consistently provide the same relevant recommendations based on their profile regardless of the device used.

Feedback and Rating System for Resources

User Story: As a user of SalesMap AI, I want to leave feedback and ratings on the resources I use so that I can share my experience and help others in the community find the most effective tutorials and materials.
Description: This requirement specifies the need for a Feedback and Rating System within the Resource Library, allowing users to rate and provide feedback on tutorials and videos. Implementing this system will enable the collection of user insights, helping the team to refine the resource library continually. A feedback mechanism will empower users to share their opinions, improving the quality of resources over time, and promoting community engagement within the platform while giving new users a sense of trust in the materials provided.
Acceptance Criteria: User Ratings for Tutorials and Videos

Given a user accesses a tutorial or video in the Resource Library, when they complete viewing the content, then they should have the option to provide a rating from 1 to 5 stars and an associated text feedback within a 300 character limit.

Feedback Submission Confirmation

Given a user submits feedback after rating a resource, when the submission is successful, then the user should receive a confirmation message indicating their feedback has been recorded.

Average Rating Calculation

Given multiple users have rated the same tutorial or video, when the ratings are collected, then the system should calculate and display the average rating prominently on the resource page.

Feedback Display on Resource Page

Given a user views a tutorial or video in the Resource Library, when the resource has received feedback, then the most recent feedback should be displayed below the resource description.

User Feedback Retrieval

Given an admin user accesses the Resource Library dashboard, when they request feedback data for a specific resource, then they should receive a list of all feedback entries associated with that resource, including ratings and comments.

Feedback Filtering Options

Given a user views the Resource Library, when they want to see resources with specific average ratings, then they should have the option to filter the resources by rating ranges (1-5 stars).

Feedback Improvement Suggestions

Given users provide feedback indicating areas for improvement, when the feedback is analyzed by the admin, then actionable suggestions should be documented for resource enhancement planning.

Resource Accessibility Compliance

User Story: As a user who may have accessibility needs, I want the tutorials and resource materials to be compliant with accessibility standards so that I can fully utilize the knowledge available without barriers.
Description: The Resource Accessibility Compliance requirement ensures that all resources in the library meet recognized accessibility standards (such as WCAG) to make tutorials and videos usable for all individuals, including those with disabilities. This requirement focuses on implementing features like adjustable text sizes, captions for videos, and alt text for images, thus promoting inclusivity. By prioritizing accessibility, SalesMap AI strengthens its commitment to a wider audience and enhances user experience for those needing additional support.
Acceptance Criteria: Users can navigate the Resource Library and access various resources while using assistive technologies such as screen readers.

Given a user is accessing the Resource Library with a screen reader, when the user selects a tutorial, then the screen reader should read the title, author, and description of the tutorial clearly.

Users with visual impairments are able to adjust text size and color contrast for better readability within the Resource Library.

Given a user is in the Resource Library, when they adjust the text size and color contrast settings, then the content should reflect those changes immediately without distortion.

The embedded videos in the Resource Library contain captions that are accurate and synchronized with the audio.

Given a user is watching a tutorial video, when the captions are displayed, then they should match the spoken content with no more than a three-second delay.

Images in the Resource Library include descriptive alt text for users with visual impairments.

Given a user is accessing an image resource in the library using assistive technology, when they request the alt text, then the description should accurately convey the content and purpose of the image.

Users can access FAQs in the Resource Library that are formatted for easy navigation and comprehension.

Given a user is browsing the FAQs section, when they utilize the search functionality, then the top relevant FAQs should be displayed in a clear and accessible manner.

All onboarding resources in the Resource Library are designed to meet WCAG 2.1 Level AA standards.

Given a resource is accessed, then it should pass automated accessibility tests for WCAG 2.1 Level AA compliance, ensuring that all elements are accessible to individuals with disabilities.

Feedback Integration

The onboarding assistant gathers feedback from users about their learning experience, allowing continuous improvement of the onboarding process. This feature ensures that the onboarding dialogue evolves based on user insights, enhancing the relevance and effectiveness of the training material.

Requirements

User Feedback Collection

User Story: As a new user, I want to provide feedback on the onboarding process so that I can help improve the training materials for future users.
Description: The User Feedback Collection requirement involves implementing a feature that allows the onboarding assistant to gather insights from users regarding their learning experiences. This feature will use various collection methods, such as surveys, polls, and feedback forms, to capture user sentiments and suggestions. The collected feedback will be stored and analyzed to identify trends, areas for improvement, and potential enhancements to the onboarding process. Effectively integrating this feature will ensure that the onboarding content is continuously updated and refined based on real user experiences, leading to a more relevant and effective training program that ultimately improves user satisfaction and retention rates.
Acceptance Criteria: User submits feedback during onboarding process

Given a user is engaged in the onboarding assistant, when they complete a module, then they are prompted to provide feedback through a survey form that captures their learning experience and suggestions.

Feedback collection through various methods

Given that the user is in the onboarding assistant, when they indicate they want to give feedback, then they can choose from at least three options: survey, poll, or feedback form, ensuring diverse feedback mechanisms.

Data analysis of collected feedback

Given that the feedback has been collected, when the onboarding team reviews the data, then they are able to identify at least three actionable insights or trends from the feedback analysis to improve the onboarding process.

Feedback storage and retrieval

Given that feedback has been collected, when an administrator wants to access user feedback reports, then the system retrieves the feedback data without any errors and displays it in a user-friendly format.

User satisfaction based on feedback

Given that users have completed the onboarding process, when their feedback is analyzed, then at least 80% of users report that the training material was relevant and effective for their learning needs.

Continuous improvement of onboarding based on feedback

Given that feedback is collected and analyzed quarterly, when the onboarding materials are updated, then the changes reflect the most common user suggestions and improvements identified from the feedback.

Real-time Feedback Analysis

User Story: As an onboarding manager, I want to see real-time analysis of user feedback so that I can make immediate adjustments to the onboarding materials and enhance user experience.
Description: The Real-time Feedback Analysis requirement focuses on implementing analytical tools that can process incoming user feedback immediately after collection. This feature will use AI-driven analytics to provide insights into user responses, categorizing feedback into themes and identifying common issues or praises swiftly. The goal of this requirement is to enable the onboarding team to respond promptly to user sentiments and adjust the training content as needed. This will not only enhance user satisfaction but also foster a sense of community and involvement among users as they see their suggestions being acted upon. Integration with the existing dashboard will allow for seamless tracking of user feedback trends over time.
Acceptance Criteria: Real-time Feedback Categorization by Theme

Given user feedback submissions, when the feedback is processed by the AI analytics tool, then it should automatically categorize the feedback into predefined themes with at least 90% accuracy.

Immediate Response Notification for User Feedback

Given a user submits feedback, when the feedback is categorized, then the onboarding team should receive a notification within 5 minutes to allow for prompt action on critical issues.

Feedback Trend Visualization on Dashboard

Given that real-time feedback data is being processed, when a user views the dashboard, then they should see an updated visual representation of feedback trends categorized by themes and sentiments.

User Satisfaction Improvement Tracking

Given that feedback has been implemented in the onboarding process, when comparing user satisfaction scores before and after implementation, then the scores should show at least a 20% improvement.

Integration with Existing CRM Systems

Given the need for seamless operations, when user feedback is analyzed, then the results should be integrated into existing CRM systems without data loss or inconsistency.

Feedback by User Demographics Analysis

Given diverse users providing feedback, when the feedback is analyzed, then the system should report insights segmented by user demographics, allowing targeted improvements.

Continuous Feedback Loop Mechanism

Given the ongoing nature of user feedback collection, when changes are made to the onboarding process based on feedback, then a mechanism should exist to solicit feedback on those changes within 4 weeks.

Feedback Loop Mechanism

User Story: As a repeat user, I want to be informed about the changes made to the onboarding process based on feedback so that I feel that my input is valued and contribute to the community.
Description: The Feedback Loop Mechanism requirement entails creating a structured process whereby user feedback leads to tangible updates in the onboarding process. This feature will establish workflows that include planning, implementation, and notification stages for any changes made in response to user feedback. It will also include features that allow users to be informed of updates based on their suggestions, fostering a sense of collaboration and encouraging more users to provide feedback. This requirement is crucial for reinforcing the commitment to continuous improvement and responsiveness to user needs, ultimately improving user engagement during the onboarding process.
Acceptance Criteria: User submits feedback after completing the onboarding process through the Feedback Integration feature.

Given a user has completed the onboarding process, when they submit feedback through the feedback form, then their feedback should be recorded in the system and acknowledged by the user.

Administrator reviews aggregated feedback from users regarding their onboarding experience.

Given multiple user feedback submissions, when the administrator accesses the feedback dashboard, then they should see an organized view of feedback categorized by themes and trends.

System implements changes to the onboarding process based on user feedback.

Given valid feedback has been submitted, when the onboarding team reviews and decides on improvements, then the changes should be documented in the change log and updated in the onboarding material within two weeks.

Users receive notifications about updates made to the onboarding process as a result of their feedback.

Given updates have been made to the onboarding materials due to user feedback, when the onboarding updates are published, then all users who provided feedback should receive a notification email informing them of the changes.

Users are prompted to provide feedback after completing each module of the onboarding process.

Given a user finishes an onboarding module, when the user is prompted for feedback, then they should see a feedback form that is easy to access and complete without technical issues.

A monthly report summarizes user feedback trends and highlights changes made to the onboarding process.

Given a month has passed, when the designated report is generated, then it should accurately reflect user feedback trends and detail any changes made in response, ensuring transparency in the improvement process.

Feedback from users influences the adjustment of onboarding content and structure.

Given that sufficient feedback indicates a need for change, when the onboarding team revises the content, then the new versions should incorporate at least 70% of the common user suggestions gathered from feedback.

Customizable Feedback Questions

User Story: As an onboarding administrator, I want to customize the feedback questions so that I can gather targeted insights to improve the onboarding process efficiently.
Description: The Customizable Feedback Questions requirement allows administrators to modify or create the specific questions posed to users during the feedback collection process. This feature will enable flexibility in adapting the feedback forms to target specific areas of the onboarding process that require evaluation. By allowing customization, the onboarding team can tailor the feedback collection to address emerging issues or changes in the program swiftly. This adaptability will ensure that the feedback gathered is relevant and actionable, ultimately leading to a more effective onboarding experience for users.
Acceptance Criteria: Customizing Feedback Questions to Gather Insights on User Satisfaction

Given an administrator is logged into the SalesMap AI platform, When they navigate to the feedback settings section, Then they can see an option to customize feedback questions, and can add or modify at least one question in the feedback form successfully.

Saving Custom Feedback Questions for Future Use

Given an administrator has customized feedback questions, When they save the changes, Then the system should confirm that the feedback questions have been saved and should reflect the updated questions when the feedback form is accessed again.

Previewing Feedback Questions Before Deployment

Given an administrator is customizing feedback questions, When they request to preview the feedback form, Then a preview displays all current questions as they will appear to users, allowing administrators to verify correctness before deployment.

Deleting Existing Feedback Questions

Given an administrator is viewing the list of current feedback questions, When they choose to delete a feedback question, Then the question should be removed from the list and should not appear in the feedback form thereafter.

Restricting Question Types Based on User Feedback Goals

Given an administrator is customizing feedback questions, When they select the type of question (e.g., multiple choice, open-ended), Then the system should ensure that only the relevant question types are available based on the feedback goals selected.

Setting Default Feedback Questions for New Users

Given an administrator has configured the feedback settings, When a new onboarding session is initiated, Then the system should automatically assign the default feedback questions as configured by the administrator for that session.

Community Connection

Facilitating a connection with other new users and experienced SalesMap AI users, this feature creates forums or chat groups for collaboration and knowledge sharing. Users benefit from a supportive community that fosters discussion and peer learning throughout their onboarding journey.

Requirements

User Onboarding Forums

User Story: As a new user, I want to participate in forums with experienced users so that I can learn best practices and gain insights into using SalesMap AI effectively.
Description: This requirement focuses on the creation of dedicated forums for new users to connect and collaborate with experienced SalesMap AI users. The forums will allow users to ask questions, share experiences, and provide insights related to their onboarding journey. These forums should be easily accessible from the main dashboard and have features such as threads, replies, and moderation to ensure a supportive environment. This functionality enhances user engagement, facilitates peer learning, and promotes a sense of community, ultimately leading to better user retention and success rates.
Acceptance Criteria: New user navigates to the User Onboarding Forums section from the main dashboard to seek assistance and connect with peers.

Given that a new user is on the main dashboard, when they click on the 'User Onboarding Forums' button, then they should be redirected to the forums page without any errors, and the page should load within 2 seconds.

An experienced user logs into the system and participates in an ongoing discussion within a forum thread.

Given that an experienced user is logged into their account, when they select a forum thread and post a reply, then the reply should be visible to all users in the thread immediately, and the post count should increase by one.

A new user posts a question in the forum seeking help for a specific issue related to the SalesMap AI platform.

Given that a new user has created an account, when they submit a question in the forum, then their post should appear in the thread and show their username, the timestamp of the post, and an option for other users to reply or like the post.

A moderator reviews and approves a reported post within the user forums that contains inappropriate content.

Given that a post has been reported by users for violating the community guidelines, when the moderator reviews the post and decides to remove it, then the post should no longer be visible in the forum, and the post count should decrease by one.

A user receives notifications about new replies and activity in the forums they are participating in.

Given that a user has participated in forum threads, when there is a new reply to their post, then they should receive a notification in their account dashboard and via email within 5 minutes of the reply being posted.

Users can search for specific topics within the User Onboarding Forums to find relevant discussions and answers.

Given that there is a search bar available in the forum section, when a user enters a keyword related to their query and submits the search, then the results should display relevant threads and posts that include the keyword within 3 seconds.

New users have access to guidelines on how to effectively use the forums for maximum engagement and learning.

Given that a new user accesses the User Onboarding Forums for the first time, when they enter the forums section, then they should see a pinned post at the top of the page that outlines the forum rules, best practices, and how to get support.

Real-time Chat Groups

User Story: As a new user, I want to chat with experienced users in real-time so that I can quickly get help with my questions and enrich my learning experience.
Description: This requirement entails implementing real-time chat groups within the SalesMap AI platform, allowing users to instantly connect with peers and mentors for immediate assistance and advice. The chat feature should support direct messaging and group discussions, facilitating spontaneous interactions that enhance the onboarding process. It will increase user satisfaction and accelerate learning by providing on-the-spot support and fostering a collaborative community atmosphere.
Acceptance Criteria: New user initiates a chat group with other onboarding users to discuss their initial experiences with SalesMap AI.

Given a new user has successfully logged into SalesMap AI, when they navigate to the Community Connection feature, then they should be able to create a new chat group and invite at least five users to join the discussion.

An experienced user responds to questions from new users in a designated chat group.

Given that an experienced user is in an active chat group with new users, when they post an answer to a question, then their response should be displayed in real-time to all members of the group without any noticeable delay.

Multiple users participate in a group discussion about a specific feature of SalesMap AI.

Given that a chat group is created specifically for discussing SalesMap AI's lead scoring feature, when at least three users contribute to the conversation, then the chat should allow for simultaneous messaging and display all messages chronologically.

Users want to send a direct message to a mentor or peer for private advice.

Given that a user is logged into the platform, when they select a peer's username from the chat list, then they should be able to initiate a private direct message that only the sender and recipient can view.

Users need to access chat history for later reference.

Given that a user has been actively participating in chat groups, when they navigate to the chat history section, then they should be able to view past discussions with full timelines and timestamps for each message posted.

Knowledge Base Integration

User Story: As a user, I want access to a knowledge base so that I can find answers to common questions without needing to ask for support.
Description: This requirement covers the integration of a comprehensive knowledge base that includes frequently asked questions, troubleshooting guides, user tutorials, and best practices. This knowledge base will be searchable and categorized for ease of access, enabling both new and experienced users to find relevant information quickly. By providing easy access to resources, this feature supports self-service learning and reduces the need for direct support, enhancing overall user satisfaction and efficiency.
Acceptance Criteria: New users are onboarding and need to access the knowledge base to find answers to common questions regarding the SalesMap AI features.

Given a user clicks on the 'Knowledge Base' section in the SalesMap AI dashboard, When they type a keyword related to their question into the search bar, Then the system should display relevant articles within 2 seconds that match their search criteria.

An experienced user wants to troubleshoot a specific issue they encountered while using the platform, and they turn to the knowledge base for assistance.

Given an experienced user navigates to the 'Troubleshooting Guides' category in the knowledge base, When they select an article, Then they should be able to view the entire article without loading errors within 3 seconds.

A user seeks guidance on best practices for utilizing predictive analytics within the SalesMap AI platform.

Given the user browses the 'Best Practices' category in the knowledge base, When they click on a best practice article, Then they should be able to bookmark the article for future reference and share it with other users.

A user is utilizing the knowledge base to improve their understanding of newly added features in SalesMap AI after an update.

Given the user accesses the knowledge base after a new feature update, When they view the 'What’s New' section, Then it should display an updated list of features and corresponding user tutorials in a clear and user-friendly format.

Support request rates are high as users struggle to find basic information. The product team decides to evaluate the knowledge base effectiveness in reducing support tickets.

Given user feedback is collected after the knowledge base launch, When the feedback shows at least a 30% reduction in support tickets related to common queries, Then the knowledge base implementation can be deemed successful.

Users frequently need guidance on navigating the community connection feature in relation to the knowledge base.

Given a user accesses the community forum and searches for discussions related to knowledge base issues, When they find a relevant discussion thread, Then it should have at least 5 user responses providing useful insights or answers.

Users on the platform are trying to find specific FAQs regarding integration with third-party tools available in SalesMap AI.

Given a user enters an integration-related question in the FAQ search of the knowledge base, When the search results are displayed, Then they should include at least 5 relevant FAQs ranked by relevance and be accessible within 2 seconds.

User Feedback Mechanism

User Story: As a user, I want to provide feedback on my onboarding experience so that I can help improve the community resources for future users.
Description: This requirement involves creating a feedback mechanism that allows users to share their experiences and suggestions about their onboarding process with SalesMap AI. It should include surveys and comment forms accessible within the community feature, providing vital insights into user needs and areas for improvement. This feedback will inform future development efforts and improve user experience, ensuring that the community continues to meet user expectations.
Acceptance Criteria: New users access the Community Connection feature after their onboarding process to provide feedback on their experience with SalesMap AI.

Given a new user who has completed the onboarding process, when they access the Community Connection feature, then they should be able to find a feedback form easily and submit their feedback without any technical issues.

Experienced users use the feedback mechanism to share suggestions for improving the onboarding process based on their own experiences with SalesMap AI.

Given an experienced user in the Community Connection, when they navigate to the feedback section, then they should see an option to complete a survey and submit comments specific to their experience, and the form should allow for multiple types of feedback.

The feedback mechanism must collect and categorize user inputs for analysis.

Given that multiple users have submitted feedback via the feedback mechanism, when an admin reviews the submissions, then all feedback should be organized by category (e.g., positive, negative, suggestions) within a centralized database for analysis.

The completion of the feedback mechanism leads to actionable insights for product development.

Given that a monthly review of feedback submissions occurs, when the development team analyzes the feedback data, then at least three actionable insights should be documented and presented in the product development meeting.

Users receive confirmation after submitting their feedback through the mechanism.

Given a user has successfully submitted their feedback, when they complete the submission, then they should receive a confirmation message on the screen and via email acknowledging receipt of their feedback.

The feedback mechanism should be available in multiple formats to accommodate user preferences.

Given diverse user needs, when users access the feedback mechanism, then they should be able to choose between a survey, comment form, or a video feedback option, ensuring at least two different formats are provided.

The feedback collected should influence future community features and enhancements.

Given that feedback has been gathered, when the community feature is updated, then at least one enhancement should directly reflect user suggestions received through the feedback mechanism.

Event and Webinar Hosting

User Story: As a user, I want to join live webinars hosted by experts so that I can learn more about using SalesMap AI effectively in my sales processes.
Description: This requirement focuses on the ability to schedule and host live events, such as webinars or Q&A sessions, within the community platform. These events should be accessible to all users, allowing them to learn from experts and engage directly with top SalesMap AI users. This feature will foster active participation, enhance user knowledge, and build a stronger community, ultimately contributing to better user outcomes and product adoption.
Acceptance Criteria: User schedules a live webinar event from the SalesMap AI community platform.

Given a user is logged into their SalesMap AI account, when they navigate to the 'Events' section and select 'Schedule a Webinar', then they should be able to input all necessary details (title, description, date, and time) and press 'Save' to confirm the event creation.

Users can register for a live event hosted in the community platform.

Given a user views a scheduled webinar in the community event calendar, when they click on the 'Register' button, then they should receive a confirmation message indicating successful registration and an email with event details.

Users receive reminders for upcoming webinars in the community.

Given a user is registered for an upcoming webinar, when the event is one day away, then the user should receive an email reminder containing the event details and a calendar link to add the event.

Users participate in a live Q&A session during a webinar.

Given a user is attending a live webinar, when the host opens the Q&A session, then the user should be able to submit questions through a designated chat interface and view responses in real-time.

User can view recorded webinars after the event has concluded.

Given a user has missed a live webinar, when they visit the 'Past Events' section of the community platform, then they should be able to access and play the recorded webinar session.

The community platform tracks user engagement with webinars.

Given multiple webinars have been conducted, when an admin accesses the 'Webinar Analytics' dashboard, then they should see metrics such as attendance numbers, participant engagement rates, and feedback ratings.

Users can give feedback on webinars they attended.

Given a user has attended a webinar, when they receive a post-event feedback survey, then they should be able to rate the session and provide comments, which will be stored for review in the admin dashboard.

Widget Marketplace

The Widget Marketplace offers users a curated selection of pre-built widgets that can be easily integrated into their dashboards. This feature allows users to quickly access industry-specific visualizations and tools tailored to their needs, enriching their dashboard experience and enabling faster decision-making with relevant data.

Requirements

Curated Widget Selection

User Story: As a sales manager, I want to access industry-specific widgets that can be easily integrated into my dashboard so that I can visualize my sales data more effectively and monitor key performance metrics at a glance.
Description: The Curated Widget Selection requirement ensures that the Widget Marketplace provides users with a broad array of pre-built widgets that are specifically tailored to different industries. This selection will include performance indicators, graphical visualizations, and analytical tools that resonate with the diverse needs of sales teams. Users will benefit from having quick access to relevant tools without the need for extensive customization or development time. The integration of these widgets into the dashboard will elevate user experience and enable data-driven decision-making.
Acceptance Criteria: Users can browse the Widget Marketplace to find and select pre-built widgets that suit their industry needs.

Given the user accesses the Widget Marketplace, when they filter by industry type, then they should see a relevant selection of widgets specific to that industry.

Users can easily integrate selected widgets into their dashboards.

Given the user has selected a widget from the marketplace, when they click on the 'Add to Dashboard' button, then the widget should be added to their dashboard without any errors.

Widgets provide real-time data visualization and updates upon integration into the dashboard.

Given the user has integrated a widget into their dashboard, when the underlying data updates, then the widget should reflect the new data within 5 seconds.

Users can customize the layout of their dashboard after adding widgets from the marketplace.

Given the user has multiple widgets on their dashboard, when they move a widget to a new position and save changes, then the widget's new position should be retained after refreshing the dashboard.

Users can access help or documentation for each widget in the marketplace.

Given the user hovers over a widget in the Widget Marketplace, when they click on the 'Help' icon, then they should be directed to a relevant help page or documentation for that widget.

The Widget Marketplace updates to reflect new widgets added based on user feedback or industry trends.

Given that new widgets have been added to the marketplace, when the user refreshes the marketplace page, then they should see the updated list of available widgets including new entries.

Real-time Data Integration

User Story: As a salesperson, I want the widgets on my dashboard to update in real-time with the latest data from our CRM so that I can respond quickly to changing sales conditions and customer needs.
Description: Real-time Data Integration will allow the Widget Marketplace to connect and pull data dynamically from the user's existing CRM and sales databases. This requirement ensures that the widgets reflect the most current information, enhancing the accuracy of visualizations and insights provided to users. By facilitating real-time data updates, users can make informed decisions based on the latest data trends, thus driving more effective sales strategies and increasing responsiveness to market changes.
Acceptance Criteria: User connects their CRM to the Widget Marketplace and selects a widget for real-time data integration.

Given that the user has integrated their CRM with the Widget Marketplace, When the user selects a relevant widget, Then the widget should display real-time data reflecting the latest sales information from the CRM.

The user wants to visualize sales trends using a specific widget that pulls data from their CRM in real-time.

Given that the user selects the sales trends widget, When the user refreshes the dashboard, Then the widget should automatically update to show the most recent sales trends without manual intervention.

A user makes adjustments to their CRM data and wants to confirm that these changes are reflected in their Widget Marketplace dashboard immediately.

Given that a user has updated their CRM data, When the user views their dashboard, Then all related widgets should display the updated data within 1 minute of the CRM update.

The Widget Marketplace provides a historical view of data to ensure users can compare trends over time with real-time integration.

Given that the user has selected a widget that shows historical data, When the user toggles the view to include real-time data, Then the widget should clearly differentiate between historical data and the current data being pulled from the CRM.

Users need to ensure that the widget maintains high availability and responsiveness while integrating real-time data.

Given that multiple users are accessing the Widget Marketplace simultaneously, When any user interacts with the widgets, Then all widgets should load and respond without exceeding a 3-second delay.

Users require assurance that their data remains secure during real-time integrations from their CRM.

Given that the user has connected their CRM, When the data is being pulled into the Widget Marketplace, Then all data transferred should be encrypted and compliant with data protection regulations.

Users want to receive alerts if real-time data integration fails or encounters issues.

Given that the integration is set up, When there is a failure in pulling real-time data, Then the user should receive a notification alerting them of the integration status and next steps to resolve the issue.

Custom Widget Builder

User Story: As a business analyst, I want to build my own widgets tailored to the specific metrics I track so that I can visualize our sales performance in a way that best suits our unique business processes.
Description: The Custom Widget Builder requirement enables users to create personalized widgets tailored to their specific needs. This feature will include a simple drag-and-drop interface and a set of customizable parameters, allowing users to configure visualizations that best reflect their unique KPIs. This flexibility empowers users to incorporate the most relevant metrics into their dashboards and adjust them as necessary, fostering a highly user-centric experience and enhancing the platform's value.
Acceptance Criteria: User creates a new custom widget for tracking sales performance metrics.

Given a user has navigated to the Custom Widget Builder, when they utilize the drag-and-drop interface to select and add visual elements according to their sales KPIs, then they should be able to save the widget successfully and see it reflected in their dashboard.

User customizes a widget to track different KPI values over time.

Given a user is editing an existing custom widget, when they change the parameters to reflect a new time period and KPI values, then the widget should update in real-time to show the new data accurately without requiring a page refresh.

User accesses the customized widget on their dashboard after creating it.

Given that a user has created and saved a custom widget, when they return to their dashboard, then the custom widget should be visible, correctly displaying the selected metrics and formats as intended.

User deletes a custom widget from their dashboard.

Given a user wants to remove an unnecessary custom widget, when they select the delete option for the widget, then the widget should be removed from the dashboard immediately, with confirmation of the successful deletion.

User attempts to create a custom widget without any visual elements.

Given a user is in the Custom Widget Builder and does not add any visual elements, when they try to save the widget, then an error message should be displayed indicating that at least one visual element is required to save the widget.

User shares their custom widget configuration with a team member.

Given a user has created a custom widget, when they use the share functionality to send the widget configuration link to a team member, then the team member should be able to access and view the widget with the same parameters without additional configuration.

User wants to modify the layout of the custom widget on the dashboard.

Given that a user has a custom widget placed on their dashboard, when they drag the widget to a different location on the dashboard and save the layout, then the widget's new position should be retained the next time the user accesses their dashboard.

User Review System for Widgets

User Story: As a user, I want to read reviews and ratings of widgets in the marketplace so that I can choose the most effective and reliable tools for my sales dashboard.
Description: The User Review System for Widgets will allow users to share feedback and ratings on various widgets available in the Widget Marketplace. This requirement is designed to foster community engagement and help others choose the most effective tools based on user experiences. Implementing a review system will not only guide users in selecting widgets that meet their needs but also provide valuable insights for future widget enhancements and introductions.
Acceptance Criteria: User submits a review for a widget after using it for a week.

Given the user has logged into their account, when they navigate to a widget in the Widget Marketplace, and click on the 'Write a Review' button, then they should be able to enter a rating (1-5 stars) and text feedback and successfully submit the review.

User views the reviews for a particular widget.

Given a user is on the widget's detail page, when they scroll down to the reviews section, then they should see all submitted reviews, including star ratings and feedback text from other users.

User edits their previously submitted review.

Given the user has previously submitted a review, when they navigate to their reviews section and select 'Edit' on a specific review, then they should be able to update their rating and feedback and successfully save the changes.

User filters widgets based on review ratings.

Given the user is on the Widget Marketplace page, when they apply a filter to show only widgets with an average rating of 4 stars or higher, then the displayed widgets should accordingly reflect this rating threshold.

Users can report inappropriate reviews.

Given the user is viewing a review they find inappropriate, when they click the 'Report' button, then the review should be flagged for moderation and the user should receive a confirmation that their report has been submitted.

Admin reviews reported reviews for content violations.

Given an admin is accessing the admin panel, when they navigate to reported reviews, then they should see all flagged reviews along with options to remove or leave them intact, based on content policy decisions.

Widget Documentation and Support

User Story: As a new user, I want access to thorough documentation and support resources for the widgets so that I can utilize them effectively and resolve any questions or issues I may encounter.
Description: Widget Documentation and Support is a comprehensive resource requirement that will provide users with extensive guides, tutorials, and FAQs regarding the widgets available in the Widget Marketplace. This encompasses clear instructions on how to install, configure, and troubleshoot each widget. A robust support system ensures that users feel confident in utilizing the widgets and can maximize their effectiveness, which ultimately enhances user satisfaction and retention.
Acceptance Criteria: Users access Widget Documentation for the first time to learn how to install a specific widget from the marketplace.

Given the user is on the Widget Marketplace page, when they click on a widget, then they should be redirected to a dedicated documentation page that provides installation instructions, configuration steps, and troubleshooting tips specific to that widget.

A user needs to troubleshoot an issue they encounter with a widget they have already installed.

Given the user has installed a widget, when they navigate to the troubleshooting section of the documentation, then they should see relevant FAQs and step-by-step solutions that address the common issues related to that widget.

An organization is onboarding new team members who need to learn about various widgets available in the Widget Marketplace.

Given a new user accesses the Widget Marketplace, when they view the overview section, then they should see an organized list of all widgets available with links to comprehensive documentation for each widget, including usage examples and best practices.

Users are looking for specific configuration guidance for a custom requirement using a particular widget.

Given the user is on a widget’s documentation page, when they click on the 'Advanced Configuration' link, then they should have access to an interactive guide that assists them in setting up the widget according to their custom needs, including examples and screenshots.

Users want to provide feedback on the widget documentation and support resources they have utilized.

Given the user has viewed documentation for a widget, when they reach the end of the documentation page, then they should see a feedback form that allows them to rate the documentation and submit comments or suggestions for improvement.

A user encounters an outdated version of the widget documentation and seeks assistance.

Given the user is navigating the documentation, when they find discrepancies or outdated information, then they should be able to report this issue directly through a visible 'Report Issue' feature that contacts support for updates.

Users are looking for a comprehensive understanding of all widgets available, including usage scenarios.

Given the user is on the widget documentation landing page, when they click on the 'All Widgets' menu, then they should see a complete list of widgets along with a brief description, intended use cases, and links to detailed documentation for each widget.

Drag-and-Drop Layouts

The Drag-and-Drop Layouts feature enables users to effortlessly rearrange widgets on their dashboard. By allowing users to customize the arrangement to suit their workflow and preferences, this functionality elevates user engagement, making it easier for them to access the information they need at a glance.

Requirements

Customizable Dashboard Widgets

User Story: As a salesperson, I want to customize my dashboard widgets so that I can see the most critical sales metrics at a glance and improve my productivity.
Description: This requirement allows users to select and customize which widgets appear on their dashboard. Users can prioritize key performance indicators and metrics that matter most to them, enabling a more tailored view of their sales data. It enhances user satisfaction by empowering users to shape their own experience, promote efficiency, and quickly access relevant information without unnecessary clutter.
Acceptance Criteria: User wants to customize their dashboard to prioritize key metrics for their sales performance.

Given the user is on their dashboard, when they select widgets from the widget library and drag them onto the dashboard, then the selected widgets should appear in the chosen order, with the correct data displayed.

User adds a new widget to their dashboard after initially setting it up with a few widgets.

Given a user has already set up their dashboard with certain widgets, when they add a new widget to the dashboard, then the existing widgets should retain their positions, and the new widget should appear in the correct designated area.

User wants to remove a widget from their dashboard that they no longer find useful.

Given the user is on their dashboard, when they click the remove button on a selected widget, then the widget should be removed from the dashboard and should not display any data.

User rearranges the widgets on their dashboard to reflect a new workflow.

Given the user has multiple widgets on their dashboard, when they use the drag-and-drop feature to rearrange the widgets, then the widgets should move to the new positions and save this arrangement for future sessions.

User refreshes their dashboard after making changes to the arrangement of widgets.

Given the user has rearranged widgets on their dashboard, when they refresh the page, then the dashboard should display the widgets in the new arrangement as set by the user before the refresh.

User accesses their dashboard on a different device after customizing it.

Given the user has customized their dashboard on one device, when they log into their account on another device, then their customized dashboard with all widget arrangements and selections should appear as previously saved.

User encounters a situation where the drag-and-drop feature does not work as expected.

Given the user is in the process of dragging a widget, when the widget fails to move, then an error message should be displayed to inform the user that the action could not be completed and provide a suggestion for resolution.

Responsive Design for Mobile Use

User Story: As a sales manager, I want to access my dashboard on my mobile device so that I can stay updated on sales performance while traveling or working remotely.
Description: The application must support a responsive design that allows users to access and utilize the dashboard effectively on various screen sizes, particularly mobile devices. This capability is essential for ensuring that users can monitor sales activities and engage with their dashboard while on-the-go. It enhances user experience and accessibility, allowing for greater flexibility in how and where users can utilize the platform.
Acceptance Criteria: User accesses the SalesMap AI dashboard on a mobile device to view real-time sales metrics while traveling.

Given a mobile device with a screen size less than 6 inches, When the user opens the dashboard, Then the layout adjusts fluidly to the screen size without losing functionality or clarity.

User rearranges the dashboard widgets on a tablet while in a meeting, optimizing view for presenting to a client.

Given a tablet screen size between 7 and 10 inches, When the user drags and drops a widget to a new position, Then the widget should maintain its functionality and not interfere with other widgets' display.

User checks the dashboard on a smartphone to ensure all critical information is accessible at a glance during a sales call.

Given the dashboard is viewed on a smartphone, When the user scrolls vertically, Then all widgets should remain visually accessible and not require horizontal scrolling for any content to be visible.

User updates the layout of their dashboard on a mobile device to streamline their workflow while multitasking.

Given the user is logged in to the mobile version of SalesMap AI, When they save a new layout arrangement, Then the updated layout should be retained upon the next login and across all devices.

User utilizes a mobile device to navigate between different sections of the dashboard quickly during a conference.

Given the dashboard has multiple sections, When the user selects a different section from a dropdown menu, Then the transition occurs within 2 seconds without any loading errors.

User interacts with the dashboard to filter sales data while on their mobile phone to see real-time insights.

Given that filtering options are visible on the mobile dashboard, When the user applies a filter, Then the data displayed should update to reflect the selected criteria within 3 seconds.

User switches between landscape and portrait mode on their mobile device to check metrics effectively during a presentation.

Given the dashboard is opened in landscape mode, When the user rotates the device to portrait mode, Then the layout adjusts seamlessly without affecting the visibility of any elements.

Widget Snap-to-Grid Functionality

User Story: As a user, I want my widgets to snap into place when I move them so that I can quickly and easily organize my dashboard without any overlapping or misalignment.
Description: Implement a snap-to-grid feature for the drag-and-drop functionality, so when users rearrange the widgets on their dashboard, they automatically align to a grid for a cleaner and more organized look. This requirement will enhance the user interface by providing a polished appearance, making it easier for users to arrange and align widgets precisely without manual adjustments.
Acceptance Criteria: User rearranges widgets on the dashboard to create a personalized view that prioritizes the most critical sales data for their workflow.

Given the user drags a widget to a new location, when they release it, then the widget should snap to the closest grid point and align perfectly with adjacent widgets.

User attempts to drag multiple widgets simultaneously to organize their dashboard layout quickly.

Given the user selects multiple widgets to move, when they drag them, then each widget should individually snap to the grid upon release, maintaining their spacing.

User adjusts the size of a widget on their dashboard and wants to ensure it still aligns with the grid after the resize.

Given the user resizes a widget to a larger dimension, when they release the widget, then it should automatically resize and snap to the nearest grid size without overlapping adjacent widgets.

User removes a widget from their dashboard and wants the remaining widgets to automatically realign according to the grid.

Given the user deletes a widget from their dashboard, when the action is confirmed, then all remaining widgets should snap to fill any gaps left by the removed widget, adhering to the designated grid alignment.

User wants to ensure that widgets automatically adjust to the grid when changing the layout in responsive design on smaller screens.

Given the user accesses the dashboard on a mobile device, when they rearrange the widgets, then all widgets should snap to the grid to maintain a clean and organized layout within mobile constraints.

User checks how well the snap-to-grid feature works across different browsers and devices.

Given the user utilizes various browsers (Chrome, Firefox, Safari) and devices (desktop, tablet, mobile), when they perform drag-and-drop actions, then the snap-to-grid functionality should work consistently without any misalignment issues.

User Permissions for Dashboard Customization

User Story: As an administrator, I want to control which team members can customize their dashboards so that I can maintain consistent reporting standards across the sales team.
Description: Create a system of user permissions that allows administrators to control which users can customize their dashboard layouts. This feature ensures that only authorized personnel can make changes to certain aspects of the dashboard, promoting security and consistency across the organization. It is vital for maintaining corporate standards and protecting sensitive sales data from unauthorized modifications.
Acceptance Criteria: User Access Control for Dashboard Customization

Given an administrator, when they assign user roles, then users with 'customize permissions' can rearrange dashboard widgets, while users without this permission cannot rearrange them.

Audit Trail for Dashboard Changes

Given that a user with customization permissions rearranges dashboard widgets, when the changes are saved, then an audit log entry reflecting the user's actions and timestamp should be created.

Default Dashboard Layout for Unauthorized Users

Given a user without customization permissions, when they attempt to access the dashboard, then they should see a predefined default layout without any option to modify it.

Role-based Permission Verification

Given multiple users with different roles, when they attempt to customize their dashboards, then only users with the appropriate role should successfully access the customization options.

Error Message for Unauthorized Customization Attempts

Given a user without customization permissions, when they try to access the drag-and-drop feature, then they should receive an informative error message stating they are not authorized to customize the dashboard.

Administrative Override for User Customizations

Given an administrator, when they need to restrict a user's dashboard layout, then the administrator should be able to override any customizations made by that user.

Performance Impact Assessment

Given various users with different permissions, when multiple users are customizing their dashboards, then system performance should not degrade significantly, maintaining responsiveness.

Integration with Third-Party Tools

User Story: As a sales analyst, I want to integrate my dashboard with third-party analytics tools so that I can view all relevant data in one place and make informed decisions more easily.
Description: Develop integrations with popular third-party tools such as Google Analytics and CRM systems, allowing users to pull data from these sources directly into their dashboard. This requirement will enhance the platform's capabilities, providing users with a comprehensive view of their sales performance alongside analytics from various sources. It fosters a more connected workflow and adds value to the user's experience by unifying diverse data points in one location.
Acceptance Criteria: Third-Party Tool Integration Verification for Google Analytics

Given a user has valid Google Analytics credentials, when the user authenticates and connects the Google Analytics account to SalesMap AI, then data from the Google Analytics account should be successfully pulled into the user's dashboard without errors.

User Dashboard Data Display from CRM System Integration

Given a user has successfully integrated a CRM system, when they navigate to the dashboard, then they should see real-time sales performance data pulled from the CRM displayed accurately and updated at least every 5 minutes.

Error Handling for Failed Integration Attempts

Given a user attempts to integrate a third-party tool with invalid credentials, when the authentication fails, then the system should display an error message clearly stating the reason for the failure and suggest corrective action.

Checking Data Accuracy from Integrated Analytics

Given that Google Analytics data has been successfully integrated, when the user compares the data in SalesMap AI with the Google Analytics platform, then the key metrics in both systems should match accurately within a 5% variance.

User Customization of Integrated Data Module

Given a user has integrated third-party tools into their dashboard, when the user attempts to customize the layout of the integrated data widgets, then they should be able to easily drag-and-drop the widgets to rearrange them as desired.

Alerts for Integration Data Updates

Given that a user has set up integrations with third-party tools, when there is a significant change in the integrated data (e.g., a sales metric increases or decreases beyond a defined threshold), then the user should receive an alert notification on their dashboard.

Integration Configuration and Settings Access

Given a user has integrated third-party tools, when they navigate to the settings menu, then they should have the option to configure or disconnect any of the integrated services with intuitive controls.

Real-time Data Refresh Capability

User Story: As a user, I want my dashboard to refresh automatically with real-time data so that I can respond immediately to changes in sales activity without manual refreshing.
Description: Implement a feature that allows the dashboard to refresh in real time, displaying the most current sales data as it comes in. This requirement is essential for providing users with up-to-date information, fostering quicker decision-making and responsiveness to changing sales conditions. Users will benefit from having the latest insights at their fingertips, enhancing their ability to act promptly on emerging sales trends and opportunities.
Acceptance Criteria: User Accessing the Dashboard to View Latest Sales Data

Given a user is logged into the SalesMap AI platform, When the user accesses their dashboard, Then the dashboard should display the most recent sales data within a 5-second refresh interval.

User Monitoring Potential Sales Trends

Given that the dashboard displays sales data, When a significant change occurs in sales metrics, Then this change should be reflected on the dashboard within 5 seconds, allowing users to identify trends in real time.

User Customizing their Dashboard Layout

Given a user has rearranged their dashboard widgets, When new sales data is received, Then the dashboard should refresh without altering the positions of existing widgets or causing layout disruption.

User Receiving Notifications of Real-Time Updates

Given the user has opted in for real-time notifications, When new sales data is refreshed, Then the user should receive a notification alerting them to the new information available on the dashboard.

User Filtering Sales Data on the Dashboard

Given a user applies filters to the sales data (e.g. date range, region), When the dashboard refreshes, Then the displayed data should correspond precisely to the applied filters without delay.

User Evaluating Dashboard Performance Under Load

Given a high volume of sales transactions occurs, When the dashboard refreshes, Then it should maintain performance and refresh within the prescribed 5-second window without crashing or freezing.

Interactive Data Filters

Interactive Data Filters empower users to customize the data displayed in their widgets based on specific criteria such as date ranges, lead scoring, or campaign performance. This feature enhances analytical capabilities, enabling users to focus on the most relevant insights, leading to improved strategic decision-making.

Requirements

Dynamic Date Range Selection

User Story: As a sales analyst, I want to select specific date ranges for the data displayed in my reports so that I can analyze trends over different periods effectively and tailor my strategies accordingly.
Description: The Dynamic Date Range Selection requirement allows users to set customizable date ranges for their data filters, enabling them to view specific periods relevant to their sales performance, campaign effectiveness, or lead activity. This functionality benefits users by providing granular control over the data they analyze, making it easier to identify trends and seasonal impacts in their sales efforts. Integrating this feature involves creating a user-friendly interface that presents various options for selecting start and end dates, as well as predefined ranges like 'Last 7 Days', 'Last Month', or 'Year to Date'. The expected outcome is improved user engagement with data insights, leading to faster and more informed decision-making.
Acceptance Criteria: User wants to analyze their sales performance over the last quarter by selecting a dynamic date range from the filter options.

Given that the user is on the data analytics dashboard, when they access the date range filter, then they can select 'Last Quarter' and the data displayed should update to reflect only the sales data from the last three months.

A sales manager needs to review the effectiveness of a recent marketing campaign by looking at the data from the last month.

Given that the user has selected the 'Last Month' option from the date range filter, when they apply this selection, then the displayed metrics should include only data from the last 30 days, with no data from earlier time periods.

The user wishes to compare performance between specific dates to assess seasonal trends in lead activity.

Given that the user enters custom start and end dates in the date range filter, when they apply those dates, then the data displayed should only show information within the chosen period, ensuring that the metrics accurately reflect the selected timeframe.

A user wants to quickly apply a standard date range option to view recent sales trends.

Given that the user selects the 'Last 7 Days' option from the filter, when they apply the selection, then the dashboard should instantly refresh to show only the sales data from the last week, and the filter should remain applied until changed by the user.

The user is analyzing the impact of seasonal promotions on lead activity and requires a specific date range to be input manually.

Given that the user manually inputs a start date of January 1st and an end date of January 31st, when they save these selections, then the data should accurately reflect lead activity for the month of January only, without including data from other months.

A user intends to access historical data to make informed strategic decisions for the upcoming quarter.

Given that the user selects a predefined range, 'Year to Date', when applying this selection, then the displayed data metrics should reflect all sales and lead information from January 1st of the current year to the present date.

A user requires confirmation that their selected date range saved correctly and can be changed easily if needed.

Given that the user sets a custom date range and then clicks the 'Save' button, when they navigate away from the page and return, then the selected date range should remain saved and be visibly unchanged in the date filter settings.

Lead Scoring Criteria Customization

User Story: As a sales manager, I want to customize the lead scoring criteria to reflect my business’s unique priorities so that I can focus on the leads that are most likely to convert.
Description: The Lead Scoring Criteria Customization requirement enables users to modify the scoring parameters used to evaluate leads based on their unique business needs and historical data trends. This feature empowers businesses to prioritize prospects that are most likely to convert, enhancing the effectiveness of their sales strategies. The implementation involves allowing users to assign weights to various attributes (e.g., engagement level, demographic information, or past interactions) and to create dynamic scoring models that automatically adjust based on new inputs. The expected outcome is a more tailored lead management process that directly aligns with the user's target market and improves sales efficiency.
Acceptance Criteria: User modifies the lead scoring criteria to prioritize leads who interacted with the company in the last 30 days, focusing on increasing engagement and conversion rates.

Given a user is logged into SalesMap AI, when they access the lead scoring customization settings, and assign a weight of 10 to leads with recent interactions, then the system must save these changes and reflect them in the lead scoring calculation.

A sales manager needs to evaluate the impact of changes made to the lead scoring model over the last month, analyzing conversion rates based on different scoring criteria.

Given that the user has applied new scoring criteria to leads over the last month, when they generate a report, then the report must display the updated conversion rates with a comparison to the previous scoring model over the same period.

A marketing team wants to adjust the lead scoring model in real-time based on new data inputs from ongoing campaigns to ensure timely prioritization of leads.

Given an active campaign is generating new lead data, when the user inputs updated engagement metrics into the lead scoring model, then the system must automatically recalculate all relevant lead scores within 5 seconds and provide an updated widget view.

A user wants to access the historical lead scoring data to make informed adjustments to the scoring parameters based on past performance metrics and trends.

Given the user selects the historical data option, when they specify the date range for which they want to view past scoring metrics, then the system must present a comprehensive report that includes lead scores and their corresponding conversion rates over the selected period.

An operations manager needs to ensure that all changes made to lead scoring parameters are traceable and have an audit trail for compliance purposes.

Given the user modifies any scoring parameter, when they save these changes, then the system must log the change with the date, time, user ID, and the specifics of what was changed, ensuring compliance is met.

A sales representative wants to test the lead scoring criteria before implementing them on a larger scale to ensure they align with the expected outcomes.

Given a sales representative adjusts lead scoring parameters in a sandbox environment, when they run a test analysis with sample leads, then the system must provide immediate feedback indicating the expected lead prioritization based on the new criteria.

Campaign Performance Metrics Filter

User Story: As a marketing director, I want to filter campaign performance metrics so that I can identify what’s working well and optimize our marketing strategies based on data.
Description: The Campaign Performance Metrics Filter requirement provides users the ability to filter data based on specific campaign performance metrics such as open rates, click-through rates, or conversion rates. This feature enhances analytical capabilities by allowing users to dive deeper into the effectiveness of their marketing campaigns and make data-driven adjustments. The implementation includes creating filters that can be applied to various visualizations on the dashboard, ensuring that users can quickly isolate and analyze successful and underperforming campaigns. The expected outcome is a clearer understanding of campaign effectiveness and a more informed adjustment process for future marketing efforts.
Acceptance Criteria: User applies the Campaign Performance Metrics Filter to analyze the effectiveness of a recent email marketing campaign on the SalesMap AI dashboard.

Given the user has access to the Campaign Performance Metrics Filter, when they select filters for open rates and click-through rates for the last month, then the dashboard should display only the campaigns that meet the specified criteria with the relevant performance metrics.

A sales manager regularly reviews the performance of multiple campaigns using the filter feature to determine ongoing marketing strategies.

Given the user has applied multiple filters including conversion rate thresholds, when they adjust the parameters, then the visualizations should update in real-time, reflecting only the campaigns that match the new filters without requiring a page refresh.

The user has used the Campaign Performance Metrics Filter to isolate underperforming campaigns and is now looking to export the filtered data for further analysis.

Given the filtered data based on the user's specified parameters, when the user selects the 'Export' option, then the system should successfully generate a downloadable report in CSV format containing all visible data on the dashboard reflected by the filters applied.

A user is interested in understanding the historical trend of campaign performance metrics over the past year.

Given the user selects a date range of 'Last Year' in the Campaign Performance Metrics Filter, when they view the dashboard, then historical performance data should be displayed for all campaigns within that date range, including average open and click-through rates, and conversion rates.

An analyst wants to compare the performance of different campaign types using the filter feature on the SalesMap AI dashboard to generate insights.

Given the user has selected filters for different campaign types (e.g., email, social media), when they apply these filters, then the dashboard should enable side-by-side comparisons of the selected metrics across the specified campaign types without data loss or latency.

The user needs to verify that the Campaign Performance Metrics Filter only includes valid metrics and date ranges applicable to their selected campaigns.

Given the user accesses the Campaign Performance Metrics Filter, when they attempt to apply filters with invalid metric names or out-of-range dates, then an error message should inform them about the invalid selection and prompt them to correct it.

A user wants to quickly switch between different pre-saved filter configurations on the dashboard to analyze various aspects of campaign performance.

Given pre-defined filters have been saved by the user, when they select a saved filter configuration, then the dashboard should instantly refresh to display the respective filtered metrics relevant to the chosen configuration without additional input required from the user.

Real-Time Data Update Notification

User Story: As a sales representative, I want to receive real-time notifications about changes in lead scores so that I can take immediate action on high-priority prospects.
Description: The Real-Time Data Update Notification requirement enhances the user experience by providing alerts when relevant data updates occur within the dashboard, such as changes in lead scores or campaign performance metrics. This feature ensures that users are always informed of critical changes, enabling them to react promptly, whether that means adjusting a campaign strategy or following up on a high-priority lead. Implementation involves integrating a notification system that triggers based on specific criteria set by the user (e.g., significant score changes or milestone achievements). The expected outcome is improved response times and a proactive sales approach based on real-time information.
Acceptance Criteria: User receives a notification for a significant change in lead score while actively using the dashboard.

Given the user is actively using the dashboard, when a lead's score changes by more than 20%, then a notification should be displayed to the user with details about the lead and the new score.

User sets specific parameters to receive notifications for campaign performance metrics.

Given the user sets thresholds for campaign performance, when the performance metric exceeds the defined thresholds, then the user should receive a notification alerting them of the change.

User checks the notifications after a significant change has occurred.

Given the user was notified about a change, when the user clicks on the notification, then they should be redirected to the relevant dashboard widget reflecting the updated data.

User deactivates notifications for lead scores.

Given the user is in the notification settings, when the user opts to disable lead score notifications, then they should no longer receive alerts for lead score changes.

User receives a summary notification at the end of the day summarizing significant updates.

Given the user is registered for daily summaries, when the end of the day arrives, then the user should receive a notification summarizing all significant changes in lead scores and campaign performance metrics from that day.

User experiences a delay in receiving notifications for critical updates.

Given the real-time data update notification system is operational, when a significant change occurs, then the notification should be delivered to the user within 5 seconds.

User customizes notification preferences based on specific criteria.

Given the user accesses notification settings, when the user customizes their preferences to receive notifications only for high-priority leads, then the system should save these preferences and only notify the user for those leads.

Customizable Dashboard Layouts

User Story: As a sales team lead, I want to customize my dashboard layout so that I can highlight the most relevant data for my team's specific needs and workflow.
Description: The Customizable Dashboard Layouts requirement allows users to rearrange and personalize the widgets and data displays on their dashboards according to their preferences. This feature enhances user satisfaction by providing a tailored experience that aligns with individual workflows and priorities, leading to increased productivity. Implementation involves drag-and-drop functionality for widgets, as well as options to hide, show, or resize specific sections of the dashboard. The expected outcome is a more efficient user interface that caters to the unique needs of each user, thus improving engagement with their sales data.
Acceptance Criteria: User Rearranging Dashboard Widgets

Given that the user is logged into their SalesMap AI account, when they drag a widget from one position to another on the dashboard, then the widget should be successfully moved to the new location without any errors and remain in the new position upon refreshing the page.

User Hiding and Showing Widgets

Given that the user is on the customizable dashboard, when they select to hide a specific widget from their view, then that widget should no longer be visible on the dashboard and the option to show it again should be accessible from a 'Show Widgets' menu.

User Resizing Dashboard Sections

Given that the user has multiple widgets displayed on their dashboard, when they select to resize a section of the dashboard, then that section should change size accordingly and adjust the layout of any adjacent sections or widgets appropriately.

User Saving Dashboard Layouts

Given that the user has customized their dashboard layout, when they click on the 'Save Layout' button, then their preferences for widget positions, visibility, and sizes should be saved and automatically applied the next time they log into SalesMap AI.

User Accessing Customized Dashboard on Different Devices

Given that the user has customized their dashboard layout on their primary device, when they log into their SalesMap AI account from a different device, then their customized dashboard layout should be reflected exactly as saved, maintaining consistency across devices.

User Switching Between Dashboard Layouts

Given that the user has multiple saved dashboard layouts, when they select a different layout from the 'Dashboard Layout' dropdown, then the dashboard should seamlessly switch to the selected layout, with all widgets displayed as per the selected configuration.

User Dashboard Performance During Customizations

Given that the user is actively customizing their dashboard, when they perform actions such as dragging, hiding, or resizing widgets, then the performance of the dashboard should remain smooth without noticeable lag or delays in response to user interactions.

Real-Time Update Alerts

Real-Time Update Alerts notify users when key metrics in their widgets change or reach certain thresholds. This proactive feature ensures that users stay informed about critical developments in their sales data, allowing for prompt responses and optimization of strategies.

Requirements

Threshold Notification

User Story: As a sales manager, I want to receive real-time update alerts about critical sales metrics so that I can swiftly adapt my strategies based on the latest data changes and optimize my team’s performance.
Description: This requirement involves the implementation of a notification system that alerts users when key sales metrics in their widgets change or reach predefined thresholds. By integrating with existing user dashboards, this feature will utilize real-time data analysis to trigger alerts based on user-defined parameters such as sales volume, lead engagement, and campaign performance. This proactive notification mechanism enhances user responsiveness, allowing businesses to optimize their sales strategies promptly based on critical data changes. The alerts will be customizable, providing users with flexibility in monitoring the metrics that matter most to them, thus significantly improving user engagement with the platform.
Acceptance Criteria: User receives an alert when sales volume drops below a user-defined threshold on their dashboard.

Given the user has set a sales volume threshold, When the sales volume recorded drops below the threshold, Then the user should receive an immediate notification alerting them of the drop.

User receives an alert when lead engagement metrics exceed a defined threshold, indicating high interest.

Given the user has set a lead engagement threshold, When the lead engagement metrics exceed this threshold, Then the user should receive a notification indicating increased lead activity.

User can customize alert settings for different metrics in their dashboard.

Given that the user accesses the alert settings, When they set or modify thresholds for specific metrics, Then the system should allow these changes to be saved and applied immediately to their dashboard notifications.

User receives a consolidated daily summary alert of all metric changes that reached defined thresholds.

Given the user has opted in for daily summary alerts, When the end of the day occurs, Then the user should receive a summary alert that lists all metrics which triggered alerts throughout the day.

User receives feedback on the effectiveness of the alerts generated after following up on the recommended actions in the notifications.

Given that the user acts upon an alert notification, When they follow through with the recommended action, Then the system should prompt the user to provide feedback on the effectiveness of the alert.

Custom Alert Settings

User Story: As a business owner, I want to customize my alert settings for different sales metrics so that I can focus on the most relevant data that drives my business decisions and performance.
Description: This requirement focuses on allowing users to set customized alert preferences tailored to their specific needs and business strategies. This feature will enable users to define which metrics they want to monitor and at what values, ensuring that they receive notifications that align with their unique sales objectives. Integration with user profiles will ensure that these settings are saved and easily accessible across sessions. Providing custom alert settings enhances user satisfaction by equipping them with the power to focus on metrics that directly impact their success, thus increasing platform utilization and engagement.
Acceptance Criteria: User Customization of Alert Preferences for Sales Metrics

Given a logged-in user, when they navigate to the alert settings page, then they should be able to select specific metrics from a list and set threshold values for those metrics.

Saving and Retrieving Custom Alert Settings

Given a user has set custom alert preferences, when they log out and log back in, then their saved alert preferences should be displayed correctly in the alert settings page.

Notification Triggering Based on Custom Alerts

Given a user has set a custom alert for a sales metric, when that metric reaches the defined threshold, then the system should send a notification to the user via their preferred method (email or in-app alert).

Editing Existing Custom Alert Settings

Given a user has existing custom alert settings, when they navigate to the alert settings page and edit those settings, then the updated settings should be saved successfully and reflected in the user’s profile immediately.

User Interface for Custom Alert Settings

Given a user is on the alert settings page, when they view the interface for customizing alerts, then the layout should be intuitive, with clear labels and descriptions for all options available for setting alerts.

Integration with User Profile for Alert Settings

Given that a user has set custom alert preferences, when they access their user profile from any module of the platform, then they should be able to see and manage their alert settings without issues.

Historical Data Comparison

User Story: As a sales analyst, I want to compare current metrics with historical data so that I can identify trends and make informed predictions about future performance.
Description: This requirement entails the development of a feature that allows users to compare current sales metrics against historical data. By incorporating data visualization tools, users can easily assess trends over time and determine how current performance stacks up against previous periods. This capability enhances strategic planning by providing users with critical insights into their sales trajectories, facilitating data-driven decision-making processes. The historical data comparison will be seamlessly integrated within the dashboard to provide a comprehensive view, thereby maximizing the value of the sales data collected and enabling users to forecast trends more accurately.
Acceptance Criteria: User initiates a comparison between current sales metrics and historical data for the last quarter.

Given the user selects the Historical Data Comparison feature in the dashboard, when they choose the last quarter as the time frame, then the system displays the current sales metrics alongside the historical data for the same period, allowing for clear visual comparison.

User wants to visualize sales trends over the past year comparing current month's data with each month of the previous year.

Given the user selects the current month and chooses to compare it with the previous year, when the user clicks 'Compare', then the dashboard displays a line graph visualizing the current month's sales metrics against each month of the previous year for easy trend analysis.

User sets a specific threshold for sales metrics to identify significant changes in performance when comparing with historical data.

Given the user defines a performance threshold (e.g., a 20% increase or decrease) for current sales metrics, when comparing with historical data, then the system provides visual alerts clearly indicating any metrics that surpass these thresholds.

User reviews changes in key performance indicators (KPIs) to make strategic decisions.

Given the user initiates a historical comparison analysis, when selecting a range of KPIs to review, then the dashboard provides a detailed report highlighting the percentage change and variance from historical averages, aiding in strategic decision-making.

User seeks to generate a report of historical data comparisons for stakeholders.

Given that the user has completed a historical data comparison analysis, when they select the option to generate a report, then the system produces a PDF report summarizing the analysis, including visual graphs and key findings, which can be shared with stakeholders.

User checks for discrepancies in data between current and historical metrics after an update.

Given that the user has completed a historical data comparison following a recent update to sales metrics, when reviewing the comparison, then any discrepancies must be highlighted, prompting the user to investigate the data accuracy and sources.

Integrated Dashboard Notifications

User Story: As a user of SalesMap AI, I want to receive notifications directly on my dashboard about any significant changes in my sales metrics so that I can stay informed and act quickly without disrupting my workflow.
Description: This requirement is centered around integrating real-time alerts directly within the user dashboard, ensuring that notifications about key metric changes are immediately visible to users. By utilizing visual cues such as badges or pop-up alerts, users can quickly assess critical developments without navigating away from their current tasks. This integration aims to improve user experience by making vital information readily accessible, thereby reducing response times to significant sales data changes and supporting a more agile approach to sales strategy adjustments.
Acceptance Criteria: User receives real-time alerts during an active sales call when key metrics in their dashboard change significantly, affecting their strategy and engagement with the lead.

Given the user is on an active sales call, when a key metric in the dashboard changes significantly, then the user should receive a pop-up alert that describes the change and its impact.

User is reviewing their dashboard in a meeting and crucial sales metrics, such as lead scores and conversion rates, update automatically, prompting immediate discussion.

Given the user is viewing their dashboard in a meeting, when key metrics update beyond preset thresholds, then an audible alert and a visual badge should appear on the dashboard to notify the user of the changes.

A user sets personalized thresholds for key metrics in their dashboard and expects to receive notifications when these thresholds are crossed during business hours.

Given the user has set personalized thresholds for key metrics, when those metrics cross the thresholds, then the user should receive a notification alerting them to the change during business hours.

The user is managing multiple campaigns and relies on real-time alerts to adjust their strategies based on current performance metrics from their dashboard.

Given the user is managing multiple campaigns, when performance metrics such as click-through rates or engagement levels fall below a defined threshold, then the user should receive an immediate alert to facilitate timely strategy adjustments.

The user checks their dashboard after making changes to the sales strategy and wants to see if those changes positively impacted key metrics.

Given the user has made changes to their sales strategy, when they next access their dashboard, then they should see real-time updates reflected in the metrics along with a summary alert of any significant changes since their last access.

A user employs the integrated dashboard with multiple users in their sales team, and requires notifications to be visible to all team members when any key metric changes significantly.

Given multiple users are accessing the integrated dashboard, when a key metric changes significantly, then all online users should receive synchronized notifications regarding the change, ensuring transparency within the team.

Mobile Alert Functionality

User Story: As a remote sales representative, I want to receive mobile notifications on my phone about important sales updates so that I can make informed decisions and respond promptly no matter where I am.
Description: This requirement involves the development of a mobile notification system that ensures users receive alerts about key metric changes while on the go. The mobile functionality will extend the desktop alert capabilities to mobile platforms, providing users with instant access to critical sales updates regardless of their location. This feature is essential for sales professionals who rely on timely information to make decisions, allowing them to remain productive outside of traditional office settings. Ensuring that the notifications are optimized for mobile devices will enhance user convenience and overall satisfaction with the platform.
Acceptance Criteria: User receives instant mobile alerts for changes in key performance metrics when out of the office.

Given a user has their mobile app installed and notifications enabled, When a key performance metric changes or reaches a predefined threshold, Then the user should receive a push notification within 5 seconds of the change.

User is able to customize the types of alerts they wish to receive.

Given a user navigates to the alert settings, When the user selects specific metrics for notifications, Then the system should save these preferences and only send alerts for the chosen metrics.

Notification contains actionable insights based on the changed metrics.

Given a user receives a mobile alert for a metric change, When the user opens the alert, Then the notification should clearly state what metric changed, the new value, and suggested actions based on this change.

User experiences no delay in receiving notifications on diverse mobile devices.

Given that multiple users with different mobile devices are receiving alerts simultaneously, When a key metric changes, Then all users should receive their notifications without any delay or performance issues.

User can acknowledge and dismiss alerts directly from their mobile device.

Given a user has received a notification, When the user interacts with the notification to acknowledge or dismiss it, Then the system registers this action and updates the alert status in the user's dashboard accordingly.

User can track alert history in the mobile app.

Given a user navigates to the alert history section in the mobile app, When the user views this section, Then it should display a chronological list of previously received alerts with date, time, and metric details.

Custom Color Schemes

The Custom Color Schemes feature allows users to personalize the aesthetic of their dashboard widgets, aligning them with their branding or personal preferences. This not only enhances user satisfaction through visual appeal but also helps in quickly identifying critical data through customized color coding.

Requirements

Custom Color Schemes Functionality

User Story: As a sales manager, I want to customize the color schemes of my dashboard widgets so that I can align them with my company branding and quickly identify important metrics.
Description: The Custom Color Schemes function allows users to select and apply personalized color palettes to their dashboard widgets, enabling greater flexibility in design. This requirement will facilitate users in aligning the aesthetic of their dashboards with their company's branding or personal taste, thus enhancing their overall user experience. Implementing this feature will also improve data visibility by allowing users to apply specific colors to denote urgency or importance, leading to quicker and better-informed decision-making. The color schemes should be easily selectable and changeable without affecting the underlying data and metrics, ensuring that personalization does not compromise performance or usability.
Acceptance Criteria: User selects a custom color scheme for their dashboard widgets based on their branding requirements.

Given the user is on the dashboard settings page, when they select a color scheme from the available options and apply it, then the dashboard widgets should immediately reflect the selected colors without any delay or performance issues.

User changes the custom color scheme multiple times within a single session.

Given the user has previously set and applied a color scheme, when they select a different color scheme from the settings, then the current color scheme should update instantly on the dashboard without affecting the underlying data visualizations or performance.

User applies a custom color scheme to denote urgency in data visibility.

Given the user has access to a color palette that includes a red color option, when they apply red to a specific metric to indicate urgency, then the dashboard should display this metric with the red color consistently across all views of the dashboard.

User seeks confirmation when applying a custom color scheme to their dashboard widgets.

Given the user has selected a new custom color scheme, when they click on the 'Apply' button, then a confirmation prompt should be displayed asking if they wish to proceed with the changes before applying the new scheme.

User reverts back to the default color scheme after applying a custom scheme.

Given the user has applied a custom color scheme, when they select the option to revert to the default color scheme, then the dashboard should return to its original color scheme immediately and consistently without losing any user data or settings.

User checks for accessibility compliance with custom color schemes.

Given the user has applied a custom color scheme, when they assess the dashboard for accessibility, then the contrast between the background and text colors should meet established accessibility standards to ensure readability for all users.

User saves their custom color scheme preferences for future sessions.

Given the user has applied a custom color scheme, when they log out and then log back in, then their previously selected custom color scheme should be preserved and applied to the dashboard upon logging in.

Color Palette Options

User Story: As a user, I want to have multiple color palette options available so that I can select themes that resonate with my personal style and branding needs.
Description: This requirement focuses on providing a variety of color palette options for users to choose from when customizing their dashboard. Users should be able to select from predefined themes or create their own colors using a color picker tool. The implementation of this feature will cater to diverse user preferences and allow for a more tailored interface that can accommodate various branding strategies, making the platform more appealing and beneficial for users.
Acceptance Criteria: User selects a predefined color palette from the options available in the dashboard customization settings.

Given that the user is on the dashboard settings page, when they select a predefined color palette, then the dashboard changes to reflect the selected colors immediately without requiring a page refresh.

User creates a custom color scheme using the color picker tool in the dashboard settings.

Given that the user opens the color picker tool, when they select their desired colors and save the changes, then the dashboard should display the new custom colors accurately across all widgets without any errors.

User examines the visual difference between the default and custom color schemes on their dashboard.

Given that the user has both the default and a custom color scheme applied, when they toggle between the two, then they should clearly see a distinct visual difference reflecting their selections in real-time.

User accesses the color palette options and views the descriptions of each predefined palette.

Given that the user is in the color palette selection menu, when they hover over each predefined color palette, then a description tooltip should appear, detailing the themes and their intended purposes.

User attempts to save a color scheme with an invalid color format.

Given that the user applied an invalid color format in the color picker, when they attempt to save the changes, then they should receive an error message indicating the color format is invalid and the changes should not be saved.

User checks whether the selected color scheme persists after refreshing the dashboard.

Given that the user selects a custom color scheme and refreshes the dashboard, when the dashboard loads, then the selected custom color scheme should still be active and reflected in the dashboard layout.

User seeks to reset their dashboard to the default color settings after applying custom colors.

Given that the user has applied a custom color scheme, when they click the 'Reset to Default' button in the settings, then the dashboard should revert to the original default color scheme without any manual adjustments required.

Real-time Preview of Color Changes

User Story: As a user, I want to see a real-time preview of my color changes on the dashboard so that I can make adjustments easily and see the results immediately.
Description: To enhance the user experience during customization, this requirement involves creating a real-time preview feature. When users select colors for their dashboard widgets, they should immediately see how those changes will look without needing to save or refresh. This functionality aims to provide instant feedback and instill confidence in users' design choices, ultimately promoting greater satisfaction and frequent use of the customization feature.
Acceptance Criteria: User selects a color from the color palette for a specific dashboard widget.

Given the user is on the dashboard customization page, when the user selects a color for a widget, then the widget should update its color in real-time without any delay or need for refreshing the page.

User changes colors multiple times for a single widget.

Given the user is customizing a dashboard widget, when the user selects and changes colors multiple times, then all color changes should reflect immediately, maintaining the last selected color until the user decides to save.

User reverts to the original color after making changes.

Given the user has made a color change to a widget, when the user clicks the 'Revert' option, then the widget should return to its original color immediately without any delay.

User views multiple widgets while making color changes.

Given the user is customizing colors of multiple dashboard widgets, when the user selects a color for one widget, then all other widgets should maintain their current states without interference, and the changes should appear in real-time for the selected widget only.

User attempts to select a color that has been previously used for a different widget.

Given the user is customizing a dashboard, when the user selects a previously used color from the palette, then the user should receive an indication (e.g., a highlight or message) that this color is already in use on another widget.

User opens and closes the customization menu during color selection.

Given the user is currently selecting a color for a widget, when the user closes and then reopens the customization menu, then the previously selected color should still be displayed for that widget without any need for re-selection.

User customizes colors and navigates away from the dashboard.

Given the user has customized the color of a dashboard widget and then navigates away from the dashboard to another page, when they return to the dashboard, the widget should retain the last selected color seen before leaving the page.

Save and Reapply Custom Color Schemes

User Story: As a user, I want to save my custom color schemes so that I can quickly switch back to my preferred settings anytime without redoing my work.
Description: This requirement entails allowing users to save their customized color schemes and effortlessly reapply them on demand. Users should have the option to create multiple saved color schemes for different purposes or occasions. This will enable users to maintain consistency in their visual branding across different presentations or reports, thereby reinforcing brand identity while streamlining the workflow when switching between different styles is needed.
Acceptance Criteria: User stores a custom color scheme for the first time after setting their preferred colors for dashboard widgets.

Given the user has selected colors for each widget, when they click 'Save Color Scheme', then a new color scheme should be saved, and the user should receive a confirmation message.

User attempts to reapply a previously saved color scheme to their dashboard widgets.

Given the user has multiple saved color schemes, when they select a saved color scheme and click 'Apply', then the dashboard should reflect the selected color scheme immediately, maintaining the user's previous selections unchanged.

User saves a custom color scheme with a specific name and intends to use it later for consistency across reports.

Given the user names their saved color scheme, when they access the saved schemes, then the name should be displayed in a list allowing the user to select it for future use.

User edits an existing saved color scheme and saves the changes.

Given the user modifies an existing color scheme, when they click 'Save Changes', then the updated color scheme should overwrite the previous one and a success message should appear to confirm the update.

User deletes an unwanted saved color scheme from their list of saved schemes.

Given the user has a list of saved color schemes, when they select a color scheme to delete and confirm the action, then the selected color scheme should be removed from the list and a deletion confirmation should appear.

User has multiple color schemes and wants to view them before deciding which one to apply.

Given the user has saved multiple color schemes, when they navigate to the color schemes section, then all saved schemes should be visually listed with their corresponding colors for easy identification.

Accessibility Considerations

User Story: As a user with color vision deficiency, I want the custom color schemes to include accessible options so that I can use the dashboard effectively and easily identify important metrics.
Description: This requirement aims to ensure that the custom color schemes feature adheres to accessibility standards, allowing users with color vision deficiencies to successfully interact with the dashboard. Additional features may include options for high contrast colors or text labels accompanying colors. Implementing this will ensure inclusivity, enabling all users to benefit from the customization feature and enhancing the overall usability of the SalesMap AI platform.
Acceptance Criteria: Testing Color Contrasts for Dashboard Customization

Given a user with color vision deficiencies, when they access the custom color schemes feature, then they should be able to select and apply color combinations that meet the WCAG AA contrast ratio requirements for text and background elements.

User Experience with High Contrast Color Scheme

Given a user selecting a high contrast color scheme, when the scheme is applied to dashboard widgets, then all widgets should visually indicate data changes and highlights as per the high contrast settings without ambiguity.

Accessibility Testing with Screen Readers

Given a user utilizing a screen reader, when they navigate through the dashboard using custom color schemes, then all relevant elements should have appropriate alternative text descriptions that convey their meaning clearly without relying solely on color cues.

Validation of Text Labels with Color Codes

Given a user customizing dashboard colors, when they save their preferences, then all color-coded data points must have accompanying text labels or indicators that describe the information that they represent, ensuring clear understanding for all users.

User Feedback on Color Accessibility Features

Given a user with color vision deficiencies, when they provide feedback on the custom color scheme feature, then at least 80% of users should indicate that they feel included and can successfully navigate the dashboard using the available color options.

Implementation of Save and Reset Options

Given a user customizing their dashboard colors, when they apply changes, then they should have the option to save their customization as well as easily revert back to the original settings without losing any previous configurations.

Documentation of Accessibility Features

Given the release of the custom color schemes functionality, when users access the help documentation, then comprehensive instructions should be provided on how to utilize accessibility features, including color schemes, ensuring clarity and ease of use.

Performance Snapshot Widgets

Performance Snapshot Widgets deliver at-a-glance summaries of key performance indicators (KPIs) relevant to the user’s specific role. By providing quick visuals of essential metrics, this feature enables users to gauge sales performance instantly, facilitating timely adjustments to their strategies.

Requirements

Customizable KPI Selection

User Story: As a sales manager, I want to choose which KPIs I see in my Performance Snapshot Widget so that I can focus on the metrics that are most relevant to my sales strategy.
Description: The Customizable KPI Selection requirement allows users to personalize which key performance indicators (KPIs) are displayed on their Performance Snapshot Widgets. This feature enhances user experience by ensuring relevance and focus on metrics that matter most to different user roles. By enabling users to select and arrange their KPIs, the system becomes more intuitive and tailored. This customization fosters quicker decision-making, as users can prioritize insights that align with their specific strategies and targets, ultimately driving a more efficient sales process.
Acceptance Criteria: User selects preferred KPIs for their Performance Snapshot Widget upon first login to the platform.

Given a new user accessing the dashboard for the first time, When they navigate to the KPI selection page, Then they should be able to choose from a list of available KPIs and arrange them in order of preference before saving their selection.

User modifies their selected KPIs after initial setup.

Given a user who has previously set up their KPIs, When they click on the 'Edit KPIs' button, Then they should be able to add or remove KPIs from their selection and rearrange the order, with changes saved successfully.

User views their customized Performance Snapshot Widget after selecting KPIs.

Given a user has selected and saved their KPIs, When they return to the dashboard, Then the Performance Snapshot Widget should display the selected KPIs in the chosen order with accurate data reflecting current performance.

User attempts to select KPIs beyond the allowed limit.

Given a user is selecting KPIs for their widget, When they attempt to select more than five KPIs, Then a warning message should appear indicating they have exceeded the limit.

User encounters an error while saving their KPI selections due to technical issues.

Given a user has selected their KPIs and clicks 'Save', When there is a backend error or connectivity issue, Then an error message should be displayed, and their selections should not be saved until the issue is resolved.

User accesses help documentation regarding KPI selection customization.

Given a user is on the KPI selection page, When they click on the 'Help' icon, Then they should be directed to relevant documentation that outlines how to customize their KPIs and the benefits of doing so.

User successfully resets their KPI selections to system defaults.

Given a user has customized their KPIs, When they click the 'Reset to Default' button on the KPI selection page, Then all selected KPIs should revert to the predefined system default settings.

Real-Time Data Updates

User Story: As a sales executive, I want my Performance Snapshot Widgets to update in real-time so that I can make fast decisions based on the most current sales data available.
Description: The Real-Time Data Updates requirement ensures that the Performance Snapshot Widgets refresh automatically, providing users with the latest sales data without needing a manual refresh. This capability is crucial for maintaining accuracy and timeliness in performance reporting, enabling users to make informed decisions quickly based on current information. Timely updates enhance the utility of the widgets, as users can react promptly to changes in sales performance or market conditions, thereby optimizing their sales strategies efficiently.
Acceptance Criteria: User is logged into the SalesMap AI platform and lands on the dashboard to view the Performance Snapshot Widgets within the first hour of a sales campaign launch, expecting the data to reflect real-time updates from ongoing sales activities.

Given the user has logged into the SalesMap AI platform, When the dashboard loads, Then the Performance Snapshot Widgets must display the most current sales data without a manual refresh.

A sales manager checks the Performance Snapshot Widgets during a weekly sales meeting, looking to confirm that all KPIs have been updated in real-time since the start of the meeting.

Given the sales meeting is underway, When the performance data is refreshed using live sales data, Then all KPIs shown in the Performance Snapshot Widgets should reflect updates from the current sales activity within 5 seconds of a data change.

A user is using the SalesMap AI dashboard while on a call with a client, requiring immediate access to the most up-to-date lead scores and sales performance metrics without manually refreshing the widgets.

Given the user is on a call and the conversation pertains to lead scores, When a new lead enters the system, Then the Performance Snapshot Widgets should automatically refresh to include the updated lead score within 3 seconds of the data change.

During high-traffic sales periods, the user needs to ensure that the Performance Snapshot Widgets continue to refresh automatically to reflect ongoing changes, ensuring decision-making is based on the most accurate data.

Given it is a high-traffic sales period, When there is a change in any of the tracked KPIs, Then the Performance Snapshot Widgets should not only refresh automatically but also maintain the response time of less than 5 seconds for all updates.

A user accesses the dashboard from different devices (desktop and mobile) expecting real-time data consistency across sessions.

Given the user has logged into the SalesMap AI platform from both desktop and mobile devices, When there is an update to the sales data, Then the Performance Snapshot Widgets on both devices should reflect the exact same updated sales information within 5 seconds.

Multi-User Role Adaptability

User Story: As a team member, I want to see KPIs relevant to my specific role so that I can stay focused on tasks that directly impact my performance metrics.
Description: The Multi-User Role Adaptability requirement allows the Performance Snapshot Widgets to dynamically adjust the displayed metrics based on the user's role within the organization. For instance, a sales manager may see different KPIs compared to a marketing associate. This adaptability enhances user experience by ensuring that each user receives relevant information tailored to their responsibilities. By streamlining the data each user sees, it not only minimizes information overload but also ensures that users are more focused on their specific goals, leading to improved sales performance.
Acceptance Criteria: Sales Manager Accessing Performance Snapshot Widgets

Given a user with the role of Sales Manager, when they access the Performance Snapshot Widgets, then the displayed metrics should include total sales, sales conversion rates, and team performance metrics relevant to their role.

Marketing Associate Accessing Performance Snapshot Widgets

Given a user with the role of Marketing Associate, when they access the Performance Snapshot Widgets, then the displayed metrics should include lead generation statistics, campaign performance metrics, and marketing ROI.

Admin Customizing User Roles and Metrics

Given a user with Admin privileges, when they customize user roles in the Performance Snapshot Widgets, then they should be able to add or remove KPIs displayed to different user roles and save those settings successfully.

User Role Changes and Dashboard Updates

Given a user who has recently changed roles, when they log into the SalesMap AI platform, then the Performance Snapshot Widgets should automatically update to reflect the metrics relevant to their new role without requiring additional actions from the user.

Evaluating User Engagement with Metrics

Given the dynamically adapted Performance Snapshot Widgets, when users of different roles engage with their dashboards, then at least 75% of users report that the metrics displayed are relevant and helpful in their day-to-day activities.

Performance Snapshot Widgets Performance across Different Devices

Given a user accessing the Performance Snapshot Widgets from a mobile device, when they view the metrics, then the layout should be responsive and easy to read, maintaining clear visibility of at least three key performance indicators without scrolling.

Error Handling for Role Assignment in Performance Snapshot Widgets

Given an erroneous role assignment that does not correspond to existing user roles, when a user attempts to access the Performance Snapshot Widgets, then they should receive a clear error message indicating that their role is not recognized.

Visual Analytics Enhancements

User Story: As a data analyst, I want to have enhanced visual representations of data in my Performance Snapshot Widgets so that I can quickly analyze trends and make data-driven decisions for improving sales performance.
Description: The Visual Analytics Enhancements requirement involves improving the graphical representations of data within the Performance Snapshot Widgets. Using advanced visualization techniques like graphs, charts, and heatmaps can make it easier for users to interpret data quickly. This will enhance user engagement and understanding of trends and patterns over time. By implementing visually appealing analytics, users can better grasp complex data sets at a glance, leading to quicker insights and more strategic planning in their sales approaches.
Acceptance Criteria: As a sales manager, I want to view my team’s monthly sales performance through intuitive graphs and charts in the Performance Snapshot Widgets to quickly assess areas for improvement and strategize effectively for the upcoming month.

Given that I have access to the Performance Snapshot Widgets, when I select the 'Monthly Sales Performance' option, then I should see a bar graph representing total sales by each team member, with colors indicating high, medium, and low performance.

As a marketing professional, I need to analyze the effectiveness of my recent sales campaigns using heatmaps in the Performance Snapshot Widgets, so I can identify regions where campaigns performed exceptionally well or poorly.

Given that I am viewing the Performance Snapshot Widgets, when I select the 'Campaign Effectiveness' option, then I should see a heatmap displaying the performance of campaigns across different geographic regions with appropriate color gradients (red for low performance and green for high).

As a sales executive, I want to have quick access to key performance indicators (KPIs) such as conversion rates and average deal sizes through compelling visuals that allow me to track my goals at a glance.

Given that I have opened the Performance Snapshot Widgets, when I navigate to the 'Key Metrics' section, then I should see a pie chart that clearly illustrates my conversion rate compared to the team average, along with a separate line graph tracking average deal sizes over the last quarter.

As a business owner, I need to ensure that all important KPIs are visually represented on the dashboard so that I can make informed decisions about my sales strategies based on comprehensive data assessments.

Given that I am accessing the main dashboard, when I browse the Performance Snapshot Widgets, then all crucial KPIs should be displayed visually, including sales growth, customer acquisition rates, and churn rates, with the ability to hover over elements for detailed data insights.

As a team leader, I want to customize the Performance Snapshot Widgets to display the most relevant metrics for my weekly meetings, ensuring the data presented is directly tied to my team's targets and initiatives.

Given that I have permission to customize the dashboard, when I go to the settings of the Performance Snapshot Widgets, then I should be able to select and rearrange the widgets based on metrics most pertinent to my team's objectives and have my new layout saved for future use.

Historical Performance Comparison

User Story: As a sales director, I want to compare current KPIs with historical performance data to evaluate the effectiveness of our sales initiatives over time so that I can adjust our strategies accordingly.
Description: The Historical Performance Comparison requirement allows users to view their current KPIs in relation to historical performance metrics. This feature enables users to analyze trends over time and gauge the effectiveness of their sales strategies. By providing a comparative view, users can identify areas of improvement and track the impact of their efforts over different periods. This capability not only enhances strategic planning but also empowers users with insights necessary for making informed decisions to enhance future performance.
Acceptance Criteria: User accesses the Historical Performance Comparison feature through the Performance Snapshot Widgets on their dashboard.

Given that the user is logged in, when they select the Historical Performance Comparison option, then they should see a visual representation of KPIs for at least the last three quarters.

User compares current KPIs with previous periods to assess performance improvements.

Given that the user has selected a KPI to compare, when they view the comparison chart, then they should see historical data points clearly marked and differentiated by color for ease of analysis.

User analyzes trends over time using the Historical Performance Comparison feature.

Given that the user has activated the Historical Performance Comparison, when they look at the trend line, then they should be able to toggle between different time frames (monthly, quarterly, yearly) to adjust the view.

User modifies the parameters of the Historical Performance Comparison to generate customized reports.

Given that the user interacts with the filter options, when they choose specific KPIs and date ranges, then the data displayed should reflect only those selections and update in real time.

User wants to export the historical performance comparison data for further analysis outside the platform.

Given that the historical comparison data is displayed, when the user clicks the export button, then the data should download in a .csv format without any loss of data integrity.

User interacts with tooltip hints for clarification on KPIs shown in the Historical Performance Comparison.

Given that the user hovers over any KPI on the historical performance comparison chart, when they do so, then a tooltip should display the definition and significance of that KPI.

User seeks to understand the significance of trends displayed in the Historical Performance Comparison.

Given that the user views the performance metrics, when they access the help section, then they should find comprehensive guides explaining how to interpret trends and make data-driven decisions based on them.

Widget Sharing Functionality

Widget Sharing Functionality enables users to share their customized widgets with team members or departments. This collaborative feature fosters a shared understanding of key metrics across the organization, enhancing cohesion and improving strategic alignment within sales efforts.

Requirements

Real-time Widget Collaboration

User Story: As a sales manager, I want to collaborate in real-time with my team on widget configurations so that we can optimize our sales strategies together without delays in communication.
Description: The Real-time Widget Collaboration requirement enables users to interact with shared widgets in real-time, allowing multiple team members to view and edit widget configurations simultaneously. This functionality promotes teamwork by providing immediate feedback and fostering a collaborative environment. Incorporating live commenting and chat features will further enhance communication among team members as they work together on optimizing sales strategies. This requirement is crucial for enhancing engagement and ensuring that all relevant parties can contribute to widget development, leading to more effective sales processes and improved decision-making outcomes.
Acceptance Criteria: Real-time collaboration in editing a shared sales performance widget amongst the sales team during a weekly strategy meeting.

Given that multiple users have access to the shared sales performance widget, when one user updates a metric value, then all other users should see that update reflected in real-time without needing to refresh their view.

Implementation of live commenting functionality while collaborating on a new lead scoring widget during a brainstorming session.

Given that a user is editing the lead scoring widget, when they add a live comment, then the comment should appear within the widget interface for all collaborating users immediately, allowing for responsive discussion.

Using the widget collaboration feature to gather feedback on a newly designed sales dashboard widget from team members across different departments.

Given that a user shares a newly designed sales dashboard widget, when team members access the widget, then they should be able to submit feedback or suggestions via a comment section that records timestamps and user identification.

Multiple users interacting with a shared conversion tracking widget during a product launch review meeting.

Given that the conversion tracking widget is being used in a review meeting, when one user adjusts the display settings or filters, then all attending users should see these changes apply immediately to ensure everyone is on the same page.

Assessing the usability of collaborative editing features as a new sales strategy widget is shared among the marketing and sales teams.

Given that users from both sales and marketing departments have access to the shared strategy widget, when they make edits or changes simultaneously, then the system must prevent conflicting changes and notify users of any discrepancies before finalizing.

Widget Permissions Management

User Story: As an admin, I want to manage widget access permissions so that I can control who can see or edit sensitive sales data, ensuring security and proper collaboration.
Description: The Widget Permissions Management requirement establishes a role-based access control system for users sharing widgets. This will allow administrators to define who can view, edit, or share certain widgets based on their roles within the organization. By ensuring that sensitive data is only accessible to authorized personnel, this requirement enhances security and confidentiality of strategic insights. Implementing a permissions management feature will help maintain data integrity while promoting responsible sharing practices among team members.
Acceptance Criteria: As an administrator, I want to set specific viewing permissions for different user roles, so that I can control who has access to sensitive widget data.

Given that I am logged in as an administrator, when I navigate to the widget permissions settings and assign viewing access to a role, then users with that role should only be able to view the widgets specified, without the ability to edit or share them.

As a user with edit permissions, I need to modify widget metrics and settings, ensuring that changes are only saved if I have the required role.

Given that I am logged in as a user with edit permissions, when I modify the metrics of a widget and save the changes, then those changes should be reflected in the widget without any authorization errors, and users without edit permissions should not see this option.

As a team member wanting to share a widget with my department, I must ensure that I can only share it if I have the correct permissions set by the administrator.

Given that I am logged in as a user who tries to share a widget, when I initiate the sharing process, then I should only be able to share the widget if my permissions allow it, otherwise I receive an error message indicating my lack of permissions.

As an administrator, I need to revoke access to certain widgets, ensuring that no unauthorized users can view them after revocation.

Given that I am logged in as an administrator, when I revoke permissions for a user role from accessing a specific widget, then users assigned to that role should no longer see the widget in their dashboard.

As a user, I want to view a notification if my access to a widget is revoked, so I am aware of the changes to my permissions.

Given that I am logged in as a user who previously had access to a widget, when the administrator revokes my access, then I should receive an email notification and an in-app alert informing me of the change in my permissions.

As an administrator, I want to audit the permissions assigned to various widgets to ensure compliance with internal data security policies.

Given that I am logged in as an administrator, when I access the permissions audit feature, then I should be able to generate a report listing all widgets along with their corresponding permissions and roles, validated against the internal data security policies.

As a user, I want to understand the permissions associated with a widget before trying to access it, so that I can identify any potential access issues.

Given that I am logged in as a user, when I view a widget's details, then I should see a clear indication of the access permissions and my role's relation to these permissions, allowing me to understand my access level.

Customizable Widget Templates

User Story: As a sales representative, I want to create and reuse my own widget templates so that I can save time and maintain consistency in my reporting.
Description: The Customizable Widget Templates requirement allows users to create, save, and reuse widget templates tailored to their specific needs. This feature will streamline the creation of new widgets, reducing time spent on setup and ensuring consistency in reporting formats across different departments. It empowers users to develop personalized metrics that align closely with their sales strategies, ultimately driving better performance and results. The introduction of template customization will also enhance the user experience by providing them with tools that fit their distinctive workflow.
Acceptance Criteria: User creates a customizable widget template for tracking sales performance metrics based on specific criteria relevant to their department.

Given the user is logged into SalesMap AI, when they navigate to the widget creation section and select 'Create Template', then they should be able to customize fields (like metrics, date range, and visualization type) and save the template for future use.

A user shares a previously created customizable widget template with their team members through the Widget Sharing Functionality.

Given the user has created a customizable widget template, when they select the 'Share' option and enter the email addresses of their team members, then those team members should receive an email notification with access to the shared template.

A user edits an existing customizable widget template to update the metrics that are relevant to new sales strategies.

Given the user has accessed an existing customizable widget template, when they make changes to the metrics and save the template, then the changes should be reflected in both the template and any widgets created from it.

A user attempts to create a widget template but leaves mandatory fields blank.

Given the user is in the widget template creation form, when they try to save the template without filling in the required fields, then they should receive an error message indicating which fields need to be completed.

A user utilizes a customized widget template to generate a report for their sales performance over the last quarter.

Given the user selects a customized widget template, when they input the required date range and metrics, then the system should generate a report that accurately reflects the data based on the selected parameters.

Historical Data Comparison

User Story: As a sales analyst, I want to compare current sales metrics with historical data so that I can identify trends and make data-driven decisions based on performance changes over time.
Description: The Historical Data Comparison requirement allows users to compare current widget metrics against historical data over specified time periods. This feature is essential for identifying trends and measuring performance improvements or declines, thereby facilitating informed strategic adjustments. By providing visual graphs and reports, users can easily interpret data and derive actionable insights from their comparative analysis. It serves to enhance the analytical capabilities of SalesMap AI, enabling users to respond proactively to market changes.
Acceptance Criteria: User Accessing Historical Data Comparison for the First Time

Given a user is logged into the SalesMap AI platform, when they navigate to the Historical Data Comparison feature, then they should see an interface displaying options to select specific widgets and time periods for comparison.

User Compares Current Metrics with Historical Data

Given a user has selected a widget and specified a historical time period, when they initiate the comparison, then the system should generate and display visual graphs comparing current metrics against the selected historical data, along with key trend insights.

User Generates and Saves a Report Based on Comparison Results

Given a user has completed a comparison, when they choose the option to generate a report, then the system should create a downloadable report that includes graphs, key insights, and a summary of the comparative analysis.

User Shares Historical Data Comparison Findings with Team Members

Given a user has access to the Historical Data Comparison feature, when they select a sharing option, then they must be able to send an invitation or a report link to specified team members via email directly from the dashboard.

User Receives Real-Time Notifications on Significant Metric Changes

Given a user has setup alerts on specific metrics, when there is a significant change in comparison results during a specified time period, then the user should receive a real-time notification alerting them of the change via their chosen notification method.

User Filters Historical Data by Custom Parameters

Given a user is on the Historical Data Comparison interface, when they apply custom filters for specific metrics or segments, then the comparison results should update dynamically to reflect the newly applied filters without delay.

Integrated Feedback System

User Story: As a user, I want to submit feedback on widget functionality so that I can help improve the tools I rely on for my sales processes.
Description: The Integrated Feedback System requirement enables users to submit feedback or suggestions directly related to widget performance or usability. This feature will be instrumental in gathering user insights to inform ongoing product improvements and development. By integrating feedback functionalities into the widget sharing interface, users can easily communicate their experiences, fostering a culture of continuous improvement within the organization. This requirement aligns with the goal of ensuring that the platform evolves based on user needs and enhances overall satisfaction.
Acceptance Criteria: User submits feedback on widget performance after utilizing it for a week in their sales workflow.

Given the user is logged into the SalesMap AI platform, when they navigate to the widget sharing interface and select 'Submit Feedback', then they should be presented with a feedback form to rate widget performance on a scale of 1 to 5 and provide comments.

User attempts to submit feedback without filling out mandatory fields in the feedback form.

Given the user is on the feedback form and leaves mandatory fields blank, when they select 'Submit', then an error message should display, prompting the user to complete all required fields before submission.

User receives a confirmation after successfully submitting feedback on a widget.

Given the user has filled out the feedback form correctly and selects 'Submit', when the feedback is submitted, then the user should see a confirmation message indicating their feedback has been received and will be reviewed.

Multiple users submit feedback to gauge collective insights on a widget's usability.

Given multiple users have access to a shared widget, when each user submits unique feedback through the feedback form, then all submissions should be recorded and accessible in the feedback analytics dashboard for review.

User reviews past feedback submitted on a specific widget's performance.

Given the user is on the widget sharing interface, when they select a specific widget and choose to view feedback history, then they should see a chronological list of all feedback submissions related to that widget.

User wants to report a technical issue experienced while using a widget.

Given the user is logged in and encounters a technical issue with a widget, when they click 'Report Issue' on the feedback interface, then they should be able to describe the issue and submit it for further investigation.

Real-Time Scoring Adjustments

This feature automatically updates lead scores in real-time, incorporating new data from interactions and engagements as they happen. By continuously refining lead scores, users can immediately prioritize follow-ups based on the most current information, reducing the risk of missing valuable opportunities and enhancing conversion rates.

Requirements

Dynamic Lead Scoring

User Story: As a sales representative, I want lead scores to be updated in real-time so that I can prioritize my follow-ups based on the most current engagement data and maximize my closing rates.
Description: This requirement involves creating a system that actively monitors and adjusts lead scores based on real-time data inputs. The functionality will allow the application to analyze various engagement metrics, such as email opens, link clicks, and social media interactions, as they happen. By integrating this real-time feedback loop into the lead scoring mechanism, SalesMap AI enhances its ability to provide actionable insights for sales teams, allowing for immediate prioritization of leads that show a higher propensity to convert. This continuous learning approach not only improves the accuracy of lead scoring but also optimizes the follow-up strategies of sales personnel, resulting in better engagement rates and increased sales opportunities.
Acceptance Criteria: Real-time updates of lead scores based on engagement metrics from email interactions.

Given a lead has opened an email, when the email open event is recorded, then the lead's score should increase by the predefined value reflecting engagement.

Adjustment of lead scores due to social media interaction analytics, such as likes or shares.

Given a lead engages with social media content (likes or shares), when the interaction is logged, then the lead's score should be updated instantaneously to reflect this new engagement level.

Immediate prioritization of leads for sales personnel based on updated scoring metrics.

Given the lead scoring system has received new input data, when the scores are recalibrated, then the sales dashboard should display the leads sorted in order of their updated scores, allowing for immediate follow-up prioritization.

Integration with CRM to ensure lead scores reflect overall engagement across multiple platforms.

Given a lead's interaction data from both the web and CRM is available, when the system processes this data, then the lead score should accurately incorporate all engagement metrics from these integrated platforms.

User notifications when a lead's score reaches a certain threshold indicating high conversion potential.

Given a lead's score has increased due to recent engagements, when the score crosses a designated threshold, then the user should receive a notification alerting them to follow up with that lead.

Assessment of lead scoring adjustment impact on overall sales conversion rates.

Given the lead scoring system is in use, when a report is generated after a specified period, then the report should show improved conversion rates correlated with the implementation of real-time scoring adjustments.

Performance metrics evaluating the effectiveness of the dynamic lead scoring mechanism over time.

Given the dynamic lead scoring feature has been active for a set duration, when performance metrics are reviewed, then they should demonstrate a positive trend in lead conversion rates compared to the previous scoring method.

Historical Engagement Analytics

User Story: As a sales manager, I want to visualize historical engagement metrics for each lead so that I can identify successful patterns and optimize my team's outreach strategies accordingly.
Description: This requirement entails developing a feature that tracks and analyzes the historical engagement data of leads over time. By storing and analyzing past interaction data, SalesMap AI can identify trends and patterns in lead behavior, providing valuable insights into which engagements have historically resulted in conversions. This data will inform better decision-making for future engagements, allowing sales teams to tailor their strategies based on historical performance, thereby improving the effectiveness of their outreach efforts and enhancing overall sales performance.
Acceptance Criteria: Tracking and Analyzing Lead Engagement Data Over Time

Given a lead with historical engagement data, when a user accesses the lead profile, then the system should display a complete history of all interactions with timestamps and engagement types.

Identifying Engagement Trends for Conversion Analysis

Given the historical engagement data of multiple leads, when the user generates a report, then the report should highlight at least three patterns or trends that correlate with successful conversions.

Integrating Historical Engagement Insights with Real-Time Scoring

Given that a lead's real-time score has been updated, when a user views the lead's analytics, then the historical engagement data should reflect any changes in lead score based on the latest interactions recorded.

Tailoring Sales Strategies Based on Historical Performance

Given historical engagement insights, when a sales team creates a follow-up strategy, then the strategy should be informed by at least two different historical engagement metrics that indicated previous success.

Ensuring Data Accuracy and Reliability

Given the historical engagement data collected, when the system performs an accuracy check, then it should confirm that all entries are recorded without errors and within an acceptable time frame after each interaction.

User Accessibility and Navigation of Historical Data

Given a sales representative accessing the historical engagement analytics, when they navigate the tool, then they should be able to seamlessly filter and sort data by date range, lead score, and engagement type within three clicks.

Training and Support for Users on Historical Analytics Features

Given that the historical engagement analytics feature has been implemented, when the training session is completed, then at least 80% of participants should demonstrate proficiency in using the feature through a post-training assessment.

Automated Alert System for Lead Actions

User Story: As a sales representative, I want to receive automated alerts when leads perform significant actions so that I can follow up instantly and capitalize on their interest.
Description: This requirement involves creating an automated alert system that notifies sales representatives when significant actions are taken by leads, such as completing a form, downloading a resource, or engaging extensively with the website. The system will utilize real-time data triggers to send alerts via email or push notifications, ensuring that sales teams can promptly follow up with interested leads. This immediate response capability allows for timely engagement, increases the chances of conversion, and enhances the relationship between sales teams and potential customers.
Acceptance Criteria: Sales representatives receive an alert when a lead fills out an online form requesting more information about a product.

Given a lead completes a product inquiry form, when the form is submitted, then the system sends an email alert to the assigned sales representative within 2 minutes.

Sales representatives are notified when a lead downloads a resource from the company website.

Given a lead downloads a resource, when the download is completed, then the system triggers a push notification to the assigned sales representative within 3 minutes.

Sales representatives are alerted when a lead visits the pricing page on the website multiple times in a single session.

Given a lead visits the pricing page more than 3 times in a 15-minute window, when the condition is met, then an alert is sent to the assigned sales representative within 5 minutes.

Sales representatives are notified when a lead engages with a targeted email campaign by clicking on links more than once.

Given a lead clicks on links in a targeted marketing email more than twice, when this threshold is reached, then the system sends an immediate email alert to the assigned sales representative.

Sales representatives receive alerts when leads spend over 10 minutes on high-value product pages.

Given a lead spends more than 10 minutes on a high-value product page, when the session ends, then the system generates and sends an alert in real-time to the assigned sales representative.

Sales representatives are notified of leads who interact with chatbot services on the company website.

Given a lead engages with the chatbot for more than 5 interactions, when the interaction metric is recorded, then an alert is sent to the assigned sales representative within 3 minutes.

Sales representatives receive updates on lead activity based on scoring thresholds that trigger alerts.

Given a lead's score increases by a predetermined threshold due to new engagement activities, when the score reaches the threshold, then an alert notification is sent to the assigned sales representative within 2 minutes.

Customizable Lead Scoring Parameters

User Story: As a marketing manager, I want to customize the lead scoring parameters so that I can adjust the scoring criteria to fit our unique sales process and improve conversion rates.
Description: This requirement focuses on providing users with the ability to customize lead scoring parameters based on their specific sales strategies and business needs. Users will be able to define their own criteria for scoring leads, including weighting various interactions and engagements differently. This feature promotes flexibility in the scoring process, ensuring that the lead scoring mechanism aligns with the unique characteristics of each user's sales environment. By enabling customization, SalesMap AI empowers businesses to adopt the most effective scoring strategies to optimize lead prioritization.
Acceptance Criteria: User Customization of Lead Scoring Parameters in SalesMap AI

Given a SalesMap AI user in the lead scoring settings, when they select custom options and define scoring parameters, then the system should save and apply these new parameters immediately to the lead scoring model without any errors.

Real-Time Updates of Lead Scores Based on User-Defined Parameters

Given a user-defined lead scoring configuration, when a new interaction occurs, then the lead score should update in real-time according to the latest data and the pre-determined scoring weights that the user has set.

Effectiveness of Custom Scoring in Prioritizing Leads

Given a user has set customized lead scoring parameters, when a user reviews their lead list, then leads should be prioritized according to the defined scoring criteria, and the top leads should correlate with higher conversion rates in actual follow-ups.

Validation of Default Scoring Parameter Options for New Users

Given a new user who has not customized lead scoring parameters, when they access the lead scoring feature for the first time, then the system should display default scoring parameters and explanations for each parameter and allow the user to modify them easily.

User Feedback on Customization Experience

Given a user who has customized their lead scoring parameters, when they provide feedback on the process, then the system should capture feedback efficiently, and the average feedback rating should be tracked for improvements.

Testing the Flexibility of Scoring Weight Adjustments

Given a user who has adjusted the weights of their lead scoring parameters, when they view the scoring impact report, then the system should reflect changes in lead prioritization and provide clear analytics on how those adjustments have affected engagement.

Real-Time Dashboard Integration

User Story: As a sales executive, I want to view real-time updates on lead scores and activities on my dashboard so that I can make informed decisions and prioritize my tasks efficiently.
Description: This requirement involves enhancing the SalesMap AI user interface by integrating a real-time dashboard that displays updated lead scores and engagement metrics. The dashboard will provide visual representations of lead activities, allowing sales teams to quickly assess the status of their leads at a glance. This integration will facilitate quicker decision-making and more efficient follow-up actions, as users will have immediate access to the most pertinent data without needing to navigate through multiple screens or reports.
Acceptance Criteria: Real-Time Display of Lead Scores and Engagement Metrics

Given that a user is on the SalesMap AI dashboard, when new data regarding lead interactions is received, then the lead scores and engagement metrics should automatically update within 5 seconds without requiring a page refresh.

Visualization of Lead Activity Trends

Given the user accesses the dashboard, when they view the lead activity over the past 30 days, then the dashboard should display visual graphs (such as line or bar graphs) representing the lead scores and engagement metrics in an easily interpretable format.

User Notification for Lead Score Changes

Given that a lead's score has changed due to new engagement data, when the score updates, then the user should receive a notification indicating which lead's score has changed and the value of the new score.

Accessibility of Historical Engagement Data

Given that a user wants to review past lead interactions, when they select a lead from the dashboard, then the user should be able to view the last three months of engagement data including dates, types of engagement, and previous lead scores.

Real-Time Follow-Up Prioritization

Given that lead scores are updated in real-time, when a user reviews the dashboard, then the leads should be automatically sorted by the highest score at the top for prioritized follow-up actions.

Seamless Integration with CRM Systems

Given that the user has connected their CRM to SalesMap AI, when the real-time dashboard displays lead information, then all lead data should accurately reflect the latest updates from the CRM without discrepancies.

User-Friendly Interface for Dashboard Navigation

Given that a user accesses the real-time dashboard, when they navigate the dashboard, then they should be able to easily understand how to access different lead metrics and visualizations without training or assistance.

Predictive Engagement Recommendations

User Story: As a sales strategist, I want to receive predictive engagement recommendations for my leads so that I can tailor my approach and improve the likelihood of conversions.
Description: This requirement includes the development of a predictive analytics feature that recommends optimal engagement strategies for each lead based on their scoring and historical interaction data. By leveraging machine learning algorithms, SalesMap AI will analyze past interactions and suggest the best communication methods and timing to engage each lead effectively. This recommendation engine aims to enhance the personalization of outreach efforts and increase engagement rates, as sales teams will be able to approach leads with tailored strategies that resonate with their specific interests and behaviors.
Acceptance Criteria: Salesperson utilizes the predictive engagement recommendations feature during a weekly sales meeting to strategize outreach efforts for the upcoming week.

Given the lead scoring data and interaction history are available, when the salesperson accesses the predictive engagement recommendations, then the system should display tailored engagement strategies for each lead ranked by score and predicted engagement likelihood.

A sales representative interacts with a lead who has a low engagement score, and the predictive analytics feature proposes a new strategy.

Given the lead's interaction history and current engagement score, when the sales representative reviews the predictive analytics recommendations, then the proposed strategies should reflect a higher priority contact method that aligns with identified lead preferences.

The marketing team reviews the overall effectiveness of the predictive engagement recommendations over the last quarter to improve strategies.

Given the engagement rates and conversion successes after implementing the predictive recommendations, when the marketing team analyzes the data, then there should be at least a 20% increase in lead engagement rates and a 15% rise in conversion rates.

During a customer feedback session, sales teams share their experiences using the predictive engagement recommendations feature.

Given that sales teams have used the predictive engagement recommendations for at least one month, when the feedback is collected, then at least 80% of the sales team members should indicate that the recommendations have positively impacted their ability to engage leads effectively.

The system updates lead engagement scores in real-time as new interaction data becomes available that informs the recommendations.

Given that new interaction data is logged in the CRM, when a lead's engagement data is updated, then the predictive engagement recommendations should reflect these changes within 5 minutes of data entry.

Sales management wants to ensure that the predictive engagement recommendations align with company sales goals and strategies.

Given the company’s sales objectives and strategy documents, when the predictive engagement recommendations are reviewed, then at least 90% of the recommended strategies should align with the stated objectives and recommended practices.

Customizable Scoring Criteria

Users can define and customize the parameters that influence lead scoring, including demographic factors, engagement levels, and buying signals. This flexibility allows sales teams to align the scoring system with their unique sales strategies, ensuring that their prioritization reflects their specific targets and market dynamics.

Requirements

Dynamic Lead Score Adjustment

User Story: As a sales manager, I want to adjust lead scoring parameters dynamically so that I can quickly respond to market changes and improve our sales team's effectiveness in targeting high-potential leads.
Description: This requirement involves allowing users to have the ability to dynamically adjust lead scoring based on changes in market conditions or business strategies. Users can modify scoring criteria effortlessly through an intuitive interface, ensuring real-time adaptation to emerging trends and customer behaviors. The system will provide recommendations based on historical data and predictive analytics to facilitate these adjustments. By enabling customizable lead scoring, the platform ensures alignment with organizational goals, enhancing the precision of lead prioritization and ultimately improving conversion rates.
Acceptance Criteria: User adjusts lead scoring criteria to prioritize demographic factors during a quarterly sales strategy meeting.

Given that the user is accessing the scoring configuration interface, when they modify the demographic weighting and save the changes, then the lead scoring system should reflect these updates in real-time for all relevant leads.

A sales manager uses the intuitive interface to adapt lead scoring due to a sudden market trend detected by predictive analytics.

Given that predictive analytics suggests a change in market conditions, when the user reviews the recommendation and implements adjustments to the scoring criteria, then the adjustments should be logged and displayed in the user dashboard with timestamps.

The user wants to refine lead scoring by adjusting engagement levels based on recent customer interaction data.

Given that the user has access to historical engagement data, when they select the engagement criteria and modify the scoring parameters, then the updated scoring should be automatically recalibrated for leads based on the new engagement levels.

A salesperson seeks to ensure that their customized scoring criteria align with the overall organizational strategy.

Given that the salesperson is reviewing their lead scoring settings, when they compare their criteria against organizational sales goals, then the system should provide insights or suggestions for alignment based on predefined metrics.

Multiple users are collaborating to adjust lead scoring criteria in response to a strategic pivot in their business approach.

Given that multiple users have access to the scoring configuration, when one user makes changes to the criteria, then the system should notify other users of the update and log the change history for accountability.

A user evaluates the effectiveness of previous lead scoring adjustments after implementing a new campaign strategy.

Given that the user accesses the performance analytics dashboard, when they select the timeframe post-adjustment, then the dashboard should display conversion rates and lead performance metrics that directly correspond to the updated scoring criteria.

Historical Scoring Insights

User Story: As a sales analyst, I want to review historical lead scoring data so that I can understand the effectiveness of past strategies and make data-driven decisions for future lead prioritization.
Description: This requirement entails implementing a feature that offers users insights into historical lead scoring changes and their impacts on conversion rates. The functionality will allow users to view trends over time, analyze how modifications in scoring criteria have affected lead prioritization and sales outcomes, and utilize this data to make informed decisions for future scoring adjustments. By providing these insights, the platform aids users in refining their strategies and enhances the effectiveness of lead management practices.
Acceptance Criteria: User views and analyzes historical lead scoring trends over a selected time range to assess the effectiveness of their scoring modifications on conversion rates.

Given the user navigates to the Historical Scoring Insights section, when they select a specific date range, then they can view a graphical representation of lead scores and corresponding conversion rates for that period.

Users modify scoring criteria and want to see how those changes have historically impacted lead prioritization.

Given the user has changed scoring criteria, when the user accesses historical data, then they should be able to see a comparison of lead prioritization before and after the changes.

A sales manager wants to generate insights on the effectiveness of scoring parameters based on past campaigns to inform future strategies.

Given the user selects a specific campaign, when viewing the historical scoring insights, then the user should see an analysis of scoring parameters used in that campaign and their respective conversion outcomes.

A user wants to understand the correlation between specific demographic factors and conversion rates over time.

Given the user selects demographic-based scoring criteria, when accessing historical insights, then the system should display a report showing conversion rates correlated with those specific demographics over the selected time period.

Users want to export historical lead scoring insights for further analysis in external tools.

Given the user is viewing historical scoring insights, when they click the export button, then the system should generate and download a CSV file containing all displayed historical data and trends.

Users examine the impact of engagement level changes on historical lead scoring and conversion rates.

Given the user filters insights by engagement levels, when viewing the historical data, then they should find a clear breakdown of how engagement levels have fluctuated and their impact on conversion rates over time.

A user is analyzing the average time leads spent in each scoring category before conversion.

Given the user is in the Historical Scoring Insights section, when they request a report on lead durations, then the system should display the average time leads spent categorized by scoring tier before conversion.

Integration with Third-party Data Sources

User Story: As a sales representative, I want to access third-party data sources within SalesMap so that I can enrich my lead scoring process with additional insights and improve my chances of closing deals.
Description: This requirement focuses on integrating SalesMap AI with various third-party data sources to enrich lead scoring criteria. By connecting to platforms such as social media, CRM systems, and data providers, users can import additional data points that significantly enhance the AI's decision-making in scoring leads. Enhanced scoring criteria will empower sales teams to prioritize leads based on a comprehensive view of potential clients, considering factors that extend beyond immediate engagement metrics. This integration will ultimately support more strategic lead qualification and targeted marketing efforts.
Acceptance Criteria: Integration with CRM System to Enhance Lead Scoring Criteria

Given a user has access to the SalesMap AI platform, when they integrate their CRM system, then they should be able to import lead data seamlessly, ensuring that all relevant lead information is accurately reflected in the scoring criteria.

Real-time Data Import from Social Media Platforms

Given a user wants to enhance lead scoring with social media engagement data, when they configure the integration with social media platforms, then the system should pull real-time engagement metrics for each lead, contributing to the scoring model correctly.

Customizing Scoring Parameters Based on Third-party Data

Given that third-party data sources are integrated, when a user customizes their lead scoring criteria to include new data points, then the platform should allow them to save and apply these settings without errors, and the newly defined criteria should be reflected in the lead scoring algorithm immediately.

Validation of Imported Data from External Sources

Given a user has imported lead data from third-party sources, when they review the imported data in the scoring system, then they should see accurate representations of all data points, and any discrepancies should be flagged for review.

Lead Scoring Impact Assessment Post-Integration

Given the integration with third-party data sources is completed, when a user generates a lead score report, then the report should show improved lead prioritization metrics compared to the previous scoring models, indicating successful data integration.

User-Friendly Interface for Data Integration Configuration

Given a user intends to configure third-party data integrations, when they access the integration settings, then they should find an intuitive user interface that provides clear instructions and options for each integration type without confusion or technical jargon.

Testing Integration Security and Data Privacy Compliance

Given the integration with external data sources, when the system undergoes security evaluation, then it should confirm that data is securely transferred and stored, meeting all relevant privacy regulations without any vulnerabilities identified.

User Training and Support

User Story: As a new user, I want access to training resources on lead scoring customization so that I can quickly learn how to effectively use the feature and improve my sales strategy.
Description: This requirement includes the development of comprehensive training materials and support resources for users to effectively utilize and customize the lead scoring system. It will feature tutorials, best practices guides, and access to a dedicated support team to address user queries and enhance user experience. By offering robust training and continuous support, the platform aims to empower users in maximizing the capabilities of customizable lead scoring, ensuring they can fully leverage the feature to meet their unique sales objectives.
Acceptance Criteria: User Training Session for Customizable Scoring Criteria

Given a user has accessed the training module, when they complete the tutorial on customizable scoring criteria, then they should score at least 80% on the accompanying quiz to demonstrate understanding.

Access to Support Resources after Customization of Lead Scoring

Given a user has customized their lead scoring criteria, when they access the support section, then they should find at least three relevant articles and a contact method for direct support help within two clicks.

Feedback Mechanism for User Training Effectiveness

Given that user training has been delivered, when users submit feedback through the post-training survey, then at least 90% of users should indicate that the training materials were clear and helpful, rated at a 4 or higher on a 5-point scale.

Usage Metrics Tracking for Customized Scoring

Given that the user has customized their lead scoring criteria, when they check the usage metrics dashboard, then they should see analytics reflecting their scoring criteria's impact on conversion rates within 30 days of execution.

Continuous Support Accessibility for Users

Given a user has a query regarding the customizable scoring criteria, when they attempt to reach support, then they should receive a response within 24 hours indicating next steps or solutions.

Measurement of User Confidence Post-Training

Given a user has completed the training and used the customizable scoring feature, when they are asked to self-assess their confidence in using the feature, then at least 85% should report a high level of confidence, scoring 4 or 5 on a 5-point scale.

Real-time Scoring Feedback Loop

User Story: As a sales manager, I want to see real-time feedback on lead scoring changes so that I can quickly evaluate the effects of my adjustments and make necessary modifications to improve lead prioritization.
Description: This requirement proposes the implementation of a real-time feedback loop that allows users to see the immediate impacts of their scoring adjustments on lead prioritization. As users modify their scoring criteria, the system will provide instant feedback on the lead scores and rankings, enabling users to assess the effectiveness of their changes on the fly. This capability encourages a more iterative and data-driven approach to lead management, fostering continuous optimization of sales strategies based on real-time data and outcomes.
Acceptance Criteria: User adjusts the lead scoring criteria to prioritize engagement level over demographic factors to test its impact on lead rankings.

Given the user modifies the engagement level parameters, when the scoring criteria are saved, then the lead scores should be recalibrated in real-time to reflect the new parameters and the top-ranked leads should be displayed immediately.

Sales team leader wants to assess the effectiveness of newly defined scoring criteria during a live demo session with stakeholders.

Given the new scoring criteria are applied, when the user triggers a lead refresh, then the dashboard should update within 2 seconds to show the new lead scores and rankings, allowing for immediate analysis.

User modifies scoring criteria to include recent buying signals and wants to see the effect on lead prioritization before finalizing changes.

Given that the user enables buying signals as a scoring factor, when they adjust the weights and click 'Preview', then a pop-up should show the projected lead scores and rankings based on the new criteria.

A sales representative wants to review how recent changes to scoring criteria influence overall lead conversion metrics.

Given the scoring criteria have been recently updated, when the user accesses the historical performance reports, then they should see metrics displaying conversion rates for leads scored under the previous vs new criteria side by side.

User is testing scoring modifications and wants to receive alerts if there are drastic changes in lead scores post-adjustment.

Given the scoring criteria have been adjusted, when lead scores change by more than 20% for any lead, then the user should receive a notification alerting them of the significant changes in lead priority.

Sales manager is auditing changes in lead scoring from the past month and wants to visualize the trends in lead prioritization.

Given the user selects a range in the scoring history, when they generate the report, then the system should display a comparative graph showing lead score changes over time along with key scoring adjustments made.

User wants to compare lead scores before and after making adjustments to scoring criteria to validate their effectiveness.

Given the user has adjusted the scoring criteria, when they click 'Compare' on the scoring dashboard, then the system should display a side-by-side comparison of lead scores before and after the adjustments for the last 100 leads.

Predictive Scoring Models

Utilizing advanced algorithms, this feature predicts future lead behaviors based on historical data and interaction patterns. By forecasting potential conversion likelihood, users can focus their efforts on leads that are not only currently engaged but also likely to convert, optimizing their resources for maximum impact.

Requirements

Lead Behavior Prediction

User Story: As a sales representative, I want to receive predictions on which leads are most likely to convert so that I can prioritize my outreach efforts and improve my chances of closing deals.
Description: The Lead Behavior Prediction requirement focuses on leveraging historical interaction data to create algorithms that anticipate future lead actions and conversion probabilities. This feature will integrate seamlessly with the existing database, enhancing the SalesMap AI platform's capability to segment leads based on predicted behavior. It is essential for prioritizing outreach efforts on leads with the highest likelihood of conversion, which ultimately optimizes sales team resources and improves overall campaign effectiveness. Accurate predictive analytics empower users to make informed decisions, increasing conversion rates and driving revenue growth by aligning sales strategies with the most promising prospects.
Acceptance Criteria: User inputs lead interaction data into the SalesMap AI platform to utilize predictive scoring models.

Given that the user has uploaded lead interaction historical data, when they trigger the lead behavior prediction algorithm, then the system should display a list of leads ranked by predicted conversion likelihood, with scores ranging from 0 to 100.

Sales representatives are checking leads on the dashboard to prioritize their outreach.

Given that the predictive scoring model has been executed, when a sales representative views the lead list on the dashboard, then the representative should see highlighted leads with a conversion likelihood of 75% or higher as top priority leads for outreach.

The system integrates with existing databases of lead interactions to enhance predictive scoring.

Given that the system has access to the historical lead interaction database, when the predictive scoring model runs, then it should accurately incorporate the most recent three months of data for improved predictions.

Management reviews the effectiveness of the predictive scoring feature in identifying high-conversion leads.

Given that the predictive scoring model has been in use for one month, when management analyzes conversion rates, then there should be a reported increase of at least 15% in conversions from leads identified by the prediction model compared to the previous month.

Users want to receive a summary report after each run of the predictive scoring model.

Given that the user initiates a predictive scoring run, when the process is complete, then the system should automatically generate and email a report detailing the number of leads processed, average conversion likelihood, and the top five prioritized leads.

The platform needs to provide user feedback on the predictive accuracy of the leads over time.

Given that the predictive scoring model has been implemented, when users assess lead outcomes after outreach, then the system should track and report the actual conversion rates against predicted scores on a bi-weekly basis.

Real-time Lead Scoring

User Story: As a sales manager, I want to see real-time updates on lead scores so that I can quickly identify which prospects require immediate attention and adjust my team’s focus accordingly.
Description: Real-time Lead Scoring requirement enables the SalesMap AI platform to dynamically score leads based on real-time engagement data and interactions. This feature will utilize continuous updates from user activities and interactions to adjust lead scores instantly, allowing sales teams to respond to changes in prospect interest and engagement levels. By integrating this capability, the product will provide users with immediate insights into lead status, helping them to identify hot leads quickly and adjust strategies accordingly. The result will be a significant enhancement in lead management efficiency and more timely follow-ups, directly contributing to higher conversion rates.
Acceptance Criteria: Real-time scoring updates reflect lead interactions accurately and promptly.

Given a lead is engaged in a call and the user logs this interaction in the system, when the lead's scoring data is updated, then the lead score should reflect the engagement within 5 seconds.

Sales team receives notifications for critical lead score changes.

Given a lead's score increases to a defined threshold due to recent interactions, when the score change occurs, then a notification should be sent to the sales representative responsible for that lead within 2 minutes.

Users can view the updated lead scores on the dashboard in real-time.

Given that multiple leads are engaged during a sales campaign, when the user accesses the dashboard, then the lead scores displayed should be updated and accurate, reflecting the most recent interactions without needing a page refresh.

Lead scores are calculated based on predefined criteria and interaction data.

Given that a lead interacts with marketing materials via email and website visits, when the system processes these interactions, then the lead score should increase according to the established scoring algorithm within 3 seconds.

Historical lead engagement data is leveraged for real-time scoring adjustments.

Given that a lead shows an increase in engagement based on historical trends, when real-time scoring is updated, then the lead score should accurately reflect this engagement consistent with past behaviors.

Sales teams can filter leads based on real-time scores to prioritize follow-ups.

Given that multiple leads have updated scores, when the sales team applies filtering on their dashboard, then the system should display leads in order of priority based on the most recent scores.

Automated Reporting Dashboard

User Story: As a marketing director, I want an automated reporting dashboard that visualizes lead scoring, trends, and campaign performance so that I can make informed strategic decisions based on real-time data.
Description: The Automated Reporting Dashboard requirement aims to create a comprehensive, visually appealing dashboard that consolidates all predictive scoring data, market trends, and campaign performance metrics into a single view. Integrating this feature will allow users to visualize trends over time, track lead conversion probabilities, and evaluate the effectiveness of various strategies. By offering customizable reporting options, users can tailor the dashboard to their specific needs, facilitating data-driven decisions. This feature is crucial for providing stakeholders with actionable insights and helping teams align their efforts towards high-impact strategies.
Acceptance Criteria: Users can access the Automated Reporting Dashboard from the main navigation panel in the SalesMap AI interface.

Given a user is logged into the SalesMap AI platform, when they click on the 'Reporting Dashboard' link in the navigation panel, then the Automated Reporting Dashboard should open, displaying all integrated metrics and visualizations.

The Automated Reporting Dashboard must display predictive scoring data in a clear and engaging manner.

Given the Automated Reporting Dashboard is loaded, when the user views the predictive scoring section, then it should show a graphical representation of lead conversion probabilities over time, with data points clearly labeled and color-coded for easy interpretation.

Users should be able to filter the dashboard data based on specific criteria such as date range and lead status.

Given the user is viewing the Automated Reporting Dashboard, when they select a date range and lead status from the filter options, then the dashboard must update to display only the relevant data that corresponds to the selected filters.

The dashboard must allow users to export reporting data in multiple formats.

Given the Automated Reporting Dashboard is open, when the user clicks on the 'Export' button, then they should have the option to download the report in CSV, PDF, and Excel formats, ensuring the exported data reflects what is currently visible on the dashboard.

Users should be able to customize the layout and displayed metrics on the Automated Reporting Dashboard according to their preferences.

Given the user is viewing the Automated Reporting Dashboard, when they click on the 'Customize' button and adjust the displayed metrics and layout, then the dashboard should reflect these changes without requiring a page reload.

The dashboard must provide real-time updates of the predictive scoring and campaign performance metrics.

Given the Automated Reporting Dashboard is open, when new data is available in the system, then the dashboard must refresh automatically, displaying the most up-to-date metrics without any manual intervention from the user.

Each metric in the dashboard should have a tooltip that explains its significance and how it is calculated.

Given a user hovers over any metric in the Automated Reporting Dashboard, then a tooltip should appear, providing a clear explanation of what the metric represents and the methodology used to calculate it.

User Training Module

User Story: As a new user of SalesMap AI, I want access to a training module that helps me understand how to use predictive scoring effectively so that I can maximize my sales performance quickly.
Description: The User Training Module requirement will incorporate an interactive training system within SalesMap AI that guides users through the new predictive scoring features, enhancing user adoption and effectiveness. This module will include tutorials, best practice guides, and interactive sessions that demonstrate how to leverage predictive scoring for optimal sales strategies. By providing users with targeted training resources, this requirement aims to reduce the learning curve and ensure that users can effectively utilize the new capabilities of the SalesMap AI platform, ultimately leading to increased user satisfaction and better sales outcomes.
Acceptance Criteria: User accesses the User Training Module to learn about predictive scoring models.

Given the user is logged into SalesMap AI, when they navigate to the User Training Module, then they should see an overview of predictive scoring features and available training resources.

A user completes a tutorial on predictive scoring and demonstrates understanding of the feature.

Given the user has completed the tutorial, when they take the quiz at the end, then they must score at least 80% to mark the tutorial as complete.

User participates in an interactive training session focused on implementing predictive scoring in sales strategies.

Given the user attends the interactive training session, when the session ends, then they should receive a feedback survey to assess their understanding and satisfaction with the training.

User accesses best practice guides within the User Training Module.

Given the user is in the User Training Module, when they click on the best practice guides section, then they should be able to download the guides in PDF format without any errors.

A user applies learned concepts from the training module to optimize their sales approach using predictive scoring.

Given the user completed the training, when they create a sales campaign utilizing predictive scoring, then they should report at least a 10% improvement in lead engagement rates within one month of implementation.

A user encounters issues accessing the training module and seeks assistance.

Given the user reports an issue via the help desk, when they submit their request, then they should receive a confirmation of receipt and estimated response time within 24 hours.

An admin monitors the effectiveness of the User Training Module after its launch.

Given the training module has been operational for three months, when the admin reviews user feedback and completion rates, then they should find at least a 70% satisfaction rating and 80% completion rate for the training modules.

Lead Score Insights Dashboard

A user-friendly dashboard displays trends and insights derived from lead scoring data, making it easy for users to visualize shifts in lead priorities. This feature prompts users to take action based on strategic insights, fostering a proactive approach to lead management.

Requirements

Lead Scoring Algorithm Enhancement

User Story: As a sales manager, I want an improved lead scoring algorithm so that I can prioritize my outreach efforts more effectively and increase the likelihood of converting leads into customers.
Description: This requirement involves improving the existing lead scoring algorithm to incorporate more data points, such as behavioral metrics and engagement history, allowing for more nuanced lead prioritization. Enhanced algorithms will significantly increase the accuracy of lead scores, enabling users to focus their efforts on the most promising leads. The integration of additional data sources will foster better predictive capabilities and result in higher conversion rates, thus maximizing the effectiveness of sales strategies.
Acceptance Criteria: User accesses the Lead Score Insights Dashboard after the enhancement of the lead scoring algorithm to view the prioritized list of leads based on the new scoring system.

Given the user has provided the necessary data points to the enhanced lead scoring algorithm, when they navigate to the Lead Score Insights Dashboard, then the dashboard should display a visually organized list of leads sorted by their new lead scores that incorporate behavioral metrics and engagement history.

The sales team conducts a review meeting using the Lead Score Insights Dashboard to evaluate the performance of leads scored under the new algorithm over the past month.

Given the sales team is using the updated Lead Score Insights Dashboard in a review meeting, when they analyze the lead scores from the last month, then at least 80% of leads should reflect a positive statistical correlation with their conversion rates, validating the algorithm's adjustments.

A user receives an alert from the dashboard indicating a significant change in the lead score of a key prospect due to new engagement metrics.

Given the enhanced lead scoring algorithm has been implemented, when there is a change in a lead's score based on updated engagement metrics, then the user should receive a notification on the Lead Score Insights Dashboard within five minutes of the score change occurring.

A new data point (e.g., email open rates) is incorporated into the lead scoring algorithm, and users want to assess its impact on scoring accuracy.

Given the new data point is integrated, when users run a comparison report using the previous lead scores against the current scores, then the accuracy of lead prioritization should improve by at least 15%, measured by the subsequent conversion rates of those leads.

An admin decides to review the historical lead scoring results before and after the enhancement to evaluate its effectiveness.

Given the admin is reviewing lead scoring results, when they check the lead scores from three months prior to the improvement against the scores from three months after the enhancement, then there should be a documented increase in the overall lead engagement by at least 20%.

Users utilize the automated recommendations feature on the dashboard which is influenced by the newly revised lead scoring algorithm.

Given the lead scoring algorithm has been enhanced, when users access the campaign recommendations section of the dashboard, then at least 90% of the recommended campaigns should target leads with high scores according to the updated algorithm, optimizing marketing efforts.

Real-time Alert Notifications

User Story: As a sales representative, I want to receive real-time notifications about critical lead score changes so that I can act quickly and ensure I’m engaging the most promising leads at the right moment.
Description: This requirement entails the creation of a real-time alert system that notifies users about significant changes in lead scores or shifts in engagement metrics. Users will receive notifications via email or in-app messages, prompting them to take timely action on leads showing high potential. By ensuring users are always aware of the most critical updates, this feature will enhance their ability to respond proactively to lead activities and optimize engagement strategies.
Acceptance Criteria: User receives a real-time alert notification when a lead's score increases significantly due to recent engagement activities, allowing the user to prioritize follow-up actions.

Given that a lead's activity has triggered a 20% increase in their lead score, when the score update occurs, then the user should receive an email notification and an in-app message alerting them of the change.

Multiple users log into the SalesMap AI platform and must receive individual notifications based on their assigned leads, ensuring no critical updates are missed.

Given that multiple users have different leads assigned, when a lead's engagement metrics change significantly, then each assigned user receives a tailored notification reflecting the lead assigned to them.

The user needs to customize their notification settings to filter alerts based on lead score changes, ensuring only the most relevant notifications are received.

Given that the user has access to notification settings, when changing the preferences to only alert on lead scores above a certain threshold, then only notifications meeting this criterion should be delivered.

A user wants to review and manage past notifications easily to analyze previous lead activities and adjust strategies accordingly.

Given that users have access to their notification history, when they visit the notifications section, then they should see a list of all past notifications sorted by date and lead score changes.

Real-time alerts need to be reliable, ensuring that users are notified without delays during peak engagement periods to prevent missed opportunities.

Given that the system is under high loads, when lead scores are updated, then the notifications should be sent within 5 seconds of the score change event occurring.

A user monitors team performance and wants to receive summary notifications about lead score changes across all team members to coordinate follow-up efforts effectively.

Given that the user has access to team performance metrics, when a lead's score changes for any team member, then the user should receive a daily summary email compiling all significant lead score changes for the day.

Customizable Dashboard Widgets

User Story: As a sales user, I want the ability to customize my dashboard widgets so that I can view the data that's most relevant to my goals and streamline my workflow accordingly.
Description: This requirement focuses on allowing users to customize their Lead Score Insights Dashboard with widgets that display specific metrics and trends relevant to their sales process. Users will be able to choose which insights they want highlighted, ensuring that their dashboard reflects personal preferences and needs. Customizable widgets will empower users to focus on the data most critical to their success, enhancing decision-making and operational efficiency.
Acceptance Criteria: User ability to select and add widgets to the Lead Score Insights Dashboard.

Given a user is logged into SalesMap AI, when they navigate to the Lead Score Insights Dashboard and access the widget customization menu, then they should be able to select from a list of available widgets and add them to their dashboard.

User customization of widget display settings.

Given a user has added widgets to the dashboard, when they click on a specific widget’s settings, then they should see options to adjust metrics displayed, widget size, and position on the dashboard.

User ability to save customized dashboard layout.

Given a user has made customizations to their dashboard by adding and rearranging widgets, when they click the 'Save Layout' button, then their customized layout should be saved and persist upon future logins.

User removal of widgets from the dashboard.

Given a user wishes to remove a widget from their Lead Score Insights Dashboard, when they click on the 'remove' option on the widget, then that widget should be removed from the dashboard immediately without affecting other widgets.

User dashboard reflects real-time data updates after widget customization.

Given a user has customized their Lead Score Insights Dashboard, when their chosen metrics change, then the dashboard widgets should automatically update to display the current data in real-time.

User accessibility of help instructions for dashboard customization.

Given a user is on the Lead Score Insights Dashboard, when they click on the 'Help' icon, then they should be provided with a guide on how to customize the dashboard and manage widgets effectively.

Lead Engagement History Tracking

User Story: As a salesperson, I want to view the complete engagement history of my leads so that I can tailor my follow-up strategies to better meet their needs and preferences.
Description: This requirement introduces a detailed tracking system to capture lead engagement history, including previous interactions, emails, and meeting notes. This feature will allow users to see a complete timeline of lead activities directly from the dashboard. Having comprehensive access to historical interaction data will enable sales teams to tailor their approach based on previous engagements, fostering more meaningful connections with leads and improving conversion opportunities.
Acceptance Criteria: Viewing Lead Engagement History

Given a user is on the Lead Insights Dashboard, when they select a specific lead, then the system displays a comprehensive timeline of all engagement activities including interactions, emails, and meeting notes related to that lead.

Filtering Lead Engagement Activities

Given a user is viewing the lead engagement history, when they apply filters for date range and activity type, then only the relevant engagement activities should be displayed according to the selected criteria.

Accessing Historical Interaction Data

Given that a user has selected a lead from the dashboard, when they click on the 'View History' option, then the system should present detailed interaction data including dates, types of interaction, and notes in a clear format.

Analyzing Lead Engagement Trends

Given the user accesses the Lead Engagement History, when they review engagement patterns over time, then the user should be able to see visual trends (charts or graphs) that summarize lead engagement activity.

Exporting Lead Engagement History

Given a user is viewing the engagement history of a lead, when they choose the 'Export' option, then the system should allow the user to download the lead engagement data in CSV format.

Receiving Notifications for Lead Updates

Given a user has previously engaged with a lead, when there is an update (new interaction or note added), then the user should receive a notification on the dashboard prompting them to view the updated engagement history.

Integration with Third-party CRM Systems

User Story: As a sales operations manager, I want seamless integration between SalesMap AI and other CRM systems so that I can manage all lead data in one place and improve my team's productivity.
Description: This requirement involves developing integration capabilities with popular third-party CRM systems, allowing for seamless data exchange between SalesMap AI and other commonly used platforms. This integration will ensure that users can synchronize lead information, engagement data, and scoring metrics across systems. By eliminating data silos, this feature will enhance user experience, streamline workflows, and enable more effective sales strategies.
Acceptance Criteria: Integration of SalesMap AI with Salesforce CRM system has been fully developed and is actively used by a sales team during their daily operations.

Given that a user is logged into SalesMap AI and has the Salesforce CRM integration enabled, When the user syncs lead data from Salesforce to SalesMap AI, Then all lead information, including lead scores and engagement metrics, must be accurately transferred and reflected in the SalesMap AI dashboard within 10 minutes.

SalesMap AI user accesses the Lead Score Insights Dashboard after integrating their third-party CRM system to retrieve updated lead scoring data.

Given that the user has successfully integrated their third-party CRM with SalesMap AI, When the user opens the Lead Score Insights Dashboard, Then the dashboard must display the latest lead scores and trends in real-time, reflecting the data from the integrated CRM without discrepancies.

Sales team conducts a strategy meeting using insights generated from the Lead Score Insights Dashboard after integration with HubSpot CRM.

Given that the dashboard displays insights from both SalesMap AI and HubSpot CRM, When the sales team reviews the lead scoring insights during their meeting, Then they must be able to identify at least three actionable strategies for improving lead engagement based on the displayed data.

A SalesMap AI user attempts to resolve synchronization issues between SalesMap AI and their integrated Zoho CRM system.

Given that the user identifies a mismatch in lead data between SalesMap AI and Zoho CRM, When they run the troubleshooting guide provided within SalesMap AI, Then the system should accurately diagnose the issue and offer at least three specific steps to resolve the data discrepancy.

The sales manager wants to review the effectiveness of the CRM integration over time.

Given that the integration with the CRM has been in place for over a month, When the sales manager generates a report from SalesMap AI, Then the report must clearly indicate an increase in lead conversion rates by at least 15% due to the integration.

Integration with External Data Sources

Seamlessly integrates with CRM and other business intelligence tools to aggregate external data, enriching the lead scoring process with fresh information. This comprehensive approach ensures that lead scores are informed by the most up-to-date insights available, improving accuracy and relevance.

Requirements

Real-Time Data Synchronization

User Story: As a sales manager, I want SalesMap AI to sync lead data from my CRM in real-time so that I can always have the most accurate lead scores for better prioritization and engagement with prospects.
Description: The requirement for real-time data synchronization ensures that SalesMap AI can dynamically update lead scores by continuously aggregating data from integrated external sources such as CRM systems and business intelligence tools. This feature is crucial, as it allows for immediate feedback and adjustments to lead scoring based on the latest available information. By incorporating real-time data, the accuracy and relevance of lead evaluations improve significantly, enabling sales teams to make informed and timely decisions that enhance conversion rates and overall sales performance.
Acceptance Criteria: Real-time data synchronization for lead scoring updates when a new lead is added to the CRM system.

Given a new lead is entered into the CRM, When the data is synchronized with SalesMap AI, Then the lead score should update within 5 seconds to reflect the latest data.

Validation of lead score adjustments based on external data changes, such as changes in lead engagement metrics.

Given an existing lead with an initial score, When the lead's engagement metrics are updated in the external data source, Then the lead score should be recalibrated to accurately reflect the new metrics within 10 seconds.

Monitoring of lead score accuracy by comparing against historical conversion rates from past leads.

Given a set of historical leads with known conversion outcomes, When the lead scoring algorithm is applied, Then at least 80% of the high-scoring leads should convert based on historical data.

Integration of data from multiple external sources into a single lead scoring framework.

Given multiple external data sources contributing to lead scoring, When data from these sources is aggregated, Then the lead score should combine inputs from all sources accurately without duplication or data loss.

User feedback mechanism for reporting inaccuracies in lead scoring.

Given a user identifies an inaccurate lead score, When they report the issue through the feedback mechanism, Then the system should log the feedback and provide a confirmation response within 2 minutes.

Testing data synchronization latency when multiple updates occur simultaneously across various external sources.

Given multiple simultaneous updates to external data sources, When the data synchronization process runs, Then each lead score should update within 8 seconds regardless of the number of concurrent updates.

Final review and confirmation of real-time data synchronization before deployment.

Given the synchronization feature is implemented, When it undergoes final testing by QA, Then all acceptance criteria must pass without any critical bugs or issues identified.

Custom Field Mapping

User Story: As a sales analyst, I want to map custom fields from our CRM to SalesMap AI, ensuring that all relevant data points are considered in lead scoring to optimize our sales approach.
Description: The custom field mapping requirement enables users to tailor the integration of external data sources by mapping specific fields from those sources to the corresponding fields within SalesMap AI. This flexibility is essential for businesses that utilize various software tools and require precise data alignment to enhance lead scoring accuracy. By allowing for custom field mappings, users can ensure that key data points are captured and leveraged effectively, resulting in more personalized and actionable insights that drive sales strategies.
Acceptance Criteria: User successfully maps fields from a third-party CRM to SalesMap AI during a data integration process.

Given a user accesses the custom field mapping interface, when they select a field from the external data source, then the corresponding field in SalesMap AI is correctly displayed for mapping.

User validates that the mapped fields accurately synchronize between the CRM and SalesMap AI.

Given a user has completed the field mapping, when they run a synchronization process, then the mapped data reflects the changes made in real-time within SalesMap AI.

User attempts to map a non-existent field, resulting in an error message.

Given a user tries to map a field that does not exist in SalesMap AI, when they submit the mapping, then an error message is displayed indicating the field cannot be mapped.

User saves the custom field mappings and expects them to be persistent across sessions.

Given a user saves the field mappings, when they log back into SalesMap AI, then the custom mappings should still be available and correctly configured.

User views and manages previously saved custom field mappings.

Given a user accesses the custom field mapping section, when they view previously saved mappings, then they should be able to edit or delete any of these mappings without issues.

User receives a notification when a new external data source is successfully integrated.

Given a user integrates a new external data source, when the integration is successful, then a notification pops up confirming the successful addition of the data source and prompts for field mapping.

User understands the purpose of each field and its potential impact on lead scoring.

Given a user is on the custom field mapping interface, when they hover over a field, then a tooltip should appear explaining the field's purpose and how it affects lead scoring accuracy.

Automated Data Quality Checks

User Story: As a data manager, I want automated data quality checks to ensure that the external data being used for lead scoring is accurate and reliable, so that the sales team can trust the insights provided by SalesMap AI.
Description: This requirement defines the need for automated data quality checks to validate and cleanse the incoming data from external sources before it influences lead scoring. The automation of these checks will involve assessing the completeness, accuracy, and consistency of the data, ensuring that only high-quality, reliable information feeds into the system. By implementing this feature, SalesMap AI minimizes the risk of relying on inaccurate or outdated data, which can lead to misguided sales efforts, thus enhancing overall system integrity and trustworthiness.
Acceptance Criteria: Automated Data Quality Checks for Incoming Leads from CRM

Given incoming data from external sources, when the automated data quality check process is executed, then the system should identify and flag any records with missing required fields, inaccurate formats, or conflicting information within 3 minutes.

Validation of Data Completeness and Accuracy

Given a dataset containing leads from various external sources, when the automated data quality checks are performed, then 95% of the records should pass completeness and accuracy assessments, with those failing being logged for review.

Consistency Checks During Data Integration

Given the incoming external data, when the automated checks run, then the system should ensure that all lead data is consistent across different external sources, with an error rate of no more than 2% allowed in the final aggregated data set.

Automated Alert System for Data Quality Issues

Given that automated checks detect data quality issues, when a problem is identified, then an alert should be generated automatically, notifying the designated administrator within 5 minutes of issue detection.

User Interface Display of Data Quality Results

Given that automated data quality checks are completed, when the results are generated, then users should be able to view a comprehensive dashboard summarizing pass/fail rates and specific issues identified, updated in real-time.

Historical Data Comparison for Performance Insights

Given that new incoming data is processed, when data quality checks are executed, then the system should compare the new data quality metrics against historical data, and generate a report showing trends in data quality over time.

System Efficiency Under Load

Given multiple incoming data streams from external sources, when the system performs automated data quality checks, then processing time should not exceed 5 seconds per 100 records under normal load conditions.

User Notifications for Data Updates

User Story: As a sales representative, I want to be notified whenever the external data influencing my lead scores is updated so that I can adjust my engagement strategies to align with the most current insights.
Description: The user notifications for data updates requirement entails providing real-time alerts or notifications to users when significant changes occur in the external data that impact lead scoring. This feature will keep users informed and proactive, allowing them to react swiftly to changes in lead evaluations. By enabling timely notifications about data updates, the system empowers sales teams to stay ahead of market shifts and competitor activities, thus maintaining a competitive edge.
Acceptance Criteria: User receives a notification upon a significant change in lead scoring due to updated external data.

Given the user is logged into SalesMap AI, when external data is updated resulting in a significant change in lead scoring, then the user should receive a real-time notification through the dashboard and optionally via email.

User can customize notification settings for data updates related to lead scoring.

Given the user is on the notification settings page, when the user adjusts their preferences to receive notifications for specific lead scoring thresholds, then those preferences are saved and reflected in the system notifications.

User benefits from a summary of recent data updates that triggered notifications.

Given a user has received notifications, when the user views the notification history, then they should see a summary of the external data updates that triggered each notification, including timestamps and relevant details.

Notifications are persistent until acknowledged by the user.

Given the user receives a notification of a significant data update, when the user logs into the system, then the notification remains visible until the user acknowledges it, ensuring the user does not miss critical updates.

User has the option to disable notifications for specific types of data updates.

Given the user is on the notification settings page, when the user selects the option to disable notifications for certain types of external data updates, then those changes are applied and saved, and the user no longer receives notifications for those types.

Ensure notifications do not overwhelm users with too many alerts.

Given multiple data updates occur within a short period, when notifications are generated, then the system should aggregate them into a single notification to avoid spamming the user with multiple alerts.

Engagement-Weighted Scoring

This feature assigns greater importance to recent interactions with leads, dynamically adjusting scores based on the frequency and quality of engagement. By prioritizing active leads, sales teams can optimize follow-up strategies and focus on prospects that demonstrate genuine interest.

Requirements

Dynamic Engagement Scoring

User Story: As a sales representative, I want the scoring of my leads to update automatically based on their recent interactions, so that I can focus on the leads most likely to convert and optimize my follow-up efforts.
Description: The Dynamic Engagement Scoring requirement involves the implementation of an algorithm that automatically updates lead scores based on real-time user interactions. This system will assess various engagement metrics such as email opens, click-through rates, and social media interactions, adjusting scores dynamically to prioritize leads that exhibit heightened interest. This ensures that sales teams can focus their efforts on leads most likely to convert, maximizing efficiency and improving conversion rates. Moreover, the feature will integrate seamlessly with the existing CRM system, ensuring that all data is current and easily accessible. The outcome will be a more streamlined sales process with improved tracking of engagement metrics, leading to better-informed follow-up strategies and increased revenue for small to mid-sized businesses.
Acceptance Criteria: Dynamic Engagement Scoring updates lead scores in real-time as sales agents interact with leads through email, social media, or onboarding calls during an active sales campaign.

Given a sales agent interacts with a lead through an email open, when the email is opened or clicked, then the lead's engagement score is updated immediately to reflect this interaction.

A sales manager reviews the engagement scoring dashboard at the end of a sales campaign to analyze which leads showed the highest engagement over the past month.

Given the sales manager accesses the engagement scoring dashboard, when the dashboard is loaded, then the manager can see lead scores updated based on the most recent interaction metrics such as email opens and social media clicks.

Sales agents conducting follow-ups based on lead scores for a new lead within the CRM after an initial interaction.

Given a new lead has interacted with the company's email campaign, when the follow-up is conducted, then the lead's score should dynamically adjust reflecting the engagement level, guiding the follow-up process effectively.

Before a sales call, a sales agent needs to prepare by reviewing the scores of leads based on their engagement levels.

Given the sales agent accesses the lead list, when they filter by engagement score, then they should see a prioritized list of leads that have received recent interactions, allowing for effective prioritization during the call.

The CRM system performs a nightly update to aggregate engagement metrics from various interactions and reflect them in lead scores.

Given the nightly processing is completed, when the CRM system generates a report, then the report should accurately reflect the updated engagement scores for each lead based on the interactions from the previous day.

Engagement History Tracking

User Story: As a sales agent, I want to access a detailed history of all interactions with each lead, so that I can personalize my communication and improve my chances of closing the sale.
Description: The Engagement History Tracking requirement entails creating a comprehensive logging mechanism that records all interactions with leads in a chronological order. This functionality will provide sales representatives with detailed insights into past engagements, making it easier to personalize follow-up communications. By integrating these logs with the sales dashboard, users can quickly assess which leads have engaged recently and how frequently they interact, allowing for data-driven strategy adjustments. The ability to reference engagement history aids in building a stronger rapport with prospects and enhances the overall effectiveness of sales efforts.
Acceptance Criteria: Sales representatives need to view the complete engagement history of a lead before making a follow-up call, allowing them to tailor their approach based on past interactions.

Given a sales representative accesses a lead's profile, when they view the engagement history section, then they should see a chronological list of all interactions including dates, interaction types, and notes.

A sales manager wants to analyze the engagement patterns of leads over the past three months to refine the team's follow-up strategies and maximize conversion rates.

Given the sales manager requests an engagement history report, when the report is generated, then it should display insights on the frequency and types of interactions per lead, highlighting the most recent engagements and overall trends.

A sales representative is alerted to follow up with leads that have engaged recently but have not converted, using their engagement history to personalize the conversation.

Given a lead has engaged with the company in the last week, when the representative reviews the engagement history, then they should see a summary of these interactions to assist in crafting a personalized follow-up message.

Sales reps utilize the dashboard to prioritize leads based on their recent engagement levels and past interaction quality, aiming to increase efficiency in follow-up.

Given the dashboard displays leads sorted by engagement score, when a sales rep filters leads by recent activity, then the top leads should reflect the highest engagement scores based on the defined scoring algorithm.

A user wants to ensure that all logged interactions with leads are easily accessible and retrievable, facilitating quicker preparation for follow-up meetings.

Given an interaction is logged, when the sales representative searches for that interaction in the engagement history, then it should be retrievable within five seconds and include all relevant details.

An administrator needs to confirm that the engagement history is correctly integrated with the CRM system to not lose any lead information during the synchronization process.

Given the integration is set up, when a new interaction is logged in the CRM, then it should also automatically reflect in the engagement history without discrepancies or delays.

Sales representatives require a clear understanding of the importance of various interactions with leads for effective follow-up strategies.

Given the sales rep accesses the training materials, when they review the guidelines on interaction types, then they should find clear categorizations indicating the weight assigned to each type of engagement (e.g., email open, call answered, etc.).

Real-Time Lead Alerts

User Story: As a sales manager, I want to receive instant notifications about any significant engagement from leads, so that I can take timely action and enhance the chances of conversion.
Description: The Real-Time Lead Alerts requirement consists of developing a notification system that alerts sales teams instantly whenever there is significant engagement from a lead. This may include actions such as opening an email, clicking on a website link, or requesting more information. By having immediate alerts, sales representatives can respond quickly to high-interest leads, engaging them while their interest is peaked. This functionality is expected to integrate with the existing user interface, providing notifications via email, SMS, or within the application, enhancing the responsiveness and effectiveness of the sales process.
Acceptance Criteria: Sales representative receives a notification when a lead opens an email sent to them, allowing for timely follow-up based on their engagement with the content.

Given a lead opens an email, when the email is tracked by the system, then a real-time alert is sent to the assigned sales representative via SMS and application notification within 5 seconds.

Sales teams need to be notified when a lead clicks on a link within an email or on the website, indicating a strong interest in a product or service.

Given a lead clicks on a tracked link in an email or on the website, when the action is detected, then a lead alert is generated and sent to the sales team's dashboard immediately and concurrently via email notification.

The sales team expects a notifications system that alerts them for multiple engagement events happening in a short timeframe, consolidating notifications for efficiency.

Given a lead engages in multiple actions (e.g., opens an email, clicks a link, requests information), when these events happen within a 10-minute window, then a single consolidated alert is sent to the assigned sales representative summarizing the actions taken.

The sales team requires customization options for the types of alerts they receive based on their preference for engagement activities.

Given a sales representative accesses the alert settings, when they select types of notifications (e.g., email opens, link clicks), then the system updates their preferences and only sends alerts according to their selected criteria.

Sales representatives need to ensure that alerts are acknowledged and that acknowledged alerts are not shown repeatedly, reducing notification clutter.

Given a real-time lead alert is sent, when the sales representative acknowledges the notification, then the alert is marked as read and removed from the dashboard, ensuring it is not displayed again unless a new action occurs.

Sales managers want to receive aggregated reports on engagement alerts received by their teams to analyze lead interests and monitor team performance.

Given the sales manager requests a report, when the report is generated, then it includes a summary of all real-time lead alerts received over the past week, highlighting lead engagement, response times, and follow-up actions taken by the sales team.

The sales team requires immediate and reliable notifications during high-traffic periods without delay, ensuring no leads are missed.

Given that the engagement volume with leads increases, when multiple alerts are triggered simultaneously, then the system must prioritize alert delivery maintaining a maximum latency of 5 seconds for any alert sent to the sales representatives.

Lead Interaction Analytics Dashboard

User Story: As a sales analyst, I want a dashboard that shows me the trends in lead engagement over time, so that I can identify the most effective sales strategies and improve our workflows.
Description: The Lead Interaction Analytics Dashboard requirement focuses on developing a robust analytics view that visualizes engagement trends and scores over time. This dashboard will allow users to see how engagement levels correlate with lead conversion rates, providing invaluable insights into sales strategies. By leveraging graphing tools and filtering options, sales teams can analyze which engagement activities yield the best results, making data-driven decisions for future campaigns. The dashboard will be fully interactive, allowing users to drill down into specific metrics and time frames for more granular analysis.
Acceptance Criteria: Lead Interaction Analytics Dashboard displays user engagement trends and scores over a selected time period, allowing sales teams to analyze engagement activities effectively.

Given the user selects a time frame on the Lead Interaction Analytics Dashboard, when the user applies filters for engagement types, then the dashboard must display a line graph showcasing the engagement scores over the selected period with accurate correlation to lead conversion rates.

Sales team members can drill down into specific metrics on the dashboard to gain deeper insights into lead interactions.

Given that a sales team member has accessed the Lead Interaction Analytics Dashboard, when they click on a specific engagement score metric, then the dashboard should provide detailed analytics including the time of contact, frequency of interaction, and actions taken by the lead, enabling users to understand engagement peaks.

Users of the SalesMap AI can save and share customized views of the Lead Interaction Analytics Dashboard with team members.

Given that a user has customized their view of the Lead Interaction Analytics Dashboard, when they click the 'Save' button, then the system must save the configuration and allow the user to share the view via a unique link with other team members, ensuring that the configuration remains unchanged for others to access.

Sales teams need to receive notifications about significant changes in lead engagement scores based on their interactions.

Given that the lead engagement scores dynamically adjust based on recent interactions, when a lead's engagement score increases or decreases by more than 20% within a specified time frame, then the system must send an email notification to the assigned sales representative to alert them of the change.

The Lead Interaction Analytics Dashboard should provide users with comparative analytics to evaluate different leads' engagement levels effectively.

Given that a user is on the Lead Interaction Analytics Dashboard, when they select multiple leads to compare, then the dashboard should display a side-by-side comparison of engagement scores, activities, and conversion rates of the selected leads in a clear and understandable format.

Integration with Email Marketing Tools

User Story: As a marketer, I want the system to integrate with our email marketing tools to see how our campaigns impact lead engagement, so that we can refine our messaging and increase conversions.
Description: The Integration with Email Marketing Tools requirement involves establishing connections with popular email marketing platforms to enhance lead engagement tracking. This will allow the system to pull in engagement data from email campaigns, such as open rates and click-through rates. The integration will enrich the lead scoring model by adding additional layers of engagement information and ensure that data is synchronized in real-time. By conveying a more comprehensive view of lead interactions, sales teams can craft highly relevant follow-up communications and campaigns that resonate with their audience.
Acceptance Criteria: Integration with Email Marketing Tools: Establishing a connection between SalesMap AI and email marketing platforms to pull engagement data from email campaigns.

Given that I have connected my email marketing tool to SalesMap AI, when I send an email campaign, then the system should automatically pull in the engagement metrics, including open rates and click-through rates, within 5 minutes after the campaign is sent.

Real-time Data Synchronization: Ensuring that lead engagement data is updated in real-time in SalesMap AI from linked email marketing tools.

Given that the email marketing tool is integrated, when there is an interaction with a lead (e.g., an email opened or a link clicked), then the lead score in SalesMap AI should be updated within 2 minutes based on the predefined scoring rules.

Impact on Lead Scoring Model: Evaluating how integration with email marketing tools affects the lead scoring model within SalesMap AI.

Given that engagement data from email campaigns is synchronized, when I view the lead scoring dashboard, then the lead scores should reflect the newly integrated engagement metrics accurately and be ranked by the most engaged leads at the top.

User Notification for Data Pulling: Informing users about the successful pulling of engagement data after an email campaign.

Given that an email campaign has been executed, when the data has been successfully pulled into SalesMap AI, then the user should receive a notification confirming that the engagement metrics are updated in the system.

Integration Testing with Email Marketing Tools: Testing the functionality of the integration between SalesMap AI and various email marketing platforms for lead engagement data.

Given that I am running integration tests with email platforms, when I simulate sending an email campaign, then the system should successfully capture engagement data from at least three different email marketing tools without errors.

Error Handling for Failed Data Synchronization: Managing situations where data synchronization fails due to connectivity issues or API errors.

Given that an issue occurs during data synchronization, when the system attempts to pull engagement data from the email marketing tool, then an error message should be displayed to the user, and the system should retry the process after 5 minutes.

User Interface Update for Engagement Metrics: Displaying the newly integrated engagement metrics on the SalesMap AI dashboard.

Given that the email marketing tool is integrated, when I navigate to the lead scoring dashboard, then I should see a clear overview of email engagement metrics (open rates, click-through rates) alongside the traditional scoring metrics.

Automated Scoring Notifications

Users receive automated alerts when lead scores change significantly or fall below predefined thresholds. These notifications enable timely interventions, allowing sales professionals to act swiftly on leads that require immediate attention and increasing the likelihood of conversions.

Requirements

Real-time Lead Score Tracking

User Story: As a sales professional, I want to receive real-time updates on lead scores so that I can prioritize my outreach and focus on high-potential leads immediately.
Description: This requirement involves creating a system that continuously monitors lead scores in real time, ensuring users receive immediate updates when scores change due to new interactions or activity. By integrating with existing CRM data and analytics, this feature enhances the users' ability to prioritize and respond to leads, ensuring no potential opportunity is overlooked. Implementing this functionality requires a robust backend system capable of handling real-time data feeds and an intuitive user interface that displays score changes prominently.
Acceptance Criteria: A sales representative monitors lead scores throughout the day while interacting with prospects via email, phone calls, and meetings. The representative relies on real-time notifications to quickly react to changes in lead scores, especially for leads that score below a critical threshold, allowing them to prioritize outreach and maximize conversion opportunities.

Given that lead scores are being tracked in real time, when a lead score changes significantly or falls below the predefined threshold, then the system should trigger an automated notification to the sales representative.

During a weekly sales meeting, team members review the performance of their leads and strategies using the dashboard. They discuss specific cases of leads whose scores have fluctuated over the week and evaluate how the real-time notifications helped them prioritize their actions and improve conversion rates.

Given that the dashboard displays real-time lead scores, when a lead's score is updated, then the dashboard should refresh automatically to reflect the most current scores without requiring manual refresh.

A user sets specific thresholds for lead scores that determine when notifications should be sent. They need to ensure that these thresholds can be adjusted based on various criteria, such as industry trends or recent campaign results, allowing for more tailored alerting based on their strategy.

Given that a user has defined thresholds for lead scores in the settings, when the user modifies the threshold values, then the system should save these changes and apply them for future score evaluations and notifications.

A sales manager wants to ensure that their team is receiving timely notifications when lead scores drop. They will conduct a test by simulating interactions that affect lead scores to verify that notifications are sent immediately when scores change.

Given that lead scores are affected by user interactions, when a simulated interaction occurs that results in a lead score falling below a critical threshold, then a notification should be sent to the designated sales representative within 1 minute.

After receiving notifications about lead score changes, a sales user must take quick actions based on these alerts. They need to log their actions in the CRM to ensure there is a record of how they responded to each lead score change and track their effectiveness over time.

Given that an automated notification is received for a lead with a changed score, when the user clicks on the notification, then they should be directed to the lead's profile in the CRM to log their actions and notes related to that lead.

Threshold Configuration for Notifications

User Story: As a sales manager, I want to set custom lead score thresholds for notifications so that my team only receives alerts that are genuinely actionable and relevant.
Description: This feature will enable users to define customizable thresholds for lead scores, allowing them to set specific triggers that will initiate notifications. By providing flexibility in threshold settings, users can tailor their notification preferences to align with their sales strategies and objectives. This capability is essential for ensuring that alerts are relevant and actionable, enhancing user engagement and empowering sales teams to take timely actions without missing critical opportunities.
Acceptance Criteria: User sets a lead score threshold of 70 for notifications in the SalesMap AI dashboard.

Given a user has set a lead score threshold of 70, when a lead's score drops to 65, then the user should receive an automated notification alerting them of this change.

User configures multiple thresholds for different lead categories (e.g., hot, warm, cold) in the system.

Given a user has configured thresholds for hot, warm, and cold leads, when a lead's score changes falling below the threshold for that category, then the system sends appropriate notifications for each category.

User attempts to deactivate notifications for a specific lead score threshold.

Given a user has previously activated notifications for a lead score threshold, when the user deactivates this feature, then no further notifications should be sent for that threshold until it is reactivated.

User modifies an existing threshold value for lead scoring notifications.

Given a user has changed a lead score threshold from 75 to 80, when a lead's score is 78, then no notification should be sent until the lead's score falls below 80.

User views a history of lead score changes and associated notifications in the SalesMap AI platform.

Given a user accesses the notification history, when they look at the log, then they should see entries with timestamps and details of all alerts sent based on their threshold configurations.

User receives a notification when two different leads dip below their respective thresholds simultaneously.

Given two leads with different pre-set thresholds, when both lead scores drop below their thresholds at the same time, then the user should receive two distinct notifications for both leads.

Notification Channels Selection

User Story: As a user, I want to select how I receive notifications when lead scores change so that I can stay informed through my preferred methods of communication without being overwhelmed.
Description: Users should be able to choose their preferred channels for receiving scoring notifications, such as email, in-app alerts, or SMS. This requirement addresses the diverse needs of users and ensures that they receive important updates in a manner that best suits their workflow. By allowing multiple notification channels, SalesMap AI enhances user experience and facilitates timely decision-making, which is crucial for maximizing lead conversion rates.
Acceptance Criteria: Users are setting up their preferred notification channels for receiving lead scoring alerts within the SalesMap AI platform.

Given that the user is logged into SalesMap AI, when they navigate to the notification settings page and select their preferred channels (email, in-app alerts, SMS), then the system should save these preferences and confirm them to the user.

A user has configured their alert settings for scoring notifications and a lead's score significantly decreases.

Given that a lead's score drops below the predefined threshold, when the scoring change occurs, then the user should receive an alert via their chosen notification channel(s) indicating the change in lead score.

A user wants to change their notification preferences for lead scoring alerts after previously setting them up.

Given that the user is on the notification settings page, when they update their preferred notification channels and save the changes, then the system should reflect these new preferences and notify the user of the successful update.

Users are attempting to receive immediate notifications about critical lead scoring adjustments.

Given that a lead's score changes significantly based on real-time data, when the change triggers an alert, then the user should receive the notification within 5 minutes of the score change through their preferred channel.

Users wish to test the functionality of their selected notification channels before relying on them for critical updates.

Given that the user has set their notification preferences, when they select the 'Test Notification' button on the settings page, then they should receive a test alert through all selected channels, confirming that the notifications are functioning correctly.

Users want to understand how to modify their notification settings easily within the SalesMap AI platform.

Given that the user accesses the help section, when they review the documentation on notification settings, then they should find clear, step-by-step instructions on how to modify their notification preferences.

Historical Score Analytics

User Story: As a sales analyst, I want to analyze historical lead score data so that I can identify trends and optimize our engagement strategies accordingly.
Description: This requirement entails developing a feature that provides users with access to historical data on lead scores, allowing them to analyze trends over time. By understanding how lead scores fluctuate and the factors contributing to these changes, sales professionals can refine their strategies and improve their outreach efforts. This feature will integrate data visualization tools to present this information clearly and compellingly, aiding strategic decision-making.
Acceptance Criteria: Viewing Historical Lead Score Trends

Given that a user accesses the Historical Score Analytics feature, when they select a specific lead, then they must be able to view a detailed visualization of lead score trends over the past 12 months.

Filtering Historical Data by Date Range

Given that a user is in the Historical Score Analytics section, when they select a custom date range, then they must be able to filter and view lead scores accurately for that specified period.

Identifying Factors Influencing Score Changes

Given that a user is viewing historical lead score data, when they hover over a data point in the trend visualization, then they must see contextual tooltips that explain the factors or events that influenced lead score changes.

Exporting Historical Data for Reporting

Given that a user is in the Historical Score Analytics feature, when they click the export button, then they must receive a downloadable report in CSV format containing their filtered historical lead score data.

Receiving Alerts for Significant Score Changes

Given that a lead's score changes significantly, when the Historical Score Analytics feature is active, then the user must receive an automated alert notification in real-time.

Integrating with CRM for Historical Data

Given that the user has integrated their CRM with SalesMap AI, when they access the Historical Score Analytics feature, then they must see historical lead scores that match those recorded in their CRM.

Comparing Scores Across Multiple Leads

Given that a user is in the Historical Score Analytics section, when they select multiple leads, then they must be able to compare their historical score trends side by side in a single visualization.

Team Collaboration Features

User Story: As a sales team member, I want to share lead scoring notifications and insights with my teammates so that we can work collaboratively and improve our chances of closing deals together.
Description: To facilitate teamwork and improve lead management, this feature will allow users to share notifications and notes about lead score changes with team members within the platform. This collaboration capability enables users to coordinate efforts and maintain alignment on lead priorities, ensuring that team members can update each other on lead status and conversion progress in real time, leading to greater overall effectiveness in their sales approach.
Acceptance Criteria: Lead Score Change Notification Sharing among Team Members

Given a user receives an automated notification about a significant lead score change, when they share this notification with their team via the platform, then all relevant team members should be able to view the shared notification in real-time within their dashboard.

Real-Time Updates on Lead Status Changes

Given a lead's score falls below a predefined threshold, when a user updates the lead status in the platform, then all notified team members should receive an update indicating the new lead status immediately.

Notes Sharing about Lead Scoring Changes

Given a user writes a note regarding a lead score update, when they share this note with other team members, then all designated team members should be able to view the note within their notifications section without delay.

Collaboration on Lead Priority Decisions

Given multiple users are monitoring the same leads, when one user flags a lead as a high priority due to a score change, then all other users assigned to that lead should receive an alert to act accordingly.

User Interaction with Notifications

Given a user receives a notification about a lead's score change, when they click on the notification, then it should redirect them to the lead's details page for immediate action or review.

Historical Tracking of Lead Score Changes

Given a lead's score changes over time, when users view the lead profile, then they should be able to access a history of score changes and associated notes shared by other team members.

Team Collaboration Visibility Settings

Given the user initiates a collaboration on lead scoring notifications, when they set visibility options, then only selected team members should be able to see these notifications according to the chosen settings.

Upsell Predictor

The Upsell Predictor harnesses machine learning algorithms to analyze historical purchase patterns and forecast potential upsell opportunities. By identifying customer buying behaviors and preferences, this feature gives sales teams proactive insights into which products to recommend at the optimal time, thus increasing the chances of successful upselling.

Requirements

Historical Purchase Analysis

User Story: As a sales representative, I want to understand customer purchase patterns so that I can recommend products that are more likely to lead to successful upsells based on historical data.
Description: The Historical Purchase Analysis requirement involves the development of algorithms that can sift through past sales data to identify customer purchasing trends and behaviors. This analysis will help the Upsell Predictor feature by generating insights into what products are often purchased together and at what times these purchases are most commonly made. By integrating this analysis within the SalesMap AI platform, sales teams can access consolidated insights, ultimately aiding in targeted marketing strategies to boost sales conversions. Its successful implementation will lead to enhanced understanding of customer preferences and timely recommendations, driving increased upsell opportunities.
Acceptance Criteria: Sales teams need to utilize the Historical Purchase Analysis during a sales meeting to identify upsell opportunities based on previous customer purchase behavior.

Given that the historical purchase data is available, when a sales team accesses the Upsell Predictor, then they should see a list of recommended products based on at least the last six months of purchase history for each customer.

A sales representative is reviewing the performance of the Upsell Predictor feature to maximize upsell chances for an upcoming promotional campaign.

Given that the predictive algorithm has analyzed the past sales data, when the sales representative requests insights for a specific product, then they should receive analytics that indicate the optimal upsell products and the timing of those recommendations with at least 80% accuracy based on historical data.

A sales manager wants to generate a report to assess the effectiveness of the Upsell Predictor over the last quarter.

Given that the historical purchase analysis has been performed, when the sales manager generates the report, then it should include metrics such as the number of successful upsells, the product combinations recommended, and the percentage increase in sales due to upselling, showing at least a 10% increase.

During a routine sales training session, new team members need to understand how to interpret the recommendations provided by the Upsell Predictor feature.

Given that new team members are trained, when they explore the Upsell Predictor interface, then they should be able to clearly understand and explain at least three key insights derived from the historical purchase analysis, demonstrating comprehension of how customers buy together.

A sales team is preparing for a major seasonal sale that relies on upselling strategies powered by the Upsell Predictor feature.

Given that the historical purchase analysis has completed, when the sales team tests the Upsell Predictor feature, then at least 90% of the identified upsell opportunities should align with the sales team’s current inventory and align with purchase trends identified in the last year.

Real-time Upsell Recommendations

User Story: As a salesperson, I want to receive upsell suggestions in real-time during customer conversations so that I can enhance my selling position and close more deals.
Description: The Real-time Upsell Recommendations requirement focuses on providing immediate suggestions for upselling during customer interactions. This feature will utilize real-time data processing and machine learning algorithms to analyze customer behavior at the moment, offering insights into potential upsell opportunities while the customer is engaged. Integration with existing CRM tools will ensure that sales reps can quickly access these recommendations, enabling them to personalize their pitches on the fly. The outcome will be significant as it enhances the chances of conversions and leads to increased sales figures.
Acceptance Criteria: Sales representative accesses the Real-time Upsell Recommendations during a customer call to suggest related products based on customer purchase history.

Given a sales representative is on a call with a customer, when the representative accesses the upsell tool, then the system must display at least three relevant upsell recommendations based on the customer's previous purchases within five seconds.

Customer purchases a product and prompts the upsell predictor to analyze the purchase in real-time.

Given a customer has completed a purchase, when the confirmation is processed, then the system shall analyze the purchase within three seconds and generate a new set of upsell recommendations based on the latest transaction.

Sales representative receives real-time notifications of upsell opportunities while engaging with a customer on the platform.

Given a sales representative is interacting with a customer, when there is a potential upsell opportunity based on the customer’s browsing behavior, then the system should notify the sales representative in less than five seconds with tailored product suggestions.

Integration with existing CRM allows seamless updating of customer profiles post-upsell suggestions.

Given a successful upsell is made, when the upsell recommendation is accepted, then the system must automatically update the customer profile in the CRM with the new purchase details and upsell metrics within two minutes.

Sales team reviews data on upsell success rates generated by the Real-time Upsell Recommendations feature.

Given the sales team accesses the report dashboard, when they select the upsell success rate report, then the system shall provide a comprehensive report detailing the percentage of successful upsells within the last month and the recommendations used during those transactions.

Sales representatives receive initial training on how to use the real-time upsell feature effectively during customer interactions.

Given that a sales representative completes the training module, when they demonstrate using the Real-time Upsell Recommendations tool during a mock call, then they should effectively show the ability to access and articulate upsell recommendations accurately without guidance.

Customer Segmentation for Targeted Upselling

User Story: As a marketing manager, I want to segment customers based on their buying behavior so that I can create targeted upsell campaigns that resonate with different customer groups.
Description: The Customer Segmentation for Targeted Upselling requirement involves categorizing customers based on purchasing behavior, preferences, and demographic information. This segmentation will allow the Upsell Predictor to tailor recommendations more effectively to different customer groups. By understanding the unique characteristics of each segment, SalesMap AI can provide deeper insights that are relevant, making sales strategies more effective. It will ensure that upselling efforts are optimized and directed towards the most promising customer segments, enhancing engagement and conversion rates.
Acceptance Criteria: Customer segmentation based on purchasing behavior, preferences, and demographic information for targeted upselling efforts during sales campaign planning.

Given the customer database, when the segmentation algorithm is executed, then customers should be classified into at least three distinct segments based on their buying behavior and demographics, with an accuracy of at least 85%.

Using the segmented customer groups to generate tailored upsell recommendations during a live sales call.

Given a segmented customer group, when a sales representative initiates a call, then the system should provide at least three personalized upsell recommendations within five seconds that correspond to the customer's segment.

Evaluating the effectiveness of upsell recommendations made to a specific customer segment over a one-month sales period.

Given a customer segment that received upsell recommendations, when the sales data is analyzed post-campaign, then the conversion rate for upsells should exceed 15% above the baseline for that segment.

Monitoring the performance of upsell recommendations across different customer segments through the platform's dashboard.

Given the existing sales data, when the dashboard is accessed, then it should display segment-wise performance metrics for upselling, including conversion rates and total revenue generated, updated in real-time.

Integrating customer feedback on upselling recommendations to refine future segmentation and recommendations.

Given the feedback mechanism is in place, when customers provide feedback on upsell recommendations, then the system should analyze the feedback and adjust future segment definitions and upsell protocols accordingly.

Ensuring the system can handle scalability with increasing customer data while maintaining segmentation accuracy.

Given an increase in the customer database by 50%, when the segmentation algorithm is run, then it should process the additional data without a decline in segmentation accuracy below 80% and within a response time of under ten minutes.

Performance Metrics Dashboard

User Story: As a sales manager, I want to view performance metrics on upselling efforts so that I can assess the effectiveness of our strategies and make improvements where necessary.
Description: The Performance Metrics Dashboard requirement includes the creation of a visual analytics dashboard that displays key performance indicators related to upselling efforts. This dashboard will provide sales teams with insights on upsell success rates, customer interaction quality, and effectiveness of various upselling strategies. By visualizing these metrics, sales teams can identify trends, optimize their strategies, and make data-driven decisions to enhance their sales processes. The dashboard will also integrate seamlessly with other SalesMap AI tools to provide a comprehensive view of sales performance.
Acceptance Criteria: Sales team accesses the Performance Metrics Dashboard to view upselling metrics after a recent marketing campaign has concluded.

Given the sales team has access to the Performance Metrics Dashboard, When they navigate to the upselling metrics section, Then they should see a visual representation of upsell success rates, customer interaction quality, and upselling strategy effectiveness for the past month.

The dashboard allows filtering of upselling metrics based on specific time frames and product categories.

Given the user is viewing the Performance Metrics Dashboard, When they select a time frame and product category from the filter options, Then the displayed metrics should update in real-time to reflect the selected filters.

Sales team receives insights on upsell strategies directly from the dashboard during a team meeting.

Given the sales team members are in a meeting, When they refer to the Performance Metrics Dashboard, Then they should be able to identify at least three actionable insights based on the visualizations provided for upselling success rates and strategies.

The Performance Metrics Dashboard integrates with the CRM to pull real-time data on customer interactions.

Given the dashboard is integrated with the CRM, When new customer interaction data is recorded in the CRM, Then the Performance Metrics Dashboard should reflect the updated data without manual refresh.

Sales team evaluates upselling performance before and after implementing a new upselling strategy.

Given the upselling strategy has been implemented, When the sales team compares the upselling metrics before and after the strategy implementation, Then they should observe a measurable increase in upsell success rates that meets or exceeds a predefined threshold.

Feedback Loop for Continuous Improvement

User Story: As a sales representative, I want to provide feedback on upsell suggestions so that the recommendations can be improved over time to better meet customer needs.
Description: The Feedback Loop for Continuous Improvement requirement involves implementing a mechanism to gather feedback from sales representatives regarding the effectiveness of upsell recommendations. This will include capturing success rates from real interactions and gathering subjective feedback on the usefulness of recommendations. The goal is to refine the machine learning models of the Upsell Predictor continuously, ensuring they evolve based on real-world sales data and feedback. The outcome will be a smarter recommendation engine that improves over time, increasing confidence in the recommendations provided to users.
Acceptance Criteria: Sales representatives use the Feedback Loop feature after executing upsell recommendations in a live sales environment.

Given that a sales representative has access to the Feedback Loop feature, when they provide feedback on the upsell recommendations used during interactions, then the feedback should be successfully captured and stored in the system for analysis.

Sales managers review the feedback gathered from sales representatives regarding upsell recommendations.

Given that feedback has been collected, when a sales manager accesses the feedback report, then they should see a summary of both quantitative success rates and qualitative feedback indicating the usefulness of the recommendations.

Machine learning models are updated based on the feedback received from sales representatives.

Given that feedback has been analyzed, when the machine learning models are trained with the latest feedback data, then the model's performance metrics should show a measurable improvement in predicting successful upsell opportunities.

Sales representatives receive updated upsell recommendations following model improvements.

Given that the machine learning models have been updated, when a sales representative accesses the Upsell Predictor, then the recommendations provided should reflect the latest improvements based on historical feedback data.

Sales representatives report on the accuracy and effectiveness of the updated upsell recommendations during team meetings.

Given that sales representatives have been using the updated recommendations, when they share their experiences in a team meeting, then at least 75% of the representatives should report an increase in successful upselling based on the new system recommendations.

The system logs all feedback and configurations made by sales representatives for future reference.

Given that a sales representative has entered feedback, when they check the system logs, then all feedback should be recorded with timestamps and associated sales representative identifiers for audit purposes.

Integration with Third-party Platforms

User Story: As a user of SalesMap AI, I want to seamlessly integrate the Upsell Predictor with other tools I use so that I can leverage all available data for better upselling decisions.
Description: The Integration with Third-party Platforms requirement emphasizes the need to connect the Upsell Predictor feature with various third-party tools and platforms commonly used by sales teams. This includes integration with email marketing platforms, CRM systems, and customer support tools, allowing for a seamless flow of data and insights across systems. Such integration is vital for ensuring that sales representatives have access to the most relevant upsell opportunities and customer information when needed, enhancing the overall effectiveness of sales efforts. The outcome will be streamlined operations, reduced manual data entry, and improved workflow.
Acceptance Criteria: Sales representative connects Upsell Predictor to their email marketing platform to analyze customer data and identify upsell opportunities during a campaign.

Given the user has authorized the integration, when they select a customer segment, then the system should display at least three tailored upsell opportunities based on past purchase data.

A sales team member utilizes the Upsell Predictor to view upsell opportunities for a specific customer in their CRM system during a sales call.

Given the user accesses the customer's profile, when they click on the 'Upsell Opportunities' tab, then the system must show relevant product recommendations based on the customer's purchase history and preferences.

The data from the Upsell Predictor is automatically synced with the customer support tool to provide insights during customer queries.

Given the integration is active, when a customer support representative opens a case, then they should see upsell opportunities highlighted related to the customer's transaction history directly in the support tool interface.

Sales representatives receive notification alerts about potential upsell opportunities in real-time through their integrated platforms.

Given the integration is set up, when new customer behavior patterns are detected, then the system should send an instant notification to relevant sales representatives with suggested upsell actions.

Reports generated from the integration of the Upsell Predictor with third-party platforms include metrics on upsell effectiveness.

Given the user generates a sales report, when the report is compiled, then it should include metrics on upselling success rates and the impact on overall sales based on data from all integrated platforms.

Sales manager oversees the overall performance of the Upsell Predictor integration across the team's various platforms.

Given the integration dashboard is accessed, when the manager reviews the performance metrics, then they should see aggregate data reflecting seamless data flow and user engagement across all integrated systems.

Dynamic Recommendations Engine

The Dynamic Recommendations Engine continuously updates suggested upsells based on real-time sales data and inventory levels. This ensures that sales teams always have the most relevant and feasible upsell options to present to customers, enhancing the likelihood of transaction increases and improving overall sales strategy effectiveness.

Requirements

Real-time Data Sync

User Story: As a sales representative, I want the recommendations engine to sync in real-time with sales and inventory data so that I can access the most current and effective upsell options during customer interactions.
Description: This requirement entails the implementation of a robust data synchronization mechanism that ensures real-time updates of sales figures and inventory levels within the Dynamic Recommendations Engine. The feature will continuously fetch and integrate data from various sales channels and the existing inventory management system, facilitating timely and informed recommendation updates. By maintaining up-to-date information, it allows sales teams to present the most relevant upsell options, greatly enhancing customer engagement and maximizing transaction value. This functionality is crucial for maintaining a competitive edge in a dynamic sales environment, thereby increasing the overall effectiveness of the sales strategy.
Acceptance Criteria: Sales team accesses the Dynamic Recommendations Engine during a customer interaction to display upsell options based on current inventory levels and sales data.

Given the sales team is logged in to the SalesMap AI system, when they initiate the Dynamic Recommendations Engine, then the system must display upsell options that are relevant and in stock based on the most recent sales data.

Real-time updates occur when new sales are made, ensuring that the upsell options suggested by the Dynamic Recommendations Engine reflect the latest inventory levels.

Given a new sale is completed, when the sale data is recorded, then the inventory levels should be updated within the Dynamic Recommendations Engine in under 5 seconds, reflecting the changes accurately.

A sales representative queries the system for upsell recommendations during a live customer call, relying on the most recent data for optimal performance.

Given the sales representative is on a call with a customer, when they request upsell options through the Dynamic Recommendations Engine, then the system must return recommendations that are the most current and relevant taking into account real-time inventory and sales data.

The system integrates sales data from multiple channels, providing a holistic view for the Dynamic Recommendations Engine.

Given that sales are being recorded across different channels, when the system syncs, then all relevant sales data from those channels must be accurately reflected in the Dynamic Recommendations Engine within 10 seconds of transaction recording.

Sales managers review performance data from the Dynamic Recommendations Engine to assess the effectiveness of upselling strategies.

Given the sales manager accesses the performance dashboard, when they view metrics regarding upsell effectiveness, then the system must display updated metrics based on the latest real-time sales data and customer interactions, with a refresh rate of no longer than 15 seconds.

The system automatically recommends adjustments to upsell options when inventory levels change significantly, ensuring sales teams are always equipped with the best suggestions.

Given an adjustment in inventory that exceeds a 20% change, when the system detects this change, then the Dynamic Recommendations Engine must automatically refresh the suggested upsell options within 5 seconds to reflect the new inventory status.

Sales training is conducted to familiarize the team with the usage of upsell recommendations based on real-time data.

Given a scheduled training session, when the sales team completes the training on using the Dynamic Recommendations Engine, then at least 90% of attendees must demonstrate proficiency in navigating the system and utilizing the recommendations effectively in a simulated sales scenario.

User-Friendly Dashboard

User Story: As a sales representative, I want a user-friendly dashboard to view upsell recommendations so that I can quickly understand and utilize the suggestions during sales calls.
Description: The requirement is to create an intuitive and user-friendly dashboard display that presents the upsell recommendations generated by the Dynamic Recommendations Engine. The dashboard should feature clear visualizations of recommended products, their relevance scores, and the rationale behind each suggestion, making it easy for sales representatives to quickly comprehend and utilize the data. This dashboard will enhance the user experience by reducing the learning curve, enabling even less experienced sales personnel to effectively leverage the recommendations. Furthermore, it should be accessible on all devices to support sales teams working in the field.
Acceptance Criteria: Sales Representative Accessing Dashboard in the Field

Given that the sales representative is using a mobile device, when they access the user-friendly dashboard, then the dashboard displays upsell recommendations without delay and is fully functional.

Displaying Correct Upsell Recommendations based on Dynamic Data

Given real-time sales data updates, when a sales representative views the dashboard, then the upsell recommendations displayed should align with the latest inventory levels and relevance scores, ensuring the top three products are highlighted.

Visual Clarity of Dashboard Elements

Given the user-friendly dashboard, when a sales representative reviews the upsell section, then each recommendation should have clear visual indicators (e.g., color codes, icons) to denote high, medium, and low relevance scores, enhancing usability.

Rationale Behind Upsell Recommendations

Given a sales representative reviewing a recommendation, when they click on an upsell suggestion, then a pop-up should provide a rationale explaining the recommendation based on sales history and customer behavior data.

Cross-Device Compatibility of Dashboard

Given that the dashboard is accessed on different devices, when a sales representative views the dashboard on a tablet or desktop, then the dashboard layout should remain consistent and responsive across all platforms.

User Feedback on Dashboard Usability

Given that sales representatives have used the dashboard, when a survey is conducted after usage, then at least 80% of respondents should indicate that the dashboard was easy to use and navigate.

Adaptive Learning Algorithm

User Story: As a product manager, I want the recommendations engine to learn from customer interactions so that it can improve the relevance of upsell options over time, leading to better sales outcomes.
Description: This requirement focuses on the incorporation of an adaptive learning algorithm within the Dynamic Recommendations Engine. The algorithm will analyze customer interactions and feedback to continually improve the accuracy and relevance of upsell recommendations. By utilizing machine learning techniques, the system will become more effective over time, identifying patterns in customer behavior and preferences. The adaptive aspect ensures that the recommendations evolve with changing market trends and customer needs, ultimately leading to higher conversion rates and customer satisfaction.
Acceptance Criteria: Upsell suggestions provided during a live customer engagement at various points in the sales process.

Given a customer interacts with the platform, when the adaptive learning algorithm processes their interaction data, then it presents relevant upsell recommendations that reflect the customer's previous purchasing history and current inventory levels.

Analyzing the effectiveness of upsell recommendations over a trading quarter.

Given sales data from a trading quarter is analyzed, when comparing the conversion rates of upsells before and after implementing the adaptive learning algorithm, then there should be at least a 15% improvement in upsell conversion rates due to more relevant recommendations.

Customer feedback is collected after upsell transactions to improve future recommendations.

Given that a customer completes an upsell transaction, when they provide feedback on the relevance and usefulness of the recommendation, then the adaptive learning algorithm should incorporate this feedback into its future recommendations to adjust for improved accuracy.

Real-time updates to upsell recommendations based on inventory changes.

Given that inventory levels change due to sales or restocking, when the system updates its dataset, then the adaptive learning algorithm should immediately reflect these changes in the next upsell recommendations provided to the sales team.

Monitoring the algorithm's learning curve and adjustments based on customer behavior shifts.

Given that customer buying patterns evolve over time, when the algorithm collects and analyzes data over a six-month period, then it should demonstrate at least three significant adaptations in its upsell recommendations based on new patterns.

Integration of third-party data sources to enhance the upsell recommendation engine.

Given access to external market trend data, when this data is processed by the adaptive learning algorithm, then the recommendations should incorporate insights from these external trends to refine upsell suggestions, resulting in an increase in order values by at least 10% within three months.

Customizable Recommendation Settings

User Story: As a sales leader, I want to customize the recommendation settings so that my team can align upsell options with our specific sales strategies and customer segments, improving performance.
Description: This requirement involves providing users with customizable settings that allow them to tailor the recommendations generated by the Dynamic Recommendations Engine according to specific criteria or business goals. Sales teams can modify parameters such as target customer segments, product categories, and campaign objectives to optimize the output of the recommendations engine. This functionality empowers sales representatives to align the recommendations with their unique sales strategies and customer engagement approaches, enhancing the overall effectiveness of the sales process.
Acceptance Criteria: User Customization of Recommendation Parameters

Given that the sales representative is logged into the SalesMap AI system, When they access the Dynamic Recommendations Engine settings, Then they should be able to modify parameters such as target customer segments, product categories, and campaign objectives.

Validating Personalized Recommendations

Given a sales representative has customized the recommendation settings, When they generate recommendations, Then the recommendations should reflect the modified parameters and align with the specified target customer segments and product categories.

Assessment of Recommendation Effectiveness

Given that the sales representative has implemented customized recommendations in a sales call, When they track the sales outcomes, Then there should be a measurable increase in upsell transactions compared to a baseline of previous sales outcomes without customization.

User Interface for Customization

Given that the sales representative is in the customization menu of the Dynamic Recommendations Engine, When they attempt to save their settings, Then the system should successfully save these settings and confirm with a success message.

Access and Permission Levels

Given that different user roles exist within the SalesMap AI platform, When a sales representative attempts to access the recommendation customization settings, Then they should have the appropriate permissions to modify and save these settings based on their user role.

Rollback of Changes

Given that a sales representative has made changes to the recommendation settings, When they choose to revert to the previous settings, Then the system should restore the prior configuration without loss of data or functionality.

Integration with Sales Analytics

Given that the customized settings have been applied, When the sales representative reviews the analytics dashboard, Then the performance metrics should reflect the impact of implemented upsell recommendations over time.

Performance Analytics Reporting

User Story: As a sales manager, I want to access performance analytics reports on upsell recommendations so that I can evaluate their effectiveness and refine our sales strategies accordingly.
Description: This requirement entails the development of a comprehensive reporting system that analyzes the performance of upsell recommendations made by the Dynamic Recommendations Engine. It should track metrics such as conversion rates, average transaction size, and customer feedback to provide insights into the effectiveness of recommendations. The reporting feature will help sales teams understand which recommendations perform best and why, enabling data-driven refinements to their sales strategies. Additionally, this analytics tool should present data in an easily digestible format, using visual elements for clarity and impact.
Acceptance Criteria: User Accesses Performance Analytics Reporting for Upsell Recommendations

Given a user with sales team permissions, when they access the Performance Analytics Report, then they should see metrics such as conversion rates, average transaction size, and customer feedback displayed visually in graphs and charts.

User Filters Report by Date Range

Given a user on the Performance Analytics Reporting page, when they apply a date range filter, then the report should update to reflect only the upsell recommendations made within that specified time period.

User Exports Performance Analytics Report

Given a user on the Performance Analytics Reporting page, when they click the export button, then the system should generate a downloadable report in Excel or CSV format containing all displayed metrics and visual elements.

User Views Recommendations by Performance

Given a user on the Performance Analytics Reporting page, when they select the option to view recommendations by performance, then the system should display a list of upsell recommendations sorted by highest to lowest conversion rates.

User Receives Insights on Recommendations

Given a user analyzing the Performance Analytics Report, when they click on a specific upsell recommendation, then the system should provide additional insights and reasons for its performance, based on historical data.

User Analyzes Customer Feedback Trends

Given a user with access to Performance Analytics Reporting, when they view the customer feedback section, then they should see trends over time displayed in a line chart or bar graph.

User Compares Recommendations Across Different Products

Given a user accessing Performance Analytics Reporting, when they select to compare recommendations, then the system should allow them to display a side-by-side comparison of upsell performance between different products.

Customer Segmentation Insights

Customer Segmentation Insights enables users to categorize customers based on their purchasing behavior, preferences, and interaction history. By understanding these segments, sales teams can tailor upsell strategies to specific groups, ensuring personalized and relevant recommendations that resonate with different customer profiles.

Requirements

Dynamic Customer Segmentation

User Story: As a sales manager, I want to dynamically segment customers based on their behaviors so that I can tailor my marketing strategies to meet the specific needs of each group.
Description: Dynamic Customer Segmentation enables SalesMap AI to automatically group customers into segments based on real-time data related to their purchasing behaviors, preferences, and interaction history. This segmentation enhances the precision of marketing campaigns by allowing sales teams to tailor communication and offers specifically to each group's characteristics. By utilizing advanced algorithms, this feature not only categorizes existing customers but also adjusts segments dynamically as customer behaviors change, ensuring that marketing efforts are consistently relevant and effective. The expected outcome is improved sales performance through more targeted strategies and increased customer satisfaction and retention.
Acceptance Criteria: Customer behavior shifts are detected in real-time, causing the dynamic customer segmentation feature to reclassify a specific customer group based on their recent purchasing patterns.

Given that the customer has made recent significant purchases, when the data is analyzed, then the customer should be assigned to a segment reflecting high-value clients within 5 minutes.

Sales team members need to view customer segments to tailor their communication strategies during a campaign launch.

Given that the sales team accesses the segmentation dashboard, when they filter by specific behaviors, then they should see a list of segmented customers categorized correctly within 10 seconds.

Marketing wants to execute a targeted email campaign based on the new customer segments created by the dynamic segmentation feature.

Given that new customer segments have been generated, when the marketing team selects a segment for an email campaign, then the system should allow the creation of customized messaging targeting that segment without errors.

A user requests to edit segment criteria based on recent data trends observed in sales performance reports.

Given that the user provides new criteria, when they submit the request for segment updates, then the system should successfully update the segments without downtime and notify the user of the completion within 15 minutes.

After segmenting customers dynamically, the system needs to reflect the changes on analytics reports immediately to aid in decision-making.

Given that the segments have been updated, when the sales team accesses the analytics report, then the report should show the latest segmentation details and corresponding performance metrics within 5 minutes.

Segmentation Heatmap Visualization

User Story: As a sales analyst, I want a visual heatmap of customer segments so that I can quickly identify which segments to target for sales campaigns based on performance.
Description: The Segmentation Heatmap Visualization provides a graphical representation of customer segments, illustrating the performance and activity levels of each segment visually through a heatmap interface. This feature will allow users to quickly identify high-potential segments to focus their selling efforts and understand which segments may require their attention. By offering intuitive visuals, sales teams can make data-driven decisions rapidly, aligning their strategies with market trends. The heatmap will also update in real-time to reflect current customer engagement and purchasing patterns, enabling agile decision-making and strategy adjustments.
Acceptance Criteria: Real-time updates reflecting customer engagement and purchasing patterns.

Given the user accesses the heatmap, when there is a change in customer engagement, then the heatmap should refresh within 5 seconds to reflect the latest data without requiring a manual refresh.

Accessibility and usability of the heatmap interface for different user roles.

Given the user has appropriate permissions, when they open the segmentation heatmap, then they should be able to view and interact with the heatmap without encountering errors or barriers, including users with visual impairments.

Ability to drill down into specific customer segments for detailed insights.

Given the user clicks on a specific segment in the heatmap, when they are presented with detailed data, then it should include customer names, purchasing behavior, and trend analysis for that segment.

Customizability of the heatmap color gradients to suit user preferences.

Given the user navigates to the settings page, when they select color customization options for the heatmap, then they should be able to choose from at least 5 different color schemes for data visualization.

Export functionality for heatmap data to integrate with other reports.

Given the user views the heatmap, when they select the export option, then the data should be exported in CSV format containing segment data and performance metrics without loss of information.

Historical data comparison on the heatmap for trend analysis.

Given the user accesses the heatmap, when they select a date range for historical comparison, then the heatmap should display previous engagement levels and performance metrics for the chosen segments.

User feedback and ratings on segment performance directly from the heatmap.

Given the user identifies a segment on the heatmap, when they select the feedback option, then they should be able to submit their insights and rate the segment's performance with a simple star rating system (1-5).

Automated Recommendations for Segmentation

User Story: As a marketing specialist, I want AI-generated recommendations for customer segmentation so that I can enhance my upsell strategies and improve overall campaign effectiveness.
Description: Automated Recommendations for Segmentation leverages AI to suggest optimal customer segments based on historical data, emerging trends, and predictive analytics. This feature will analyze customer interactions, feedback, and purchasing history to recommend who should be targeted for upsell and cross-sell strategies. By providing actionable insights, sales teams can effortlessly identify potential high-value customers and tailor their approaches accordingly. The accuracy and effectiveness of these recommendations will enhance the overall strategy for customer relationship management and maximize revenue opportunities.
Acceptance Criteria: Sales team member accesses the Customer Segmentation Insights feature to view automated recommendations for customer segments based on recent purchasing behavior and interaction data.

Given the sales team member is logged into SalesMap AI, when they navigate to the Customer Segmentation Insights section, then the system shall display a list of recommended customer segments prioritized by predicted upsell potential, derived from historical data analysis.

A sales manager receives automated recommendations for customer segments weekly to adjust quarterly sales strategies.

Given the sales manager has set up automated weekly email reports, when the next reporting cycle occurs, then they should receive an email containing the recommended customer segments along with specific insights into expected revenue from each segment.

Sales representatives use the automated recommendations to create targeted marketing campaigns directed at high-value customer segments identified by the system.

Given the representative selects a recommended customer segment, when they initiate a campaign based on the insights provided, then the system should provide a tailored campaign strategy including suggested messaging and offers relevant to that segment.

A sales analyst examines the effectiveness of the automated recommendations by comparing actual upsell results against predicted outcomes.

Given the sales analyst has access to performance metrics, when they compare upsell conversions from campaigns created based on automated recommendations, then they should find that at least 75% of recommended segments yield a higher conversion rate than average historical upsell rates.

Sales teams provide feedback on the accuracy of the automated segmentation recommendations after implementing them in their strategies.

Given the feedback is collected through a post-implementation survey, when 100% of the sales teams have rated the accuracy of the recommendations, then at least 80% must agree that the recommendations improved their targeting efforts and revenue generation.

Sales directors assess the frequency and relevance of the automated recommendations during quarterly business reviews.

Given the sales director presents to stakeholders, when discussing automated recommendations, then they must demonstrate that recommendations have been updated and are relevant at least once a month based on the latest customer interaction data.

Customizable Segmentation Criteria

User Story: As a product manager, I want the ability to customize segmentation criteria so that I can adapt my marketing strategies to align with changing customer behaviors.
Description: Customizable Segmentation Criteria allows users to define and create custom rules for segmenting their customer base according to various parameters such as demographic data, purchasing frequency, and product preferences. This feature empowers sales teams to refine their segmentation strategies according to their unique business goals and customer understandings, resulting in greater efficacy in targeting. By having the flexibility to alter segmentation criteria, teams can quickly adapt to market changes and product innovations, ensuring their strategies remain relevant to evolving customer needs.
Acceptance Criteria: User Customization of Segmentation Rules

Given a user with access to the Customer Segmentation Insights feature, when they specify custom rules for segmentation based on demographic data, purchasing frequency, and product preferences, then the system should successfully create a custom segment that meets the defined criteria which can be viewed in the dashboard.

Real-Time Segmentation Feedback

Given a user has defined custom segmentation criteria, when they apply these criteria, then the system should provide real-time feedback on the number of customers that fit each segment and the overall effectiveness of the segmentation strategy.

Edit and Update Segmentation Criteria

Given a user has created a custom segmentation rule, when they choose to edit that rule to reflect new business needs, then the system should allow modifications to be saved and reflected in the customer segmentation reports within two minutes.

Segmentation Insights Integration with Reporting

Given the implemented customizable segmentation criteria, when a user generates a sales report, then the report should include detailed insights based on the customized segments, allowing for targeted analysis and strategy adjustments.

User Training and Support for Custom Segmentation

Given new users of the SalesMap AI platform, when they explore the Customer Segmentation Insights feature, then they should have access to comprehensive training materials and support resources that guide them through creating and utilizing custom segmentation criteria effectively.

Integration with CRM Systems

User Story: As a sales representative, I want SalesMap AI to integrate with my CRM so that I can have the most up-to-date customer information at my fingertips without manual updates.
Description: The Integration with CRM Systems feature enables seamless synchronization between SalesMap AI and existing customer relationship management platforms. This integration ensures that customer data is continuously updated across the two systems, allowing sales teams to leverage real-time customer insights without manual intervention. By having access to the most current customer information directly from their CRM, users can streamline their workflow and enhance their engagement strategies with accurate and timely data. This integration also reduces the risk of data inconsistencies and enhances overall operational efficiency.
Acceptance Criteria: Salesperson views the Customer Segmentation Insights dashboard to check which customers are categorized as high-value segments before initiating a targeting campaign.

Given the CRM is integrated with SalesMap AI, when the salesperson accesses the dashboard, then all customer segments should be accurately displayed based on updated interaction history and purchasing behavior.

User adds a new customer to their CRM system, which automatically syncs to SalesMap AI for updated customer insights.

Given a new customer is added in the CRM, when the CRM sync process is triggered, then the customer should appear in SalesMap AI within 5 minutes with the correct segmentation information.

Sales manager reviews automated insights provided by SalesMap AI regarding customer behavior trends based on real-time CRM data.

Given the integration is functional, when the sales manager reviews the insights report, then the trends presented should reflect data from the CRM and be updated in real-time with no discrepancies.

Sales representative attempts to access a customer profile within SalesMap AI that has been recently updated in the CRM.

Given the profile has been updated, when the sales representative accesses the customer profile, then they should see the latest information including recent purchases and engagement history from the CRM.

The sales team runs a report on customer segmentation effectiveness after a marketing campaign to identify the ROI based on CRM input.

Given the CRM integration is working, when the sales team generates a segmentation effectiveness report, then the report should accurately reflect the campaign's performance metrics sourced directly from CRM data.

Sales representatives update customer engagement details in SalesMap AI that should also reflect back in the CRM system.

Given the integration between systems is active, when a sales representative updates customer engagement in SalesMap AI, then the changes should be visible in the CRM within 10 minutes with no data loss.

Performance Analytics Dashboard for Segmentation

User Story: As a team leader, I want a performance analytics dashboard for customer segments so that I can evaluate the success of our segmentation strategies and adapt as needed based on insights.
Description: The Performance Analytics Dashboard for Segmentation provides users with a comprehensive interface to monitor and assess the effectiveness of their customer segmentation strategies. Featuring key performance indicators (KPIs), conversion rates, and engagement metrics, this dashboard enables sales teams to analyze how well their segmented marketing efforts are performing. By utilizing this data, teams can identify successful approaches and areas needing refinement, resulting in continuous improvement of their sales strategies. The dashboard will be user-friendly, facilitating easy access to the metrics that matter most to sales teams.
Acceptance Criteria: User views the Performance Analytics Dashboard to assess customer segmentation effectiveness after implementing new upsell strategies for distinct customer segments.

Given a user is logged into SalesMap AI, when they navigate to the Performance Analytics Dashboard for Segmentation, then they should see a list of KPIs that includes conversion rates and engagement metrics for each customer segment.

Sales team analyzes the dashboard data to optimize their marketing strategies based on customer segmentation insights demonstrated through the dashboard.

Given the user is on the Performance Analytics Dashboard, when they select a specific customer segment, then the dashboard should update to display detailed metrics specific to that segment, including sales performance and customer feedback.

User requires insights on the changes in engagement or conversion rates after refining their customer segmentation strategy.

Given the user has set a date range, when they apply filters on the Performance Analytics Dashboard, then the system should show the comparative metrics for engagement and conversion rates before and after the segmentation changes.

Sales team members need to present a report on the effectiveness of their customer segmentation efforts to key stakeholders.

Given the user is in the Performance Analytics Dashboard, when they select the 'Export Report' option, then they should receive a download option that generates a report in PDF format containing all selected metrics along with visual graphs illustrating performance trends.

User seeks to understand the correlation between specific upsell strategies and their impact on customer segments.

Given the user has selected upsell strategies in the dashboard, when they request an analysis, then the dashboard should provide a clear visual representation of success metrics tied to those strategies, including upsell conversion rates for each targeted customer segment.

User attempts to access the Performance Analytics Dashboard from a mobile device to monitor performance on-the-go.

Given a user accesses the SalesMap AI application from a mobile device, when they navigate to the Performance Analytics Dashboard, then the dashboard should display correctly without loss of functionality or visually important data on the mobile screen.

Behavioral Trigger Alerts

Behavioral Trigger Alerts notify sales professionals when customers exhibit behaviors indicative of upsell potential, such as repeat purchases or browsing related products. By alerting users in real-time, this feature ensures timely and relevant engagement, increasing the likelihood of capturing additional sales opportunities.

Requirements

Real-time Behavioral Monitoring

User Story: As a sales professional, I want to receive immediate alerts whenever a customer demonstrates behavior that suggests upsell potential so that I can engage them at the right moment and maximize sales opportunities.
Description: This requirement focuses on the ability to continuously monitor customer behavior across the SalesMap AI platform, analyzing metrics such as repeat purchases, time spent on product pages, and interaction frequency with marketing materials. By leveraging advanced AI algorithms, this feature will identify behavioral patterns indicative of a potential upsell opportunity. The gathering and analysis of such data in real-time will enhance user engagement and foster timely interventions by sales professionals, ultimately leading to an increase in conversion rates and higher sales figures.
Acceptance Criteria: Real-time Monitoring of Repeat Purchases

Given that a customer has made a repeat purchase, when the system detects this behavior, then an alert should be sent to the sales professional within 5 minutes.

Monitoring Customer Interaction Frequency

Given that a customer has engaged with marketing materials at least 3 times in the past week, when the system identifies this pattern, then a behavioral trigger alert should be generated and sent to the sales team.

Analysis of Time Spent on Product Pages

Given that a customer spends more than 3 minutes on a specific product page, when this behavior is detected, then an alert should be dispatched to inform the sales representative of potential interest in the product.

Real-time Analysis of Browsing Related Products

Given that a customer browses 2 or more related products within a single session, when this behavior is identified, then the system must notify the sales team of a potential upsell opportunity immediately.

Integration with CRM for Behavioral Data

Given that behavioral data is captured in real-time, when this data is analyzed, then actionable insights should be generated and streamlined into the CRM within 10 minutes for the sales team to access.

Notification for High-Scoring Customer Engagement

Given that a customer reaches a predetermined engagement score based on their behaviors, when this threshold is crossed, then a notification should be issued to the sales professional in real time.

Retention of Behavioral History Log

Given that a customer's behavioral data has been logged, when the sales team reviews this data, then it should be accessible for at least the past 30 days to analyze trends and patterns.

Customizable Alert Settings

User Story: As a sales professional, I want to customize my alert settings to get notified only about the behaviors that are most relevant to my sales approach, so that I can focus on the leads that matter most to my business.
Description: This requirement enables users to configure their alert preferences based on specific criteria relevant to their sales strategies. Users can set thresholds for behavior triggers—such as the number of times a product is viewed or the frequency of purchases—allowing for a tailored approach to upsell and cross-sell opportunities. This flexibility ensures that sales professionals are alerted according to their unique sales tactics and customer engagement plans, improving the relevance and efficacy of their outreach efforts.
Acceptance Criteria: As a sales professional, I want to set a threshold for alerting me when a customer views a particular product more than 3 times in a week, so that I can engage them with personalized upsell offers.

Given that I have access to the customizable alert settings, when I set the product view threshold to 3 views in a week for a specific product, then I should receive an alert when a customer meets or exceeds this threshold.

As a sales professional, I want to configure alerts based on frequency of purchases, ensuring I am notified when a customer makes more than 2 purchases within a one-week period, to engage them with cross-sell opportunities.

Given that I have set up alerts for purchase frequency, when a customer exceeds 2 purchases in one week, then I will receive a notification of the customer’s behavior.

As a sales team manager, I want to review and change alert settings for team members to ensure they are aligned with our sales strategies, so that we can optimize our engagement tactics.

Given that I am logged in as a manager, when I access the team's customizable alert settings, then I can modify thresholds and preferences for each team member as needed.

As a sales professional, I want to receive alerts only during business hours to avoid disruptions during personal time, allowing for focused and timely follow-ups when I am available.

Given that I have set my alert preferences to include business hours only, when a customer behavior matches my alert criteria outside of those hours, then I should not receive any notification until the start of business hours.

As a user of the SalesMap AI platform, I want the ability to save my alert settings, so I can reuse them without needing to configure them from scratch each time.

Given that I have configured my alert settings, when I click on save, then my settings should be stored for future use without any errors.

As a sales representative, I want to be notified through multiple channels (email, app notifications) to ensure I don't miss important alerts about customer behaviors.

Given that I have configured multiple notification channels for my alerts, when a customer meets the alert criteria, then I should receive notifications via both email and app as per my preferences.

Integration with CRM Systems

User Story: As a sales manager, I want the Behavioral Trigger Alerts to sync with our CRM data, so that I can have a holistic view of customer interactions and history, which will support better decision-making in our sales approach.
Description: This requirement mandates that the Behavioral Trigger Alerts feature seamlessly integrates with existing CRM systems used by the sales professionals. This integration will allow for automatic updates of customer engagement records based on the behavioral triggers detected by SalesMap AI. By ensuring that all customer interactions and behaviors are consistently logged, this feature enhances the sales workflow, minimizes manual data entry tasks, and provides sales professionals with comprehensive insights into their customer history and engagement patterns.
Acceptance Criteria: Sales professionals receive an alert on their dashboard when a customer browses multiple related products on the SalesMap AI platform.

Given a customer visits the website and views related products multiple times, when the browsing behavior meets the predefined threshold for upselling, then a real-time notification should be generated and displayed on the user dashboard.

Upon completing a successful purchase, the system automatically updates the customer's engagement record in the CRM.

Given a customer completes a purchase, when the purchase is recorded, then the CRM should reflect the purchase details and any associated behavioral triggers within 5 minutes.

Sales representatives can view all alerts related to customer behavior in one centralized location within the CRM.

Given the sales representative accesses the CRM integration dashboard, when the alert overview page is opened, then the page should display all current behavioral trigger alerts sorted by urgency and relevance.

Sales professionals receive notifications for customers who have made repeat purchases within a specific time frame, indicating upsell opportunities.

Given a customer makes a repeat purchase within 30 days, when this behavior is detected, then the sales professional should receive an email notification detailing the opportunity for upselling.

The system logs customer interactions automatically when behavioral trigger alerts are activated, ensuring comprehensive engagement history.

Given a behavioral trigger alert is activated, when the customer interaction occurs, then the system should automatically log the interaction details into the CRM without manual input from the sales professional.

Performance metrics related to the effectiveness of Behavioral Trigger Alerts are available for review.

Given that the Behavioral Trigger Alerts feature is implemented, when users access the performance metrics dashboard, then they should be able to view statistics on the number of alerts generated, customer responses, and conversion rates over time.

Upsell Performance Analytics

Upsell Performance Analytics provides detailed reports and dashboards on upsell success rates, allowing sales teams to evaluate the effectiveness of their upsell strategies. By analyzing data on which upsells are performing well or not, teams can refine their approaches, optimize training, and drive higher revenue.

Requirements

Real-Time Upsell Data Visualization

User Story: As a sales representative, I want to see real-time upsell performance data on my dashboard so that I can quickly identify successful strategies and adjust my approach to maximize revenue.
Description: The Real-Time Upsell Data Visualization requirement focuses on providing an intuitive, interactive dashboard that displays key performance indicators of upsell activities. This feature will allow sales teams to visualize upsell success rates, trends, and patterns as they happen, enabling immediate adjustments to sales strategies. The data will be pulled from live transaction records and historical data to give an accurate and holistic view of upselling performance. This requirement enhances the user experience by providing actionable insights at a glance, improving decision-making and responsiveness to market changes.
Acceptance Criteria: Sales team members need access to real-time upsell data during their sales calls to adjust their pitches based on current performance metrics.

Given the sales dashboard is open, when a team member selects the 'Real-Time Upsell Data' tab, then they should see live updates of upsell success rates, categorized by product and time period, displayed in graphical format.

A sales manager wants to analyze upsell performance trends over the past quarter to refine strategic approaches.

Given the sales manager is viewing the upsell performance dashboard, when they filter data for the last three months, then the system should display trends in upsell success rates and highlight the top three products with the highest upsell rates.

Sales representatives need to receive alerts when upsell performance dips below a defined threshold to make proactive adjustments.

Given the defined threshold for upsell performance has been set, when the performance dips below this threshold, then an automated alert notification should be sent via email and within the dashboard alert system to the relevant sales team members.

A team lead is conducting a training session on upsell strategies and wants to show practical examples of upsell successes to the team.

Given the team lead is on the Real-Time Upsell Data Visualization dashboard, when they select the 'Top Upsell Cases' option, then the dashboard should generate a report highlighting successful upsell cases and the strategies employed, available for presentation.

The marketing team wants to assess how different upsell campaigns have performed over time.

Given that the marketing team accesses the upsell performance analytics feature, when they enable the 'Campaign Comparison' mode, then they should see a comparative analysis of upsell success rates across different campaigns and timeframes in a clear, interactive format.

Automated Upsell Performance Reports

User Story: As a sales manager, I want to receive automated reports on upsell performance so that I can analyze trends over time and make informed adjustments to our upselling strategies without the need for manual compilation.
Description: The Automated Upsell Performance Reports requirement involves creating a system that generates detailed reports on upsell performance at regular intervals. These reports will offer insights into upselling trends, success rates, and correlations with customer demographics and purchasing behavior. By automating this process, sales teams can save time that would otherwise be spent on manual reporting, allowing them to focus on strategy and execution. The reports will be distributed through email or accessible via the SalesMap AI dashboard, ensuring that teams stay informed and can adjust tactics based on data-driven insights.
Acceptance Criteria: Automated Upsell Performance Reports are generated at the end of each week to provide sales teams with up-to-date insights into their upsell strategies.

Given that the report generation is scheduled for Friday at 5 PM, when the report is generated, then an email is sent to all relevant sales team members containing a summary of upsell performance, including success rates and trends.

Sales team members access the Automated Upsell Performance Reports through the SalesMap AI dashboard to analyze current upsell performance metrics.

Given that the user is logged into the SalesMap AI dashboard, when they navigate to the reports section, then they should see the latest upsell performance report displayed with filtering options for date range and demographics.

Sales managers use the Automated Upsell Performance Reports to evaluate and refine upselling strategies during a quarterly review meeting.

Given that the report includes demographic correlations, when the sales manager presents the report at the quarterly review, then they must be able to identify at least three actionable insights that can be derived from the data.

The Automated Upsell Performance Reports are properly distributed to ensure all relevant stakeholders are informed about upselling strategies.

Given that the report is generated, when it is distributed, then all intended recipients (sales team, management, marketing) should receive an email notification with a link to the report within one hour of generation.

Sales teams receive notifications about the completion of their Automated Upsell Performance Reports to keep them informed.

Given that the report generation process is complete, when the report is available, then a push notification is sent to all mobile users who have opted in for report notifications.

The data included in Automated Upsell Performance Reports is accurate and reflects the current performance of upselling strategies within the last reporting period.

Given that the report is generated, when the data is cross-referenced with the CRM database, then the system should correctly reflect upselling success rates and other key metrics without discrepancies.

Upsell Strategy Recommendation Engine

User Story: As a sales strategist, I want an intelligent recommendation system for upselling so that I can focus on the most promising upsell opportunities and increase our success rates efficiently.
Description: The Upsell Strategy Recommendation Engine requirement is designed to utilize machine learning algorithms to analyze past upsell data and customer interactions. It will generate tailored recommendations for sales teams on which upsell opportunities to pursue based on statistical likelihood of success. This feature will not only save time by eliminating guesswork but also enhance the effectiveness of upsell campaigns by focusing efforts on high-potential customers. Integration with SalesMap AI's existing data sets will enhance the precision of recommendations and improve sales outcomes.
Acceptance Criteria: Sales team utilizes the Upsell Strategy Recommendation Engine during a weekly strategy meeting to identify top upsell opportunities for high-potential customers based on historical data.

Given that the sales team inputs historical upsell data, when the recommendations are generated, then at least 80% of the recommended upsell opportunities should show a statistical likelihood of success above 70%.

A sales representative uses the Upsell Strategy Recommendation Engine to prepare for a customer meeting, aiming to optimize their upsell pitch.

Given that the sales representative selects a customer profile, when the engine generates upsell recommendations, then the representative should receive at least three targeted upsell suggestions aligned with the customer's purchase history and needs.

The marketing team reviews the performance of the Upsell Strategy Recommendation Engine to assess its impact on overall upsell success rates after one month of implementation.

Given the implementation of the recommendation engine, when analyzing the upsell performance data, then there should be at least a 15% increase in the upsell conversion rate compared to the previous month without the engine.

Sales managers conduct a training session for the sales team on how to effectively use the Upsell Strategy Recommendation Engine to enhance their upselling techniques.

Given that a training session is held, when the sales team completes the session, then at least 90% of participants should express confidence in using the recommendation engine to guide their upselling strategies based on a follow-up survey.

A user must be able to retrieve and analyze the performance reports generated by the Upsell Performance Analytics feature post integration with the Recommendation Engine.

Given that the Upsell Strategy Recommendation Engine is integrated, when the user accesses the performance reports, then the reports should provide insights on recommended upsell opportunities alongside their actual performance metrics, with no discrepancies in data.

Sales teams are notified of new recommendations generated by the Upsell Strategy Recommendation Engine on a daily basis.

Given that the system is set up for daily updates, when a new recommendation is generated, then all relevant sales team members should receive a notification via the CRM system within 15 minutes of the recommendation being created.

Segmented Upsell Performance Analysis

User Story: As a marketing analyst, I want to see upsell performance broken down by customer segments so that I can identify which groups respond best and optimize our marketing strategies accordingly.
Description: The Segmented Upsell Performance Analysis requirement aims to dissect upsell performance data based on various segments such as customer demographics, purchase history, and engagement levels. This detailed analysis will allow teams to understand which segments respond best to upselling and why. By identifying the most effective segments for upselling, teams can tailor their strategies accordingly, leading to improved conversion rates and higher revenue per customer. This segmentation will be a crucial tool for refining marketing and sales approaches within the SalesMap AI platform.
Acceptance Criteria: Sales teams will access the Segmented Upsell Performance Analysis feature during their weekly strategy meeting to review upselling success across different customer demographics.

Given a valid user with managerial access, when they select the Segmented Upsell Performance Analysis report, then the system should display a dashboard showing upsell performance segmented by customer demographics (age, region, past purchase behavior).

A sales representative uses the Segmented Upsell Performance Analysis to identify which customer segment has the highest response rate to recent upselling campaigns in order to optimize future strategies.

Given a sales representative logged into the SalesMap AI platform, when they analyze the upsell performance data, then they should be able to see at least three customer segments with performance metrics (conversion rate, revenue generated) clearly displayed and sortable.

Marketing team reviews upsell performance to align future campaigns targeting specific segments identified as high-performing during the analysis.

Given a marketing manager reviewing the Segmented Upsell Performance Analysis, when they click on a high-performing segment, then the system should provide detailed insights about that segment's buying patterns, engagement levels, and suggested upsell strategies.

Sales teams need real-time updates on upsell performance to adjust their tactics and training approaches immediately.

Given an active sales team member, when they access the Segmented Upsell Performance Analysis within the dashboard, then the system should refresh data automatically and display the latest upsell performance metrics and segment insights with no more than a 5-minute delay.

The analytics dashboard should be accessible through various devices, ensuring teams can analyze upsell performance reports on-the-go.

Given a sales or marketing team member using a mobile device or tablet, when they navigate to the Segmented Upsell Performance Analysis dashboard, then the report should be fully functional and displayed responsively without loss of data or interaction capabilities.

Data security protocols need to ensure that only authorized users can access sensitive upsell performance metrics.

Given a user attempting to access the Segmented Upsell Performance Analysis report, when they are not logged in or lack the required permissions, then the system should deny access and display an appropriate error message indicating lack of authorization.

Upselling Training and FAQs Integration

User Story: As a sales team member, I want access to tailored training resources based on our upselling analytics so that I can improve my skills and understand the most effective tactics for increasing sales.
Description: The Upselling Training and FAQs Integration requirement focuses on providing sales teams with resources directly linked to upsell performance analytics. This feature will combine interactive training materials, FAQs, and best practices based on the upsell data insights generated by the platform. By having easy access to relevant training resources linked to real-time data analytics, teams can continuously enhance their skills, leading to higher success rates in upselling. This integration ensures that training is aligned with actual performance metrics and user needs, fostering a culture of learning and improvement.
Acceptance Criteria: Sales team utilizes the integrated Upselling Training resources during weekly strategy meetings to assess upsell performance and refine their sales techniques.

Given the training resources are linked to upsell performance analytics, when a sales representative accesses the training module, then they should see real-time data indicating the highest performing upsell strategies relevant to their current sales targets.

A sales manager reviews the impact of training materials on upsell success rates over a quarter.

Given the integration of training resources with performance analytics, when the sales manager pulls a report on upsell success rates, then the report should display a clear correlation between completed training sessions and increased upsell conversion rates.

New sales representatives are onboarded with the help of integrated FAQ resources which address common upselling challenges based on analytics data.

Given the presence of an FAQ section linked to upselling challenges, when a new sales representative navigates to the FAQ section, then they should find answers to at least 80% of the common queries identified in the analytics data.

Sales representatives complete a training module and immediately apply learned strategies in live selling scenarios.

Given the availability of interactive training modules, when a sales representative completes a training session, then they should demonstrate the ability to implement at least three new upselling techniques in the following week’s sales calls as recorded in the CRM.

Sales teams evaluate the effectiveness of their upselling strategy after accessing both training and performance analytics.

Given the integration of performance analytics with training modules, when sales teams meet to discuss upselling strategies, then they should be able to identify at least two specific areas for improvement based on the data reviewed from the analytics.

Sales teams leverage the highly rated upsell strategies from training modules for performance improvement.

Given the upselling training materials have ratings based on success rates, when a sales representative applies a strategy rated above 75% success in their calls, then they should achieve at least a 10% increase in their upsell conversion rate.

Monthly reviews are conducted to analyze upselling performance relative to training material usage.

Given the SalesMap AI platform tracks training material access and upsell performance, when compiling the monthly review report, it should include metrics that clearly show the relationship between training engagement and upsell success rates, allowing for data-driven decisions.

Cross-Sell Integration

Cross-Sell Integration complements upsell alerts by suggesting other products frequently purchased together based on transaction data. This feature helps sales teams maximize revenue by diversifying the upsell approach, encouraging customers to consider complementary items that enhance their overall purchase.

Requirements

Transaction Data Analysis

User Story: As a sales representative, I want to receive real-time suggestions of complementary products based on previous transaction data, so that I can offer personalized recommendations to customers and increase my sales effectiveness.
Description: The Transaction Data Analysis requirement involves developing algorithms that analyze historical and real-time transaction data to identify patterns in customer behavior. This functionality will enable the Cross-Sell Integration feature to suggest complementary products frequently purchased together by the same customer. By leveraging data analytics, SalesMap AI can enhance its recommendation accuracy, cater to individual customer preferences, and ultimately boost conversion rates and increase average transaction value. This requirement is vital for ensuring the feature is robust and effective in assisting sales teams in making informed recommendations based on actionable insights derived from data.
Acceptance Criteria: Cross-Sell Integration suggests products during checkout based on customer’s past purchases.

Given a customer has a transaction history of purchasing product A, when they are checking out with product A, then the system should recommend product B if product B is frequently purchased with product A.

Sales teams receive recommendations via the dashboard for enhancing sales strategies using transaction data.

Given the sales team accesses the dashboard, when they view the cross-sell recommendations, then the recommendations must show products that are frequently bought together, verified against transaction analytics reports.

Real-time updates of transaction data for dynamic product recommendations.

Given a new transaction occurs, when the transaction data is fed into the analysis algorithm, then the system must update the cross-sell recommendations within 5 minutes to reflect the latest data.

Historical data analysis to refine cross-sell recommendations based on seasonality and trends.

Given access to historical transaction data, when the system analyzes the past six months of data, then it should provide cross-sell recommendations that reflect seasonal trends in product purchasing.

Validation of recommendation accuracy using A/B testing among users during sales periods.

Given two groups of customers during a promotional period, when they are exposed to different sets of cross-sell recommendations, then the group with personalized recommendations should show at least a 15% higher conversion rate than the control group.

User feedback mechanism to improve recommendation algorithms.

Given a user opts to provide feedback on a recommendation, when they indicate whether the suggestion was helpful or not, then the system should log this feedback and use it to improve future recommendation accuracy.

User Interface for Recommendations

User Story: As a sales agent, I want to easily access cross-sell product recommendations on my dashboard, so that I can quickly leverage these insights during customer interactions and enhance my sales pitch.
Description: The User Interface for Recommendations requirement focuses on designing and implementing an intuitive user interface that displays cross-sell product recommendations within the sales dashboard. This interface will allow sales teams to view suggested products seamlessly while they interact with customer profiles, ensuring that the recommendations are easily accessible and actionable. The UI will highlight relevant products based on customer purchase history, visually categorize suggestions, and provide contextual information. A well-designed interface is crucial for maximizing user engagement and facilitating quick decision-making in sales interactions.
Acceptance Criteria: Sales team member views a customer profile on the dashboard and expects to see personalized cross-sell recommendations that are relevant to the customer's current purchase and past purchase history.

Given the sales team member is viewing a customer profile, when they navigate to the cross-sell recommendations section, then the interface should display at least three product recommendations that are frequently purchased together with the customer's previous purchases.

Sales team member interacts with the cross-sell recommendations and wants to view more details on a suggested product to assist in making a selling decision.

Given that the sales team member sees a product recommendation, when they click on the product, then a detailed view should open showing product information, images, and pricing to support informed selling decisions.

Sales team member is using the dashboard on a mobile device and needs to access cross-sell recommendations in a user-friendly layout.

Given the sales team member is using the dashboard from a mobile device, when they access the cross-sell recommendations, then the layout must be optimized for mobile view, displaying recommendations clearly without horizontal scrolling.

Sales team member wants to quickly identify which recommended products are top-selling items based on transaction data to prioritize sales efforts.

Given the sales team member is viewing cross-sell recommendations, when the products are displayed, then the interface should visually indicate top-selling products with a badge or indicator, allowing for quick identification.

Sales team member expects that the recommendations will update in real-time as they modify the customer’s profile or add items to the cart during the sales interaction.

Given the sales team member modifies customer profile details or adds items to the customer’s cart, when these changes are made, then the cross-sell recommendations should refresh within five seconds to reflect the new context.

Sales team member wants to see recommendations categorized by product type or category to facilitate easier browsing and selection during sales calls.

Given the sales team member is viewing the cross-sell recommendations, when the recommendations are displayed, then they should be organized into clear categories such as 'Similar Items', 'Customer Favorites', and 'New Arrivals' to aid navigation.

Sales team member inquires about the historical performance of cross-sell recommendations to evaluate their effectiveness in increasing sales.

Given the sales team member wants to analyze the effectiveness of cross-sell recommendations, when they access the analytics section, then there should be a report available showing metrics like conversion rates and total revenue generated from recommended products.

Integration with CRM Systems

User Story: As a CRM user, I want the cross-sell recommendations to sync with my existing customer data, so that I can ensure consistency in sales and marketing efforts and improve customer outreach.
Description: The Integration with CRM Systems requirement mandates developing a bridge between the Cross-Sell Integration feature and existing Customer Relationship Management (CRM) systems used by businesses. This integration will ensure that the complementary product recommendations align with customer data stored in CRM platforms, enabling sales teams to view tailored recommendations based on comprehensive customer profiles. The successful integration streamlines workflows and ensures consistency in data utilization across platforms, enhancing the collaborative efforts of sales, marketing, and service teams in driving further revenue through cross-selling opportunities.
Acceptance Criteria: CRM Integration with Cross-Sell Integration feature is activated during a sales team meeting to demonstrate how complementary product recommendations are displayed based on customer profiles stored in the CRM system.

Given a CRM system integrated with SalesMap AI, When a sales representative views a customer profile with a transaction history that includes specific products, Then the system should display relevant cross-sell recommendations based on frequently purchased items.

A sales representative is engaged in preparing an email campaign targeting a specific segment of customers who have previously purchased a selected product.

Given that the Cross-Sell Integration is functional, When the representative selects a product for the campaign, Then the system should automatically generate a list of at least three complementary products for cross-selling to those customers.

User tests the Cross-Sell Integration's compatibility with various CRM systems during the QA phase to ensure a seamless experience for users upgrading their CRM platforms.

Given different CRM systems are used, When the integration process is initiated, Then the Cross-Sell Integration should successfully connect without errors, and recommendations should be accurate based on the CRM data.

After implementing the Cross-Sell Integration, a sales manager conducts an analysis of its impact on sales revenue over a quarter.

Given the Cross-Sell Integration has been live for one quarter, When sales revenue is analyzed, Then there should be a measurable increase in revenue directly attributed to cross-selling efforts using the recommended products.

Training sessions are conducted for the sales team to familiarize them with using the Cross-Sell Integration feature in their CRM workflows.

Given the Cross-Sell Integration is live, When training is conducted, Then at least 90% of the sales team should demonstrate understanding and comfort in using the feature during practical scenarios.

Feedback is collected from the sales team regarding the usability of the Cross-Sell Integration feature within their existing CRM systems.

Given that the Cross-Sell Integration is being used, When the feedback is gathered after a month of use, Then the average satisfaction score should be at least 4 out of 5 regarding ease of use and effectiveness of the recommendations.

Performance Metrics Tracking

User Story: As a sales manager, I want to track the performance of cross-sell suggestions, so that I can analyze their impact on sales and make data-driven decisions for future improvements.
Description: The Performance Metrics Tracking requirement involves establishing metrics and KPIs that gauge the effectiveness of the Cross-Sell Integration feature. It includes setting up processes to monitor sales conversion rates from cross-sell suggestions, customer engagement levels, and overall revenue growth attributed to the feature. By tracking these metrics, the sales team can analyze the feature's impact over time, providing insights for ongoing improvements and validating the feature's effectiveness as a revenue-driving tool. This requirement is essential for demonstrating ROI and refining strategies based on empirical data.
Acceptance Criteria: Tracking revenue generated from cross-sell suggestions over a quarterly reporting period.

Given a quarter has ended, when the sales team generates a report on cross-sell conversions, then the report must quantify total revenue generated from cross-sell transactions, showing a minimum of 15% increase from the previous quarter.

Measuring conversion rates for cross-sell product suggestions.

Given a customer is presented with cross-sell suggestions at checkout, when the total number of completed transactions is counted, then the cross-sell conversion rate must be at least 10% of all transactions made in the evaluation period.

Analyzing customer engagement levels with cross-sell alerts via email campaigns.

Given customers receive emails with cross-sell product suggestions, when the email campaign is analyzed, then customer engagement must show an open rate greater than 25% and a click-through rate of greater than 5% within the first month.

Evaluating the impact of cross-sell suggestions on average order value (AOV).

Given the data from orders that include cross-sell suggestions, when the average order value is calculated, then the AOV must show an increase of at least 20% compared to orders without cross-sell suggestions during the same period.

Reviewing customer feedback on the relevance of cross-sell suggestions post-purchase.

Given customers complete a transaction that includes cross-sell items, when they are surveyed within a week of purchase, then at least 80% of respondents must indicate that the cross-sell suggestions were relevant to their purchase experience.

Monitoring the success rate of training sales teams on cross-sell integration features.

Given a training session has been conducted, when the sales team is assessed on their understanding of cross-sell strategies, then at least 90% of participants must correctly answer training assessment questions related to cross-sell scenarios.

Testing the system's ability to generate real-time analytics on cross-sell performance.

Given the Cross-Sell Integration feature is in use, when the real-time performance dashboard is accessed, then it must display up-to-date metrics on cross-sell conversions, revenue generated, and customer engagement metrics instantly, without delays.

Training and Support Documentation

User Story: As a member of the sales team, I want easy access to training materials on using cross-sell recommendations, so that I can quickly learn how to leverage this feature in my sales efforts.
Description: The Training and Support Documentation requirement entails creating comprehensive guides and training materials that educate sales teams on effectively utilizing the Cross-Sell Integration feature. This includes tutorials on interpreting recommendations, best practices for engaging customers using cross-sell tactics, and troubleshooting common issues. Ensuring that the sales team is well-informed and adequately trained is crucial for maximizing the feature's potential and fostering a culture of continuous learning within the organization, thus enhancing overall sales performance.
Acceptance Criteria: Training Sales Teams on Cross-Sell Integration Implementation

Given that the sales team has access to the training documentation, when they follow the tutorial on how to interpret cross-sell recommendations, then they should be able to identify at least 90% of the suggestions accurately in real sales scenarios.

Evaluating the Effectiveness of Best Practices

Given that sales representatives have implemented the documented best practices for engaging customers with cross-sell tactics, when a peer review is conducted, then at least 80% of their techniques should align with the provided best practices as per the documentation.

Troubleshooting Common Issues During Sales Calls

Given that a sales representative encounters a common issue during a sales call, when they refer to the troubleshooting section of the guide, then they should be able to resolve the issue successfully and report that at least 75% of common issues are addressed in the documentation.

Updating Training Materials Based on User Feedback

Given that the sales team has undergone training using the documentation, when they provide feedback, then at least 85% of the feedback should be considered for updates to the training materials to ensure continuous improvement.

Assessing Knowledge Retention Post-Training

Given that the sales team has completed the training on Cross-Sell Integration, when they take a knowledge retention quiz two weeks post-training, then they should score an average of at least 75% to indicate effective learning and retention of the material.

Monitoring Usage and Adoption of Cross-Sell Integration Features

Given that the Cross-Sell Integration feature has been active for one month, when usage metrics are analyzed, then at least 70% of the sales team should be utilizing the feature in their sales processes as recorded in the CRM.

Conducting a Follow-Up Training Session

Given that three months have passed since the initial training, when a follow-up training session is conducted, then at least 90% of attendees should report improved confidence and understanding of using the Cross-Sell Integration feature.

Personalized Upsell Playbooks

Personalized Upsell Playbooks offer customized strategies and scripts for sales representatives when presenting upsell opportunities. Tailored to individual customer profiles and historical interactions, these playbooks help streamline the conversation and increase the chances of successful upselling.

Requirements

Dynamic Playbook Generation

User Story: As a sales representative, I want dynamic playbooks to be generated for each customer based on their profile so that I can engage more effectively and increase my upselling success rate.
Description: The Dynamic Playbook Generation requirement enables the system to automatically create personalized upsell playbooks tailored to individual customer profiles. By analyzing historical interactions, customer preferences, and purchase behavior, the system will automatically generate dialogue scripts and strategies for sales representatives. This feature will enhance the relevancy of upselling efforts, increase sales representative effectiveness, and ultimately lead to higher conversion rates for upsell opportunities. It integrates seamlessly with existing customer relationship management (CRM) systems, ensuring that sales teams are equipped with the most current data and strategies at their fingertips.
Acceptance Criteria: Sales representative logs into SalesMap AI and selects an existing customer to view their profile.

Given a sales representative has access to a customer profile, When they initiate the upsell playbook generation, Then a personalized upsell playbook should be automatically created based on historical interactions and preferences.

A sales representative is using a generated upsell playbook during a customer call.

Given the sales representative is using a personalized upsell playbook, When they follow the scripted dialogue and recommend products, Then the upsell success rate should be tracked and reported to measure the effectiveness of the playbook.

The system's integration with an existing CRM is tested with live customer data.

Given the CRM is integrated with SalesMap AI, When a customer profile is updated in the CRM, Then the personalized upsell playbook should reflect the latest information from the CRM without manual intervention.

A sales manager reviews the performance metrics of the upsell playbooks.

Given the sales manager accesses the performance dashboard, When they view the metrics for upsell campaigns, Then the dashboard should display conversion rates and customer feedback that indicate the effectiveness of the dynamically generated playbooks.

Customer preferences data is added to the system for a specific customer.

Given new customer preference data has been added, When a playbook is generated for that customer, Then the playbook should include recommendations that align with the updated preferences.

An ongoing campaign is utilizing dynamically generated playbooks from SalesMap AI.

Given an ongoing sales campaign, When sales representatives utilize dynamic playbooks, Then the campaign's overall conversion should show a statistically significant increase in upselling compared to campaigns without dynamic playbooks.

A testing phase is conducted to evaluate the system's dialogue generation quality.

Given the dialogue scripts generated by the system, When they are analyzed by sales experts for clarity and relevance, Then at least 85% of the scripts should receive a positive evaluation for being clear and applicable to typical customer interactions.

Integrative Customer Insights

User Story: As a sales representative, I want to access comprehensive insights about my customers from various data sources so that I can tailor my upselling strategies effectively and close more sales.
Description: Integrative Customer Insights is a requirement that demands the integration of external customer data sources into the SalesMap AI platform. This feature will allow sales representatives to gain a comprehensive view of customer behaviors, preferences, and past interactions by consolidating data from multiple channels such as social media, previous purchase history, and customer feedback. By providing detailed insights into customer profiles, the platform will empower sales teams to customize their upsell approaches significantly and recommend products that align closely with customer interests, thus enhancing the likelihood of success in upselling.
Acceptance Criteria: Sales representatives should be able to access integrated customer insights while preparing for an upsell meeting with a potential client.

Given that the sales representative is logged into SalesMap AI, when they view the customer profile, then they should see consolidated insights from social media, purchase history, and customer feedback.

Sales representatives need to utilize personalized upsell playbooks based on the integrated customer insights during sales calls.

Given that the representative is on a call with a customer, when they navigate to the personalized upsell playbook section, then they must see playbooks tailored to the customer’s historical interactions and preferences.

Sales teams should evaluate the effectiveness of upselling strategies based on insights drawn from the integrated customer data.

Given that the sales team has executed upsell strategies over the last month, when they analyze the sales outcomes, then they must be able to track the conversion rate of upsells directly attributable to insights from the integrated data.

Sales representatives require insight into the impact of external data sources on customer engagement and upsell success.

Given that the sales representative accesses the dashboard, when they filter customer interactions by the date and types of external data used, then they should see a correlation report showing engagement levels and upsell success rates.

Sales representatives should receive alerts for high-priority upsell opportunities based on customer insights.

Given that the integrated data source identifies a significant change in a customer’s purchase behavior, when this is detected, then the sales representative should receive an immediate notification highlighting the potential upsell opportunity.

Performance Analytics Dashboard

User Story: As a sales manager, I want to view a dashboard that tracks the performance of upselling strategies so that I can evaluate their success and make data-driven improvements.
Description: The Performance Analytics Dashboard requirement focuses on creating a dedicated dashboard for tracking the effectiveness of upsell playbooks in real-time. This dashboard will visualize key metrics such as conversion rates, average order value increase, and customer engagement scores following upselling attempts. It will allow sales managers to identify trends, measure the effectiveness of various upselling strategies, and make informed decisions regarding future playbook modifications. By integrating insights from the dashboard into the SalesMap AI ecosystem, the team can continuously optimize upselling techniques and improve overall sales performance.
Acceptance Criteria: Users access the Performance Analytics Dashboard from the SalesMap AI platform to view real-time metrics related to upselling effectiveness.

Given that the user is logged into SalesMap AI, when they navigate to the Performance Analytics Dashboard, then they should see a visualization of conversion rates, average order value increase, and customer engagement scores based on the latest upselling attempts.

Sales managers filter and analyze data on the Performance Analytics Dashboard for specific time periods to assess upselling strategies.

Given that a sales manager is on the Performance Analytics Dashboard, when they select a date range filter, then the displayed metrics should update to reflect data only within that specified time frame.

Users receive actionable insights from the Performance Analytics Dashboard regarding upselling strategies that can be modified for better effectiveness.

Given that the performance metrics indicate a decline in conversion rates, when the user views the suggested modifications generated by the dashboard, then they should receive at least three actionable recommendations to improve upselling strategies based on historical data.

Sales representatives utilize insights from the Performance Analytics Dashboard during team meetings to discuss upselling techniques.

Given that a sales representative has accessed the Performance Analytics Dashboard, when they present the findings in a team meeting, then the team should be able to identify at least two key trends and discuss potential adjustments to their upselling approaches.

The Performance Analytics Dashboard integrates with existing sales data from the CRM to provide up-to-date performance insights.

Given that the dashboard is integrated with the CRM, when new upselling data is entered into the system, then the Performance Analytics Dashboard should reflect these updates without manual intervention within 5 minutes.

Sales managers compare the effectiveness of different upsell playbooks via the Performance Analytics Dashboard to determine which one works best.

Given that multiple upsell playbooks have been implemented, when the sales manager uses the dashboard to analyze conversion rates, then they should be able to compare these rates and identify the top-performing playbook based on the displayed data.

The Performance Analytics Dashboard provides a user-friendly experience for quick access to important performance metrics by sales staff.

Given that a user accesses the Performance Analytics Dashboard, when they review the dashboard layout, then they should find key performance metrics clearly displayed with intuitive navigation and minimal clicks required to access detailed insights.

User Feedback Loop

User Story: As a sales representative, I want to provide feedback on the upsell playbooks I use so that the system can become more effective and align with my selling experiences.
Description: The User Feedback Loop requirement outlines the need for a system to gather and analyze feedback from sales representatives regarding the upsell playbooks they utilize. This feedback will be incorporated into the playbook optimization process, allowing the system to adapt and enhance recommended strategies based on real-world user experiences. By continuously receiving input from the users who actively engage with the playbooks, SalesMap AI can ensure that playbooks evolve based on practical effectiveness, aiding in improved sales outcomes and representative satisfaction.
Acceptance Criteria: Sales representatives provide feedback after using the upsell playbooks during customer interactions.

Given a sales representative completes a call using a personalized upsell playbook, when they submit feedback regarding the playbook's effectiveness, then the feedback should be stored in the system and categorized for analysis.

The system analyzes feedback from multiple sales representatives to identify trends and areas for improvement in upsell playbooks.

Given a collection of feedback from at least 10 sales representatives, when the system processes this feedback, then it should generate a report that highlights common suggestions and areas of concern for playbook modifications.

Sales representatives receive notifications regarding updates or changes made to the upsell playbooks based on analyzed feedback.

Given that feedback has been incorporated into the upsell playbooks, when an update is made, then all relevant sales representatives should receive a notification outlining the changes and how to access the updated playbooks.

The feedback loop includes a satisfaction survey to gauge the effectiveness of playbook updates from sales representatives.

Given that an updated upsell playbook has been in use for one month, when a satisfaction survey is distributed to sales representatives using the playbook, then at least 75% of respondents should indicate that they find the playbook improvements beneficial.

Sales representatives can easily access historical feedback to understand how playbooks have evolved over time.

Given that a sales representative opens the feedback history section of the platform, when they search for feedback on a specific playbook, then they should be able to view a timeline of past feedback and subsequent changes made to the playbook.

The integration of user feedback directly impacts sales performance metrics tracked in the system.

Given that feedback has been collected and implemented into the upsell playbooks, when analyzing sales performance metrics, then at least a 15% increase in upsell success rates should be observed within three months post-implementation of changes.

AI-driven Recommendation Engine

User Story: As a sales representative, I want the system to recommend the best upsell products for each customer so that I can increase my chances of making a sale and improve customer satisfaction.
Description: The AI-driven Recommendation Engine requirement involves the incorporation of AI algorithms to analyze customer data and predict the best upsell products for each client. This engine will leverage machine learning to continually improve its recommendations based on interactions and outcomes from past upselling efforts. By using predictive analytics, the recommendation engine will identify the most effective products to promote, thereby increasing conversion rates and enhancing overall customer satisfaction. This engine will function as an integral part of the personalized upsell playbooks, ensuring representatives have the strongest offering suggestions.
Acceptance Criteria: Sales representative opens a customer's profile within SalesMap AI and accesses the AI-driven Recommendation Engine to receive personalized upsell suggestions based on the customer's previous purchases and interactions.

Given that the sales representative has accessed the customer's profile, when the Recommendation Engine is called, then it should return at least three tailored upsell product suggestions ranked by predicted conversion rate.

Sales representative engages a customer in a conversation about potential upsell products using the recommendations provided by the AI-driven Recommendation Engine.

Given that the sales representative is actively discussing upsell opportunities, when they present a product recommendation from the AI-driven engine, then the customer should receive the suggestion with an engagement rate of at least 60% based on follow-up feedback surveys.

Management monitors the effectiveness of the AI-driven Recommendation Engine by evaluating the upselling conversion rates over a specific period.

Given that the sales team has utilized the AI-driven Recommendation Engine for at least one month, when the conversion rates are analyzed, then the average conversion rate should show at least a 15% increase compared to the month prior to implementation.

Sales representatives provide feedback on the relevance of the upsell suggestions from the AI-driven Recommendation Engine after using it to engage with customers.

Given that sales representatives use the Recommendations Engine for upselling, when they submit their feedback on product relevancy, then at least 80% of feedback responses should rate the suggested products as 'relevant' or 'very relevant.'

Sales representatives utilize the AI-driven Recommendation Engine to assist with upselling during remote sales calls.

Given that a sales call occurs using the AI-driven Recommendation Engine, when upsell suggestions are provided during the conversation, then the call should end with at least one upsell conversion in 30% of the calls placed with recommendations.

AI algorithms in the Recommendation Engine are updated based on newly acquired customer interaction data for continual improvement of recommendations.

Given that new customer interaction data is collected weekly, when the AI algorithms are re-trained, then the accuracy of the upsell recommendations should improve by at least 10% based on test data validation.

The AI-driven Recommendation Engine integrates seamlessly with existing CRM software to automate upsell suggestions for sales representatives.

Given that the AI-driven Recommendation Engine is linked with the CRM system, when a sales representative views a customer profile, then upsell suggestions should be automatically displayed without any manual input required from the representative.

Press Articles

SalesMap AI Revolutionizes Sales Automation for Small Businesses with AI-Powered Insights: FOR IMMEDIATE RELEASE

November 30, 2024

SalesMap AI, a leading name in sales automation technology, has officially launched its innovative platform designed specifically for small to mid-sized businesses. This cutting-edge tool harnesses the power of artificial intelligence to streamline sales processes, elevate conversion rates, and maximize growth potential in today's competitive market.

The SalesMap AI platform introduces ground-breaking features such as intelligent lead scoring, which prioritizes high-conversion prospects, predictive analytics for market trend forecasting, and automated campaign recommendations tailored to individual business needs. With these advanced tools, small business owners can now better navigate the complexities of sales management.

"We understand that small to mid-sized businesses often lack the resources of larger enterprises, which is why we designed SalesMap AI with their unique challenges in mind," said Jessica Chang, CEO of SalesMap AI. "Our platform empowers users to streamline their sales processes and focus on what truly matters—growing their business."

SalesMap AI's user-friendly interface integrates seamlessly with existing Customer Relationship Management (CRM) systems, providing real-time insights through an intuitive dashboard. This not only minimizes manual tasks but also enhances strategic focus, enabling users to achieve greater operational efficiency.

Notable features of SalesMap AI include:

Engagement Heatmap: Visualize lead interaction levels to effectively prioritize follow-ups based on real-time engagement data.

Lead Activity Tracker: Monitor all interactions leads have with marketing materials, helping to customize communications and enhance conversion chances.

Conversion Probability Score: Utilize predictive analytics to assess the likelihood of conversions, optimizing sales efforts.

Multi-Channel Campaign Integration: Coordinate outreach across various platforms to ensure a consistent message reaches prospects, improving audience engagement and conversion rates.

In addition to these features, the platform also offers a robust onboarding process, complete with personalized learning pathways and interactive walkthroughs, enabling users to quickly grasp the tools available and leverage them effectively.

"Our goal is to not only provide tools but also ensure that users can make the most of these tools through comprehensive support and training," added Emily Rivera, Head of Product Development at SalesMap AI.

SalesMap AI continues to innovate, with future updates including dynamic lead scoring and intelligent upsell alerts designed to further enhance sales strategies and drive revenue growth.

To celebrate the launch, SalesMap AI is offering a complimentary 30-day trial to new users who sign up through their website.

For media inquiries, please contact:
Lisa Thompson
Marketing Manager
SalesMap AI
Email: press@salesmapai.com
Phone: +1 (555) 123-4567
Website: www.salesmapai.com

About SalesMap AI:
SalesMap AI is dedicated to empowering small to mid-sized businesses through innovative sales automation solutions. By leveraging AI technology, the platform helps users to improve efficiency, strategic decision-making, and ultimately drive growth.

END

SalesMap AI Launches Groundbreaking AI Features to Transform Sales Strategies: FOR IMMEDIATE RELEASE

November 30, 2024

SalesMap AI is excited to announce the launch of its latest features that leverage artificial intelligence to revolutionize sales strategies for small and mid-sized businesses. By employing advanced technologies, including predictive analytics and automated insights, the new functionality promises to enhance sales processes and maximize revenue growth potential.

The newly introduced features include the Dynamic Recommendations Engine and Intelligent Upsell Alerts, which utilize machine learning algorithms to identify optimal engagement strategies and upselling opportunities for sales professionals.

"These innovations are designed to provide actionable recommendations to sales teams, enabling them to convert more leads and maximize upsell potential," said Marco Jensen, Chief Technology Officer at SalesMap AI. "By instilling AI-driven decision-making into daily operations, businesses can increase their efficiency and effectiveness, ultimately leading to greater success."

The Dynamic Recommendations Engine analyzes user behavior and historical data to suggest engagement actions in real-time, allowing businesses to stay ahead of customer needs. Similarly, the Intelligent Upsell Alerts notify sales representatives of upselling opportunities based on customers' buying patterns, improving the chances of increasing transaction sizes.

Other major features include:

Real-Time Performance Adjustments: This tool allows users to fine-tune ongoing campaigns based on immediate feedback, helping improve campaign relevance and impact.

Channel Effectiveness Tracker: Evaluate the performance of various marketing channels, enabling better allocation of marketing resources.

The launch of these features follows extensive feedback from current users and is a testament to SalesMap AI's commitment to continuous improvement and innovation.

"Our user community is invaluable to our development process, and we strive to create solutions that directly address their needs," stated Tanya Liu, User Experience Specialist at SalesMap AI.

As part of the launch, SalesMap AI is hosting a free webinar series to introduce users to the new features and demonstrate how they can transform their sales strategies effectively.

For media inquiries, please contact:
Laura Klein
PR Coordinator
SalesMap AI
Email: pr@salesmapai.com
Phone: +1(555) 987-6543
Website: www.salesmapai.com

About SalesMap AI:
SalesMap AI is a trailblazer in developing intelligent sales automation solutions tailored for small to mid-sized businesses, helping them leverage technology to become more productive, strategic, and successful in their sales efforts.

END

SalesMap AI Enhances User Experience with New Training and Support Features: FOR IMMEDIATE RELEASE

November 30, 2024

SalesMap AI is thrilled to announce the introduction of new training and support features aimed at enhancing the user experience on their cutting-edge sales automation platform. These enhancements include an AI-Powered Onboarding Assistant and a Community Connection feature, providing users with personalized training support and peer learning opportunities.

The AI-Powered Onboarding Assistant is designed to adaptively guide users through the platform's features based on their specific roles and needs. By offering tailored recommendations and contextual help, new users can become proficient in using the platform quickly and easily.

"We recognize that effective onboarding is crucial to user satisfaction and success," said Anita Zhou, Head of User Experience at SalesMap AI. "Our AI-Powered Onboarding Assistant will significantly reduce the time it takes for users to navigate our platform and maximize its potential."

Additionally, the Community Connection tool will facilitate interactions among users, enabling them to share insights, strategies, and support through forums and chat groups. This peer-to-peer collaboration fosters a supportive learning environment, enriching the user experience.

Other key features launching alongside these enhancements include:

Resource Library Access: A comprehensive library of tutorials, webinars, and FAQs to empower users.

Goal-Oriented Progress Tracking: A feature that allows users to set and track their personal learning goals within the onboarding process.

SalesMap AI believes that investing in user education and community is essential for driving success among its clientele and amplifying the impact of the platform.

To kick off these enhancements, SalesMap AI will be sponsoring a live Q&A session with experts to address user questions and outline best practices for maximizing the platform.

For media inquiries, please contact:
James Connor
Media Relations Manager
SalesMap AI
Email: media@salesmapai.com
Phone: +1(555) 111-2222
Website: www.salesmapai.com

About SalesMap AI:
SalesMap AI is at the forefront of innovation in sales automation, focusing on giving small to mid-sized businesses the tools they need to thrive. Through continuous improvements and user-centered design, the platform aims to simplify sales processes and enhance the overall user experience.

END

Create products with ease

Product Details

Personas

Growth Gary

Tech-Savvy Tina

Budget-Conscious Brad

Analytical Alex

Strategy Sophia

Product Ideas

Product Features

Engagement Heatmap

Requirements

Interactive Engagement Visualization

Time-Based Engagement Analytics

Engagement Score Calculation

Customizable Notifications for Engagement Alerts

User Training and Support Documentation

Lead Activity Tracker

Requirements

User Interaction Logging

Lead Scoring Integration

Custom Reporting Features

Real-Time Notifications

User Dashboard Customization

Conversion Probability Score

Requirements

Dynamic Lead Scoring

Historical Data Analysis

Lead Segmentation Tools

Automated Campaign Recommendations

Real-Time Dashboard Enhancements

Feedback Loop Optimization

Requirements

Dynamic Feedback Analysis

Lead Engagement Scoring Module

Automated Engagement Adjustment Suggestions

Customer Feedback Integration Dashboard

Lead Segmentation Insights

Requirements

Engagement Metric Tracking

Behavioral Pattern Analysis

Demographic Data Integration

Personalized Follow-Up Recommendations

Automated Reporting Dashboard

Automated Engagement Reminders

Requirements

Threshold Settings for Engagement

Multi-Channel Reminder Options

Reminder Analytics Dashboard

Integration with Existing CRM

Customizable Reminder Templates

Behavioral Trend Analysis

Requirements

Data Pattern Recognition

Predictive Engagement Forecasting

Customizable Reporting Dashboards

Automated Engagement Alerts

Integration with External Platforms

Optimal Timing Insights

Requirements

Campaign Performance Analytics

Predictive Engagement Timing

Integration with Third-party Marketing Tools

User-friendly Dashboard Enhancements

Automated Performance Reports

Enhanced Lead Scoring Models

Channel Effectiveness Tracker

Requirements

Multi-channel Performance Analysis

Real-time Channel Insights

Channel Benchmarking

Dynamic Audience Segmentation

Requirements

Automated Engagement Tracking

AI-Powered Predictive Segmentation

Real-Time Segmentation Dashboards

Customizable Segmentation Criteria

Integration with Third-Party CRM Systems

Multi-Channel Campaign Integration