BeaconLyte

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

BeaconLyte

Tagline

Transform Retail, Illuminate Insights

Data-Driven Debbie

Name: Data-Driven Debbie
Description: Data-Driven Debbie is a mid-level manager in a retail chain who loves using analytics to drive performance. Daily, she sifts through mountains of data, looking for trends that can help her team make informed decisions on product placement and promotions. With a keen eye for detail, she regularly interacts with BeaconLyte to transform complex data into user-friendly insights that can be communicated across different departments, thereby fostering a collaborative decision-making environment.
Demographics: Age: 32, Gender: Female, Education: Bachelor's Degree in Business Administration, Occupation: Retail Operations Manager, Income Level: $75,000.
Background: Debbie grew up in a suburban neighborhood where her parents ran a small grocery store. From a young age, she was involved in the family business, learning the intricacies of retail. She went on to pursue a degree in Business Administration, focusing on marketing and analytics. After several years in the industry, she has worked her way up to a management position and is passionate about integrating data into every aspect of her work. Outside of work, she enjoys reading about new retail technologies and trends, as well as hiking during the weekends.
Psychographics: Debbie values efficiency, accuracy, and collaboration. She believes that data should drive every business decision and is motivated by the desire to improve team performance and customer satisfaction. Debbie is always on the lookout for innovative solutions that can streamline processes and increase productivity. Her interests include technology, data science, and sustainable retail practices, reflecting her commitment to making informed choices that positively impact her business.
Needs: Debbie needs real-time data analytics to provide insight into customer preferences and inventory levels. She seeks tools that enable easy collaboration with her team and better forecasting capabilities to minimize stockouts and maximize sales. Additionally, she desires user-friendly reporting features that can be shared with non-technical team members.
Pain: Debbie faces challenges with the overwhelming amount of data that can sometimes be misleading or irrelevant. She struggles with integrating various data sources into a cohesive narrative that can be understood by all stakeholders. Time constraints often hinder her ability to analyze data thoroughly before making recommendations, leading to decisions based on incomplete information.
Channels: Debbie primarily uses digital channels such as dashboards, email newsletters, webinars, and team collaboration tools like Slack or Microsoft Teams. She also engages with online forums and communities related to retail analytics for peer advice and industry trends.
Usage: Debbie uses BeaconLyte daily, spending about 1-2 hours analyzing data each morning. She accesses reports on a weekly basis to present findings at team meetings. During peak retail seasons, her usage escalates as she relies heavily on real-time data to make quick decisions that can impact sales.
Decision: Debbie’s decision-making process involves consulting data-backed reports and collaborative discussions with other department heads. She values input from her analytics team but ultimately makes decisions based on quantitative evidence combined with qualitative insights from her team.

Insightful Ivan

Name: Insightful Ivan
Description: Insightful Ivan is a forward-thinking marketing manager who harnesses BeaconLyte's powerful analytics tools to tailor marketing strategies for his retail brand. He thrives on transforming data into actionable marketing campaigns that resonate with customers, utilizing both real-time insights and historical trends to inform his strategies and improve engagement.
Demographics: Age: 37, Gender: Male, Education: Master's in Marketing, Occupation: Marketing Manager, Income Level: $90,000.
Background: Raised in a family of marketers, Ivan’s fascination with consumer behavior led him to pursue a career in marketing. He earned a Master's degree in Marketing, blending creativity with data-driven strategy. Over the past 15 years, he has worked in various retail sectors, honing his skills in campaign development and digital marketing. Ivan is also passionate about photography and often captures product shots for campaigns during his spare time.
Psychographics: Ivan is driven by a passion for storytelling and innovation in marketing. He values creativity combined with empirical evidence, believing that the best campaigns strike a balance between emotional resonance and data analytics. Ivan is motivated by seeing tangible results from his campaigns, whether it’s increased sales or enhanced brand loyalty. His interests include technology, digital marketing trends, and social media engagement strategies.
Needs: Ivan needs access to reliable analytics that can predict customer behaviors and trends. He looks for tools that enable him to create personalized marketing campaigns, analyzing customer segments, and performance metrics to refine his strategies continuously.
Pain: Ivan often encounters the challenge of aligning marketing insights with real-time data, making it difficult to pivot quickly during campaigns. He finds the integration of different marketing tools taxing and struggles with analyzing the impact of campaigns across various channels.
Channels: Ivan frequently uses social media platforms, email marketing analytics, and marketing automation tools to stay connected with consumer behaviors. He engages with webinars, industry blogs, and online marketing communities for continuous learning and inspiration.
Usage: Ivan interacts with BeaconLyte several times a week, often dedicating 3-4 hours during campaign launches to analyze performance metrics and consumer engagement data. He also accesses it before significant marketing meetings to present data-driven insights to his team.
Decision: Ivan's decisions are heavily influenced by data analysis and consumer feedback. He often collaborates closely with the sales team and utilizes A/B testing results to determine the best course of action for campaigns.

Supply Chain Samantha

Name: Supply Chain Samantha
Description: Supply Chain Samantha is an experienced logistics coordinator who uses BeaconLyte to optimize supply chain operations. Focused on minimizing costs while maximizing efficiency, she analyzes data trends to enhance procurement and distribution strategies, ensuring her retail chain has the right products at the right time.
Demographics: Age: 45, Gender: Female, Education: Bachelor's in Logistics and Supply Chain Management, Occupation: Logistics Coordinator, Income Level: $80,000.
Background: Samantha grew up in a military family, which instilled in her the values of organization and efficiency. After completing her education in Logistics, she began her career in supply chain management and has spent 20 years refining her skills across various retail channels. In her free time, Samantha enjoys organizing community events and volunteering.
Psychographics: Samantha values reliability, efficiency, and clear communication in her work. She believes that a well-optimized supply chain is crucial for the overall success of her retail business. Her motivations stem from the desire to see her company thrive and to contribute to sustainable practices in logistics. Additionally, she is interested in new technologies that can streamline supply chain processes and reduce waste.
Needs: Samantha needs accurate and timely data on inventory levels and supplier performance. She requires tools that provide alerts on stock shortages and effective forecasting to prevent disruptions in the supply chain.
Pain: Samantha struggles with the unpredictability of supply chain disruptions caused by external factors like market fluctuations or transportation issues, which makes her role challenging. There is often a lack of integration between the procurement and sales teams, leading to communication gaps.
Channels: Samantha primarily uses supply chain management software, industry newsletters, networking events, and professional forums to stay informed about best practices and industry trends.
Usage: Samantha uses BeaconLyte on a daily basis for about 2-3 hours to monitor inventory levels and analyze supply chain performance metrics. During peak seasons, her usage increases significantly as she relies on real-time data to react swiftly to changes.
Decision: Samantha's decisions are heavily driven by data analytics and departmental collaboration. She consults with her procurement team and relies on predictive analytics to inform her logistics strategies.

Customer-Centric Carl

Name: Customer-Centric Carl
Description: Customer-Centric Carl is a store manager who leverages BeaconLyte to understand customer behaviors and preferences, ensuring a personalized shopping experience that enhances customer satisfaction. He actively collaborates with his team to implement strategies based on data-driven insights to boost sales and foster loyalty.
Demographics: Age: 40, Gender: Male, Education: Associate Degree in Retail Management, Occupation: Store Manager, Income Level: $65,000.
Background: Growing up in a retail environment, Carl has always been fascinated by how stores operate. He started his career as a sales associate and worked his way up the ranks over 15 years. He is passionate about creating a positive in-store experience and often attends workshops to enhance his leadership skills. Outside of work, Carl enjoys coaching his son’s soccer team and gardening.
Psychographics: Carl believes in the power of active listening and customer engagement. He values strong relationships with his team and customers, motivated by the desire to create an exceptional shopping environment. His interests include customer service best practices and local community events, which he believes can lead to loyal customers.
Needs: Carl needs access to real-time customer data that reveals shopping patterns and preferences. He also desires training tools for his staff that can help improve customer engagement skills and adapt to business trends.
Pain: Carl faces challenges such as high staff turnover and inconsistent customer experiences that can negatively impact sales. He's often overwhelmed by the amount of data and sometimes struggles to translate insights into actionable customer service practices.
Channels: Carl primarily communicates with customers through in-store interactions, social media platforms, and community events. He also relies on feedback surveys and loyalty programs to gather insights from his clientele.
Usage: Carl uses BeaconLyte daily, spending approximately 1-3 hours analyzing customer feedback and sales trends. He reviews weekly reports to discuss performance with his team during staff meetings and to adjust strategies accordingly.
Decision: Carl's decision-making process is collaborative. He involves his staff in discussions about customer feedback and sales metrics, ensuring that strategies align with both team insights and data-driven analyses.

Product Ideas

SmartStock Alerts: SmartStock Alerts is a feature that leverages AI to send real-time notifications to Inventory Managers when stock levels fall below a defined threshold. This predictive tool will analyze sales trends and seasonal patterns to ensure timely replenishment, reducing the risk of stockouts and overstock scenarios, ultimately enhancing inventory performance.
Customer Journey Mapper: The Customer Journey Mapper provides an interactive visualization tool for Retail Analysts to trace customer interactions across various touchpoints. This feature enables users to identify pain points and opportunities, refining marketing and operational strategies based on comprehensive behavioral data and improving overall customer satisfaction.
Category Insights Dashboard: The Category Insights Dashboard aggregates performance metrics for product categories into a single, intuitive interface for Category Managers. This tool aids in quick decision-making by showcasing trends, customer preferences, and promotional performance, allowing for timely adjustments and strategy enhancements to drive sales and boost customer satisfaction.
Predictive Pricing Model: The Predictive Pricing Model incorporates machine learning algorithms to analyze competitor pricing, sales data, and customer behavior, offering dynamic pricing recommendations. This innovative feature enables Marketing Strategists to optimize pricing strategies in real-time, enhancing competitiveness and profitability while responding effectively to market changes.
KPI Performance Tracker: The KPI Performance Tracker is designed for Store Operations Managers to monitor key performance indicators through customizable, real-time dashboards. Users can set benchmarks and receive alerts on deviations, facilitating immediate action for improved operational efficiency and better customer experiences in store.
Real-Time Inventory Visualization: Real-Time Inventory Visualization provides a graphical interface for Inventory Managers to view stock levels and product movement in real-time across multiple locations. This intuitive feature enhances logistical efficiency and decision-making, supporting proactive inventory management and minimizing response times to market demand fluctuations.
AI-Enhanced Marketing Optimizer: The AI-Enhanced Marketing Optimizer analyzes past campaign data against customer behavior patterns to recommend future marketing strategies for Marketing Strategists. This feature aims to personalize campaigns, improve engagement, and significantly boost the effectiveness of advertising spend.

Product Features

Stock Health Indicator

The Stock Health Indicator provides a visual representation of inventory levels and trends, enabling Inventory Managers to quickly assess product viability. This feature helps identify slow-moving items and those at risk of stockouts, allowing for proactive inventory adjustments and enhanced decision-making.

Requirements

Real-Time Inventory Monitoring

User Story: As an Inventory Manager, I want real-time inventory monitoring so that I can quickly respond to stock level changes and avoid stockouts or excess inventory.
Description: This requirement involves implementing a real-time monitoring system for inventory levels across various channels, allowing Inventory Managers to have up-to-date visibility into stock status. This functionality should include alerts for low stock levels, enabling timely restocking decisions. By integrating this requirement within the BeaconLyte platform, users can ensure they are always aware of their inventory health, reducing the risk of stockouts and overstock scenarios. The expected outcome is improved inventory management efficiencies and enhanced customer satisfaction due to better product availability.
Acceptance Criteria: Inventory Manager receives real-time alerts for low stock levels during regular business hours, allowing them to promptly assess stock status and place orders.

Given an inventory item falls below the defined low stock threshold, When the threshold is breached, Then an alert is generated and sent to the Inventory Manager's dashboard and mobile device.

Inventory Manager accesses the dashboard to view the current inventory levels across all channels in real-time, enabling effective monitoring.

Given the Inventory Manager is logged into the BeaconLyte platform, When they navigate to the inventory dashboard, Then they should see real-time updates of stock levels for each product in all sales channels.

User conducts a review of the Stock Health Indicator to identify slow-moving products and adjust inventory levels accordingly.

Given the Stock Health Indicator is displayed on the dashboard, When the Inventory Manager selects a product, Then they should be able to view its sales trend over the past 30 days and receive recommendations for inventory adjustments.

The system captures and logs all low stock alerts for auditing and analysis purposes, ensuring compliance and enabling reporting.

Given the system has generated a low stock alert, When the alert is created, Then it should be logged in the alert history with timestamp, product details, and the user's action taken.

Inventory Manager uses predictive analytics to determine restocking needs based on historical sales data and seasonal trends.

Given that historical sales data is available for analysis, When the Inventory Manager initiates a predictive restocking analysis, Then the system should provide recommendations for restocking quantities based on predicted sales trends for the next month.

The platform displays a visual representation of inventory trends over the past quarter, aiding in strategic decision-making for future stock levels.

Given the Inventory Manager accesses the inventory trends report, When they select the ‘trends’ option, Then a chart showing inventory levels over the last three months must appear, highlighting peaks and troughs in stock levels.

Predictive Stock Analytics

User Story: As an Inventory Manager, I want predictive stock analytics so that I can optimize inventory levels based on anticipated demand changes and ensure better product availability.
Description: The predictive stock analytics requirement aims to integrate machine learning algorithms to analyze historical sales data and forecast future demand for products. This capability will enable the Stock Health Indicator to suggest optimal stock levels based on predicted trends, thus enhancing proactive decision-making for inventory adjustments. By leveraging this predictive insight, retailers can better align their stock with anticipated customer needs, ultimately driving sales and reducing waste.
Acceptance Criteria: Inventory Manager reviews the Stock Health Indicator dashboard at the end of the month to assess product viability and make informed reordering decisions for the following month based on predictive analytics.

Given historical sales data, when the predictive analytics algorithm runs, then it should calculate expected stock levels with at least 90% accuracy based on past trends.

During weekly inventory meetings, the Inventory Manager uses the Stock Health Indicator to identify slow-moving items that may require promotional strategies to boost sales.

Given the data input from the last quarter, when the Stock Health Indicator generates its report, then it should flag any items with a turnover rate below 10% as slow-moving.

As an Inventory Manager, I need to receive alerts about stock levels at risk of stockouts so I can take proactive measures.

Given the defined reorder threshold, when stock levels fall below this threshold, then an automatic alert should be sent to the Inventory Manager's dashboard and mobile device.

An Inventory Manager conducts a quarterly review of product performance, utilizing the predictive analytics feature to inform future purchasing decisions.

Given quarterly sales data and predictive analytics, when the Inventory Manager generates the report, then the report should include suggestions for optimal stock levels for the next quarter based on predictive trends.

During the setup phase, the Inventory Manager integrates the predictive stock analytics feature into existing inventory management systems.

Given the integration guidelines, when the Inventory Manager completes the setup, then the predictive stock analytics feature should successfully sync with existing inventory databases without data loss.

Slow-Moving Item Alerts

User Story: As an Inventory Manager, I want to receive alerts for slow-moving items so that I can take necessary actions to optimize my inventory and minimize losses.
Description: This requirement focuses on creating a notification system that alerts Inventory Managers when items are classified as slow-moving based on predefined criteria such as sales velocity or turn rate. By identifying these items, retailers can take action to promote or discount them, preventing potential losses from unsold stock. This feature enhances the proactive management of inventory by providing actionable insights that can lead to improved profit margins and reduced holding costs.
Acceptance Criteria: Inventory Manager receives a notification when the system identifies an item as slow-moving based on sales velocity over a specified period.

Given that the item has not sold more than the defined threshold units in the last 30 days, when the system analyzes inventory data, then an alert is triggered to notify the Inventory Manager of the slow-moving status.

The Inventory Manager views a dashboard displaying all slow-moving items to prioritize stock adjustments effectively.

Given that slow-moving items are identified, when the Inventory Manager accesses the Stock Health Indicator dashboard, then all slow-moving items are displayed with their respective sales data and recommended actions.

Inventory Manager takes action on a slow-moving item based on the alert and data provided by the system.

Given that a slow-moving item alert has been received, when the Inventory Manager reviews the item details and chooses an action (e.g., discount or promotion), then the action should successfully update the system and reflect in inventory records.

The system logs alerts for review and analysis by the Inventory Manager over time.

Given that an alert for a slow-moving item has been generated, when the notification is sent, then the system should log the alert details including timestamp, item ID, and action taken by the Inventory Manager for future reference.

Notification system maintains a history of slow-moving item alerts for trend analysis.

Given that slow-moving item alerts are generated, when viewed by the Inventory Manager in the reporting section, then the system should display a report showing the history of alerts for the past six months, including the actions taken.

Inventory Manager customizes thresholds for what constitutes a slow-moving item based on specific business needs.

Given that Inventory Managers need flexibility, when the settings are accessed, then they should be able to adjust the thresholds for slow-moving criteria (such as sales velocity) within the system without errors.

Customizable Dashboard Widgets

User Story: As an Inventory Manager, I want customizable dashboard widgets so that I can personalize my workspace to focus on the inventory metrics that are most important to my operations.
Description: This requirement outlines the development of customizable dashboard widgets for the Stock Health Indicator, allowing users to select and arrange data visualizations that best suit their needs. Users should be able to add metrics such as stock levels, turnover rates, and forecasted trends, thereby tailoring their experience and ensuring that the most relevant data is always at their fingertips. Customizable dashboards enhance user engagement and allow for quicker decision-making based on the tailored insights provided.
Acceptance Criteria: User customization of Stock Health Indicator dashboard widgets.

Given an Inventory Manager is logged into their BeaconLyte account, When they navigate to the Customizable Dashboard section, Then they should be able to add, remove, and rearrange dashboard widgets seamlessly according to their preferences.

Displaying accurate inventory metrics on the dashboard widgets.

Given the Stock Health Indicator is integrated with real-time inventory data, When an Inventory Manager adds a stock levels widget, Then the widget should display the correct current stock levels for each product in real-time.

Setting up alerts for stock levels through dashboard widgets.

Given an Inventory Manager wishes to receive alerts for stockouts, When they customize their dashboard to include an alert widget, Then the alert should trigger notifications when stock levels fall below a specified threshold.

Integrating forecasted trends into dashboard customization.

Given an Inventory Manager adds a forecast trends widget to their dashboard, When they configure it with historical sales data, Then the widget should display accurate forecasted trends for the selected products over the next 30 days.

User interaction with the dashboard widgets for improved decision-making.

Given an Inventory Manager interacts with the customized dashboard widgets, When they click on any widget displaying stock data, Then a detailed view should pop up with more in-depth analytics and insights for that product.

Saving and retrieving customized dashboard layouts.

Given an Inventory Manager has arranged their dashboard widgets, When they save their layout, Then upon re-login, the dashboard should reflect the previously saved arrangement of widgets, maintaining user settings.

Providing user guidance on widget customization.

Given an Inventory Manager accesses the Customizable Dashboard section for the first time, When they click on the 'Help' icon, Then a tutorial should appear guiding them through the process of adding and customizing widgets effectively.

Integration with Supply Chain Management Systems

User Story: As an Inventory Manager, I want the Stock Health Indicator to integrate with our SCM systems so that I can have accurate, real-time inventory data across all platforms.
Description: This requirement involves creating seamless integration between the Stock Health Indicator feature and existing Supply Chain Management (SCM) systems utilized by retailers. This integration will allow for the automatic updating of inventory levels across platforms, ensuring that all data is synchronized in real time. By connecting these systems, retailers can streamline their inventory management processes and enhance overall operational efficiency, enabling better forecasting and procurement decisions.
Acceptance Criteria: Seamless Inventory Update Triggered by SCM Changes

Given an SCM system has updated inventory levels, When the Stock Health Indicator is connected to the SCM, Then the inventory values in the Stock Health Indicator must reflect these changes in less than 5 minutes.

Real-time Data Synchronization Verification

Given that the Stock Health Indicator is hooked into the SCM system, When an Inventory Manager views the dashboard, Then the stock levels displayed must match the SCM records accurately within a tolerance of 1%.

Alerts for Low Stock Levels Integration

Given the Stock Health Indicator is integrated with the SCM systems, When stock levels fall below a predefined threshold, Then the system must send an automated alert to the Inventory Manager and update the dashboard to reflect the low stock status.

Historical Data Integration for Trend Analysis

Given historical inventory data within the SCM systems, When the Stock Health Indicator retrieves this data, Then the indicator should display trends over the past 30 days accurately on the dashboard without discrepancies.

User Access and Permissions for SCM Integration

Given different user roles in the retail system, When an Inventory Manager accesses the Stock Health Indicator, Then they should be able to view SCM integration details and make adjustments only if they have appropriate permissions.

Testing for Multiple SCM Platforms Compatibility

Given the Stock Health Indicator is designed for various SCM systems, When integrating with different SCM platforms, Then the feature should successfully synchronize inventory data without failure across at least three different SCM systems.

Performance Load Testing During High Transaction Volume

Given peak business hours where transactions are highest, When the Stock Health Indicator operates under these conditions, Then it should maintain real-time updates without lag or downtime for at least 95% of the period analyzed.

Historical Performance Reports

User Story: As an Inventory Manager, I want to generate historical performance reports so that I can analyze past inventory trends and make informed decisions for future stock management.
Description: The historical performance reports requirement aims to provide in-depth analytics features that allow Inventory Managers to generate reports based on past inventory levels, sales patterns, and stock health metrics. These reports should be customizable by date range, product category, and sales channels, offering insights into inventory effectiveness over time. This feature supports strategic planning by helping retailers identify successful stock strategies and areas for improvement, thereby facilitating data-driven decision-making.
Acceptance Criteria: Inventory Manager generates a historical performance report for the last quarter to analyze stock performance during peak sales periods.

Given that the Inventory Manager selects a date range of the last quarter, when they choose product categories and sales channels, then the system should generate a report that includes sales patterns and inventory levels for the specified time frame.

Inventory Manager customizes a historical performance report to focus on slow-moving items from the previous year.

Given that the Inventory Manager selects a date range for the previous year, when they filter the report by slow-moving product categories, then the generated report should highlight items with sales below a defined threshold along with corresponding inventory data.

An Inventory Manager analyzes inventory health metrics in historical performance reports to identify areas for improvement.

Given that the Inventory Manager accesses the historical performance report, when they review stock health metrics, then they should see a clear visualization of metrics like stockouts and excess inventory levels for selected product categories.

Inventory Manager shares the generated historical performance report with team members for collaborative decision-making.

Given that the Inventory Manager has generated the report, when they use the sharing feature, then the selected team members should receive an email with a link to the report and relevant access permissions.

Inventory Manager exports a historical performance report to analyze data in Excel for further analysis.

Given that the Inventory Manager has generated the report, when they choose to export the report, then the system should provide an Excel file that accurately reflects the displayed data, including all selected filters and metrics.

Inventory Manager schedules automated generation of monthly historical performance reports.

Given that the Inventory Manager accesses the scheduling feature, when they configure the report settings to run monthly, then the system should successfully schedule the report generation and send automatic email notifications with the report attached on the selected date each month.

Inventory Manager reviews historical performance report trends over multiple time periods for strategic planning.

Given that the Inventory Manager selects multiple date ranges from various time periods, when they initiate the report generation, then the system should display comparative analytics showing trends over the selected periods, such as sales growth or decline and inventory turnover rates.

Restock Recommendations

Restock Recommendations utilize AI to analyze historical sales data and provide data-driven suggestions on optimal reorder quantities. Inventory Managers can simplify and expedite the restocking process by ensuring they have the right amount of stock at the right time, minimizing wasted resources and maximizing sales.

Requirements

AI-Powered Sales Analysis

User Story: As an Inventory Manager, I want AI to analyze historical sales data so that I can make informed decisions about restocking and avoid stockouts or overstock situations.
Description: The AI-Powered Sales Analysis requirement involves leveraging artificial intelligence algorithms to process and analyze historical sales data. This feature will provide insights into sales trends, seasonality, and the factors impacting sales performance. The analysis will help inventory managers make informed decisions about restocking and inventory optimization. By delivering actionable insights, this requirement enhances the overall functionality of BeaconLyte, enabling retailers to align their inventories with customer demand more effectively.
Acceptance Criteria: AI Analysis of Sales Data Over a Month for Restocking Decisions

Given historical sales data for the past month, when the AI analyzes the data, then it should produce a report identifying top-selling products, seasonal trends, and recommended reorder quantities based on inventory levels.

User Experience of Accessing AI Insights through Dashboard

Given that an inventory manager logs into the BeaconLyte platform, when they navigate to the 'Restock Recommendations' dashboard, then they should see AI-generated insights clearly displayed, including visual graphs of trends and actionable recommendations.

Validation of AI-Driven Recommendations Against Actual Sales Data

Given a completed sales cycle after recommendations have been implemented, when comparing actual sales figures with AI-generated recommendations, then the recommendations should reflect a minimum increase of 10% in sales for the products restocked.

Integration Check with Existing Inventory Management Systems

Given that the Restock Recommendations feature is enabled, when it interfaces with the existing inventory management system, then the data exchange should occur without errors, and updated stock levels should reflect accurately within both systems.

Testing of Real-Time Alerts for Low Stock Items

Given that sales data is continuously updated, when stock levels fall below a predetermined threshold, then the system should send real-time alerts to the inventory manager indicating items that require restocking.

User Feedback on AI-Generated Insights

Given that inventory managers utilize the AI-Powered Sales Analysis feature for decision making, when they provide feedback on the usefulness of the insights, then at least 80% of users should report that the insights significantly aid in their restocking decisions.

Assessment of Speed in Generating Reports

Given a request for a sales analysis report, when the inventory manager initiates the report generation, then the system should return the analysis report within 5 seconds for datasets containing up to 10,000 sales records.

Customizable Reorder Alerts

User Story: As an Inventory Manager, I want to customize reorder alerts for different products so that I can prioritize stock replenishment based on each product's sales pattern.
Description: The Customizable Reorder Alerts requirement allows inventory managers to set personalized thresholds for restocking notifications based on different products or categories. This feature provides flexibility, enabling users to tailor alert settings to their preferences and operations, ensuring that they are notified just in time to place orders for essential stock items. This personalization improves efficiency and responsiveness in managing inventory.
Acceptance Criteria: Inventory Manager sets a personalized reorder threshold for a high-demand product.

Given the Inventory Manager accesses the Customizable Reorder Alerts section, when they set a reorder threshold for a product, then the system must successfully save the threshold and display it in the alerts dashboard.

Inventory Manager modifies an existing reorder threshold for a seasonal product.

Given the Inventory Manager has previously set a reorder threshold for a seasonal product, when they change the threshold to a new value, then the system must update the alert in real-time without any errors.

Inventory Manager receives a reorder alert when stock falls below the threshold.

Given the Inventory Manager has set a reorder threshold, when the stock level of the corresponding product falls below that threshold, then the system must automatically send a notification alert to the Inventory Manager's dashboard and email.

Inventory Manager sets multiple thresholds for different product categories.

Given the Inventory Manager wants to create different thresholds for multiple categories, when they input distinct thresholds for each category, then the system must reflect all thresholds accurately in the alerts overview section.

Inventory Manager tests the threshold notification system by lowering stock levels.

Given the Inventory Manager lowers the stock of a predefined product to test the alert system, when the stock level goes below the custom threshold, then the system must generate and display an alert within 5 minutes.

Inventory Manager reviews the performance of reorder thresholds in a reporting dashboard.

Given the Inventory Manager wants to analyze the effectiveness of the reorder thresholds set, when they navigate to the reporting dashboard, then the system must display data on restock alerts triggered versus stock-outs experienced for each product category.

Inventory Manager receives an alert for a product with no sales history.

Given the Inventory Manager sets a reorder threshold for a new product with no prior sales history, when the product stock level falls below the threshold, then the system must handle this situation gracefully and notify the Inventory Manager without errors.

Integration with Current Systems

User Story: As an Inventory Manager, I want BeaconLyte to integrate with our existing systems so that I can leverage current data for more accurate restock recommendations without additional manual input.
Description: The Integration with Current Systems requirement ensures that BeaconLyte can seamlessly connect with existing inventory management and sales systems in use by retailers. This integration is vital for pulling real-time data into the platform without disrupting current workflows. By having this requirement met, retailers can ensure their operations remain uninterrupted and gain insights without requiring significant changes to their existing software environments.
Acceptance Criteria: Integration with Existing Inventory Management System

Given that the retailer has an existing inventory management system, when BeaconLyte is configured for integration, then data from the inventory management system should be successfully imported into BeaconLyte within 5 minutes without errors.

Real-time Sales Data Sync

Given that sales are occurring, when a sale is made in the retail system, then BeaconLyte should reflect that sale in its analytics dashboard in real-time, updating inventory levels accurately.

User Access and Permissions Management

Given that the retailer has specified user roles, when users attempt to access BeaconLyte, then access should be granted or denied based on their roles as defined in the integration settings.

Seamless Workflow without Disruption

Given that the integration is live, when operations are being conducted in the retailer's existing system, then there should be no impact on the users' ability to operate or any workflow delays at any time.

Data Accuracy and Validation

Given that data has been imported into BeaconLyte, when a sample of inventory and sales data is cross-verified with the existing system, then a maximum discrepancy of 2% in data accuracy should be tolerated.

Integration Error Alerts

Given that an error occurs during data synchronization, when the error is detected, then the system should send an alert to designated users within 5 minutes with details of the issue.

End-user Training on Integration Features

Given that the integration is complete, when a training session is held for users on how to utilize the new features, then at least 80% of participants should be able to demonstrate proficiency in using those features within a week of training.

Dynamic Stock Level Recommendations

User Story: As an Inventory Manager, I want the system to provide dynamic recommendations for stock levels so that I can adjust inventory in real-time, maximizing sales while minimizing excess stock.
Description: The Dynamic Stock Level Recommendations feature uses real-time sales data and predictive analytics to suggest optimal stock levels on a rolling basis. This requirement allows inventory managers to maintain suitable stock levels that adapt to changing sales velocities and seasonal trends. This proactive management of stock levels reduces waste and enhances the potential for sales growth by ensuring inventory aligns with market demands.
Acceptance Criteria: Inventory Manager uses the Dynamic Stock Level Recommendations feature at the beginning of a new season to adjust stock levels based on historical data and predicted trends.

Given the historical sales data for the previous seasons, when the inventory manager accesses the Dynamic Stock Level Recommendations, then the system should display recommended stock levels that reflect a 20% increase in stock for high-demand items and a 10% decrease for low-demand items.

The inventory manager receives real-time notifications for stock levels that fall below the recommended thresholds due to sudden sales spikes.

Given that sales of a specific item have increased by more than 30% in the last week, when the stock level drops below the recommended threshold, then the system should send an alert to the inventory manager via email and SMS notification.

After implementing stock level recommendations, the inventory manager wants to review the effectiveness of the adjustments made over a three-month period.

Given the stock levels were adjusted based on the recommendations, when the inventory manager compares the sales reports from the previous three months with the current month, then the data should show a minimum 15% increase in sales for items that were restocked according to the recommendations.

During a promotional event, the inventory manager utilizes the Dynamic Stock Level Recommendations to decide on stock replenishment.

Given that a promotional event is active, when the inventory manager activates the Dynamic Stock Level Recommendations, then the system should provide stock increase suggestions based on at least 10% higher than average sales velocity for items expected to sell well during the event.

The inventory manager reviews the impact of dynamic recommendations on overall inventory costs after 6 months of usage.

Given the inventory management system has been in use for 6 months, when the inventory manager reviews the cost reports, then the total inventory holding costs should have decreased by at least 20% compared to the previous period prior to implementing the Dynamic Stock Level Recommendations.

Inventory managers need to adjust for unexpected market changes due to new competitor actions or market trends.

Given a significant market change occurs, when the inventory manager inputs new trend data into the system, then the Dynamic Stock Level Recommendations should adjust stock levels within two hours based on the updated market data.

User-Friendly Dashboard for Restocking Insights

User Story: As an Inventory Manager, I want a user-friendly dashboard that clearly displays restocking recommendations and sales analytics so that I can quickly make informed inventory decisions.
Description: The User-Friendly Dashboard for Restocking Insights requirement calls for designing an intuitive interface that displays restocking recommendations and sales analytics in a clear and digestible manner. This dashboard will facilitate ease of use for inventory managers, improving access to crucial information. By consolidating data visuals, the dashboards support quick decision-making and enhance the overall user experience with BeaconLyte.
Acceptance Criteria: Inventory Manager views the restock recommendations dashboard for the first time, looking to understand the suggested reorder quantities based on previous sales data.

Given the Inventory Manager is on the restock recommendations dashboard, When the page loads, Then the dashboard should display an overview of recommendations clearly segmented by product categories with relevant visuals (charts, graphs).

The Inventory Manager needs to evaluate historical sales trends to make informed decisions about restocking various products.

Given the Inventory Manager selects a product from the recommendations list, When they click on the product, Then the dashboard should display a detailed view including a historical sales graph, current stock levels, and sales forecasts for the next month.

An inventory manager is implementing recommendations based on the dashboard insights to restock items in a timely manner.

Given the restock recommendations are displayed on the dashboard, When the Inventory Manager accesses the reorder quantities, Then the system should allow them to adjust quantities easily with instant recalculations being displayed on the dashboard without needing a page refresh.

The Inventory Manager wants to receive alerts for low stock items identified by the restock recommendations dashboard.

Given the Inventory Manager is on the dashboard, When items fall below the preset threshold level, Then the system should trigger a real-time alert on the dashboard and send a notification to the Inventory Manager's email.

The Inventory Manager is looking to verify that the data on the dashboard reflects the latest sales information after the recent updates.

Given the Inventory Manager refreshes the dashboard, When the new sales data is available, Then the dashboard should refresh automatically and display updated sales figures and restock recommendations based on the latest data.

Seasonal Demand Alerts

Seasonal Demand Alerts notify Inventory Managers about expected fluctuations in stock requirements based on historical seasonal trends. This feature empowers users to prepare in advance for demand spikes or declines, reducing the risk of stockouts during peak periods and ensuring customer satisfaction.

Requirements

Historical Data Analysis

User Story: As an Inventory Manager, I want the system to analyze historical sales data so that I can identify seasonal trends and plan my inventory needs accordingly.
Description: This requirement focuses on the analysis of historical sales data to identify seasonal trends and patterns. It is essential for predicting future demand fluctuations, allowing Inventory Managers to prepare for varying stock requirements effectively. By aggregating and analyzing data from previous years, the system can recognize patterns that inform seasonal demand alerts, ensuring that retailers can optimize their inventory levels when anticipating increases or decreases in consumer demand. This functionality will enhance the accuracy of Inventory Managers’ forecasts, leading to improved stock availability and reduced instances of both overstock and stockouts, ultimately improving customer satisfaction and sales performance.
Acceptance Criteria: Historical Sales Data Trends Analysis for Seasonal Demand Alerts

Given that the system has access to at least 3 years of historical sales data, when the Inventory Manager requests an analysis, then the system should display identified seasonal trends and patterns with a confidence interval of at least 95%.

User Notification for Anticipated Demand Spikes

Given that a significant demand spike is predicted based on historical data, when the seasonal demand alerts feature triggers, then the Inventory Manager should receive a notification at least 7 days prior to the expected spike.

Accuracy of Predicted Inventory Requirements

Given the identified seasonal trends, when inventory requirements are predicted for the upcoming season, then the forecasted stock levels should be within 10% of actual sales for the first month of the season.

Integration with Existing Inventory Management Systems

Given that an existing inventory management system is in place, when the Seasonal Demand Alerts feature is implemented, then it should seamlessly integrate and reflect updates in stock requirements within 2 hours.

User Feedback on Seasonal Demand Alerts Effectiveness

Given the implementation of seasonal demand alerts, when collecting feedback from Inventory Managers after the first season of use, then at least 80% of users should report improved stock availability and customer satisfaction.

Real-time Adjustment of Alerts Based on Inventory Changes

Given that ongoing sales data is being tracked, when there is a significant change in inventory levels, then the system should automatically adjust the seasonal demand alerts in real-time without user intervention.

Reporting on Alerts Activation Frequency and Outcomes

Given that the alerts feature has been active for one season, when generating a report on alert activation frequency, then the report should include the total number of alerts triggered and the corresponding impact on stock levels, sales, and stockout incidents.

Real-Time Notification System

User Story: As an Inventory Manager, I want to receive real-time notifications about demand changes so that I can adjust my inventory levels quickly and avoid stockouts.
Description: This requirement entails developing a robust real-time notification system that alerts Inventory Managers about changes in expected demand based on newly updated data. The notifications should be timely and relevant, enabling managers to respond quickly to fluctuating inventory requirements. The system will integrate with the existing platform to use predictive analytics, sending alerts via multiple channels (e.g., email, mobile app, dashboard notifications) to ensure that managers have immediate access to crucial information while they monitor stock levels. This proactive approach will enhance inventory management efficiency and lead to better customer satisfaction by minimizing stockouts during peak demand periods.
Acceptance Criteria: Inventory Manager receives a notification alerting them of an upcoming seasonal demand spike for a specific product category that they manage.

Given the Inventory Manager is logged into BeaconLyte, when the system detects a seasonal demand spike based on historical data, then an alert is sent via email, mobile app, and dashboard notification within 5 minutes of the data update.

An Inventory Manager reviews the dashboard to assess current stock levels and receives a real-time alert about a sudden drop in expected demand for a specific product.

Given the Inventory Manager accesses the dashboard, when the system detects a sudden drop in demand expectation, then a dashboard notification is generated and displayed immediately, along with an email alert sent to the manager.

An Inventory Manager is notified about a stockout risk for a critical item ahead of a peak shopping season.

Given the Inventory Manager is monitoring stock levels, when the stock quantity for a critical item falls below the predefined threshold, then an alert is sent through all channels including SMS and push notifications, highlighting the stockout risk.

An Inventory Manager is validating the efficiency of the real-time notification system during a trial period with historical data inputs.

Given the real-time notification system is operational, when the historical data for a recent holiday season is processed, then the alerts generated should match the actual fluctuations recorded during that time, with at least 95% accuracy in predicted stock fluctuations.

Inventory Manager needs to adjust stock levels based on received alerts during the transition into a peak sales season.

Given the Inventory Manager receives alerts regarding anticipated demand changes, when adjustments are made to stock levels, then the system should reflect these adjustments accurately, showing real-time data updates on the dashboard within 10 minutes of changes being made.

An Inventory Manager has customized alert preferences for different product categories, intending to optimize the relevance of notifications.

Given the Inventory Manager customizes their alert preferences for various product categories, when a significant change occurs in any of those categories, then only the relevant alerts are sent through the selected channels, ensuring the manager receives targeted information.

Monitoring the notification system's performance and response time to alerts during actual demand fluctuations.

Given the real-time notification system is actively in use, when an actual demand fluctuation occurs, then the alert notifications must be received by the Inventory Manager within 5 minutes, and the system should log the response times for review.

Customizable Alert Settings

User Story: As an Inventory Manager, I want to customize my alert settings so that I can receive notifications that are tailored to my specific inventory needs and preferences.
Description: This requirement involves the implementation of customizable settings for seasonal demand alerts, allowing Inventory Managers to tailor alert thresholds and criteria based on their specific needs and preferences. Managers should be able to define parameters such as acceptable stock levels, notification frequency, and channels of communication. This flexibility ensures that alerts are relevant and actionable, helping managers to prioritize their responses effectively. By enabling customization, the feature ensures that each retailer can optimize their approach to inventory management, aligning with their unique operational practices and customer demands, which is crucial for effective seasonality management.
Acceptance Criteria: Inventory Managers need to customize their alert settings based on anticipated stock fluctuations during the holiday season to ensure appropriate inventory levels.

Given an Inventory Manager accesses the Customizable Alert Settings, when they set the acceptable stock level to 100 units and specify notification frequency as daily, then the system should save these settings and apply them to future alerts for the holiday season.

An Inventory Manager wants to receive alerts through multiple communication channels to ensure they do not miss critical notifications about inventory needs.

Given an Inventory Manager is setting up their alert preferences, when they select email and SMS as their preferred notification channels, then alerts should be sent to both channels whenever inventory thresholds are crossed.

After customizing alert settings, an Inventory Manager conducts a review of their settings to verify accuracy before the seasonal demand period begins.

Given an Inventory Manager reviews their alert configurations, when they check the settings for acceptable stock levels, notification frequency, and channels, then the displayed configurations should match the previously saved preferences.

An Inventory Manager wishes to modify alert settings mid-season based on changing market conditions and trends in sales data.

Given an Inventory Manager accesses the Customizable Alert Settings during the seasonal demand period, when they change the acceptable stock level and notification frequency, then the system should update the settings and reflect these changes immediately in the alert notifications.

An Inventory Manager is interested in receiving reports on past alerts generated to assess the effectiveness of their customized settings.

Given an Inventory Manager requests a report on past seasonal demand alerts, when they initiate the request, then the system should generate a report detailing the alerts sent, the thresholds met, and response actions taken during the specified time frame.

An Inventory Manager needs guidance on setting optimal alert thresholds based on historical data analysis.

Given an Inventory Manager accesses the Customizable Alert Settings, when they request recommendations for acceptable stock levels based on historical trends, then the system should provide data-driven suggestions to assist in setting effective thresholds.

Integration with Supply Chain Systems

User Story: As an Inventory Manager, I want seasonal demand alerts to integrate with supply chain systems so that I can manage inventory and supplier coordination effectively during peak seasons.
Description: This requirement involves integrating seasonal demand alerts with existing supply chain management systems to streamline inventory and logistics operations. This integration will ensure that Inventory Managers have a comprehensive view of not only inventory needs but also supply chain capabilities, allowing for better planning and response strategies. The system will provide insights into supplier lead times, current stock levels, and demand forecasts, enabling improved alignment between inventory levels and supply chain readiness. This holistic approach minimizes the risk of stockouts and ensures timely replenishment during peak demand seasons.
Acceptance Criteria: Notification of Seasonal Demand Alerts during planning meetings.

Given an upcoming peak season, When the Inventory Manager accesses the seasonal demand alerts dashboard, Then they should see notifications for anticipated demand spikes based on historical data.

Integration of seasonal demand alerts with supply chain management systems for real-time updates.

Given the integration is complete, When the Inventory Manager views the supply chain dashboard, Then they should see updated stock levels and supplier lead times reflecting upcoming seasonal demand alerts.

Response strategy formulation based on seasonal demand forecast metrics.

Given the seasonal demand alerts have been triggered, When the Inventory Manager reviews the demand forecasts, Then they should be able to create and save a response strategy for inventory replenishment within the system.

Testing the accuracy of seasonal demand alerts against real-time inventory levels.

Given the seasonal demand alerts are operational, When a demand forecast is generated, Then the actual stock levels should match within a 10% margin of error compared to the alerts provided.

User training on interpreting seasonal demand alerts and supply chain data.

Given training has been conducted, When Inventory Managers complete a training session, Then they should demonstrate proficiency in interpreting and acting on seasonal demand alerts with at least an 85% success rate in assessments.

Reporting capabilities on the effectiveness of the seasonal demand alerts feature.

Given the seasonal demand alerts are fully functional, When Inventory Managers generate a report, Then the report should include metrics on stockout rates and response times during peak seasons, showing at least a 20% improvement over the previous year.

Comprehensive Reporting Dashboard

User Story: As an Inventory Manager, I want a comprehensive reporting dashboard to visualize seasonal demand data so that I can make informed decisions about inventory adjustments.
Description: This requirement focuses on creating a comprehensive reporting dashboard that visualizes seasonal demand data, trends, and alert histories. This dashboard will provide Inventory Managers with actionable insights into past performance and future predictions, enabling them to make data-driven decisions regarding stock adjustments. The ability to visualize historical data alongside real-time alerts will empower users to spot trends, adjust strategies, and prepare inventory in a way that aligns with expected demand patterns. This reporting tool is critical in enhancing the decision-making capacity of retailers, leading to improved outcomes in inventory management and customer satisfaction.
Acceptance Criteria: Visualization of Seasonal Demand Patterns for Inventory Managers

Given an Inventory Manager accesses the Comprehensive Reporting Dashboard, when the seasonal demand data is displayed, then the dashboard should clearly visualize demand fluctuations over previous seasons using graphs and charts.

Real-Time Alerts Display in Reporting Dashboard

Given an Inventory Manager views the Comprehensive Reporting Dashboard, when seasonal demand alerts trigger, then the dashboard should prominently display the current alerts with timestamps and relevant product information.

Historical Data alongside Real-Time Alerts

Given an Inventory Manager is using the Comprehensive Reporting Dashboard, when the user requests to view a specific product's historical data, then the dashboard should allow for comparison between historical sales figures and current demand alerts for that product.

Actionable Insights Generation from Reporting Data

Given the Comprehensive Reporting Dashboard displays data, when the dashboard shows a significant drop in demand for a product, then it should provide actionable recommendations for stock adjustments such as reducing order quantities or promoting sales.

User Customization of Reporting Dashboards

Given an Inventory Manager accesses the Comprehensive Reporting Dashboard, when they choose to customize their dashboard layout, then the system should save their preferences and display their customized dashboard in future sessions.

Exporting Dashboard Reports for External Use

Given the Comprehensive Reporting Dashboard is displayed, when an Inventory Manager selects the export option, then the system should generate a downloadable report in multiple formats (PDF, CSV) that includes all visible data and insights from the dashboard.

Supplier Performance Tracker

The Supplier Performance Tracker integrates with supplier data to allow Inventory Managers to monitor delivery times and stock availability. This feature enhances decision-making regarding supplier relationships and can optimize reorder schedules, ultimately supporting timely inventory replenishment.

Requirements

Real-Time Supplier Analytics

User Story: As an Inventory Manager, I want to access real-time performance metrics of my suppliers, so that I can make informed decisions to optimize stock levels and improve supplier relationships.
Description: The Real-Time Supplier Analytics requirement enables Inventory Managers to access and analyze up-to-date performance metrics of suppliers, including delivery times, order accuracy, and stock availability. By integrating supplier data into the platform, this functionality allows users to visualize trends and identify potential issues, such as delays in delivery or insufficient stock levels. The insights gained from this analysis will support proactive decision-making and strengthen supplier relationships by providing a transparent view of performance. This capability is crucial for enhancing inventory management, ensuring timely replenishment, and ultimately improving customer satisfaction through reliable stock availability.
Acceptance Criteria: Accessing Supplier Performance Metrics in Real-Time

Given an Inventory Manager is logged into the BeaconLyte platform, when they navigate to the Supplier Performance Tracker section, then they should see up-to-date metrics displayed for each supplier, including delivery times, order accuracy, and stock availability, updated at least every 30 minutes.

Visualizing Trends in Supplier Delivery Times

Given the Inventory Manager is on the Supplier Performance Tracker dashboard, when they select a specific supplier for analysis, then they should be able to visualize trends in delivery times over the past 6 months through a line graph that shows monthly averages.

Identifying Stock Shortages in Real-Time

Given that the system is integrated with supplier inventory levels, when an Inventory Manager views the Supplier Performance Tracker, then they should receive alerts for any supplier that falls below a predefined stock level threshold in real time.

Generating Reports on Supplier Performance

Given an Inventory Manager wants to evaluate supplier performance quarterly, when they access the reporting feature in the Supplier Performance Tracker, then they should be able to generate a report that includes metrics such as delivery accuracy, stock availability, and a summary of issues encountered.

Evaluating Supplier Order Accuracy Trends

Given the Inventory Manager is viewing the Supplier Performance Tracker, when they filter metrics by order accuracy, then they should be able to see a trend analysis indicating whether order accuracy is improving or declining over the past year, represented as a percentage.

Routing Alerts for Immediate Stock Replenishment Needs

Given an Inventory Manager is monitoring the Supplier Performance Tracker, when any supplier’s stock availability dips below the critical threshold, then an automated alert should be triggered notifying the Inventory Manager for immediate action.

Customizing the Dashboard for Supplier Insights

Given the Inventory Manager is on the Supplier Performance Tracker dashboard, when they choose to customize their view, then they should be able to select which metrics to display prominently and rearrange them as desired, allowing for personalized insights.

Automated Reorder Alerts

User Story: As an Inventory Manager, I want to receive automated alerts when stock levels are low, so that I can reorder supplies in a timely manner and prevent stockouts.
Description: The Automated Reorder Alerts requirement provides Inventory Managers with configurable notifications for low stock levels based on supplier performance metrics. This feature uses historical data and real-time analytics to predict when stock levels are likely to fall below optimal thresholds, effectively prompting users to place orders with suppliers before a stockout occurs. By automating this process, it alleviates the burden of manual monitoring and ensures inventory is consistently replenished in line with demand, which ultimately enhances operational efficiency and mitigates the risk of lost sales due to stockouts.
Acceptance Criteria: Notification Configuration for Low Stock Levels

Given an Inventory Manager accesses the Automated Reorder Alerts settings, when they configure a low stock threshold for a specific product, then a notification should be triggered when the stock level falls below that threshold.

Real-time Stock Level Monitoring

Given the system is integrated with live supplier data, when stock levels change in real-time, then the system should update the inventory status and notify the Inventory Manager if the stock level falls below the configured threshold.

Historical Data Analysis for Reorder Patterns

Given historical sales data is available, when the automated system analyzes past stock levels and sales trends, then it should accurately predict future stock shortages and prompt reorder alerts based on these insights.

Notification Delivery Mechanism

Given the Inventory Manager has set up notifications for low stock, when the stock level triggers a reorder alert, then the notification should be sent via the configured method (email, SMS, in-app) within 5 minutes of detection.

Adjusting Low Stock Alerts

Given an Inventory Manager wants to modify the low stock threshold for a product, when they adjust the threshold in the settings, then the system should acknowledge the update and apply the new threshold to future alerts.

Supplier Performance Metrics Impact on Alerts

Given supplier performance data is integrated, when the system calculates reorder alerts, then it should factor in delivery performance metrics (average delivery time, stall triggers) to determine the optimal reorder point.

Logging Alert History for Audit Trail

Given automated alerts are generated, when an alert is triggered, then it should be logged in the system’s alert history with a timestamp and relevant product details for audit tracking.

Supplier Performance Dashboard

User Story: As an Inventory Manager, I want to customize a dashboard to view supplier performance metrics, so that I can easily analyze data and improve stockage strategies based on supplier reliability.
Description: The Supplier Performance Dashboard requirement involves creating a customizable interface for Inventory Managers to visualize supplier performance data. This dashboard will incorporate key performance indicators (KPIs) such as on-time delivery rates, order discrepancies, and inventory turnover rates. Users can modify the dashboard views to focus on specific suppliers or timeframes, making it easier to analyze trends and patterns. This feature not only provides a consolidated view of supplier data but also enables users to quickly assess areas for improvement and take actionable steps towards optimizing supplier performance, thereby enhancing overall inventory management processes.
Acceptance Criteria: Viewing Supplier Performance Metrics in Real Time

Given an Inventory Manager logs into the Supplier Performance Dashboard, when they select a specific supplier from the dropdown menu, then the dashboard displays real-time metrics including on-time delivery rates, order discrepancies, and inventory turnover rates for the selected supplier.

Customizing Dashboard Views for Specific Timeframes

Given an Inventory Manager is on the Supplier Performance Dashboard, when they apply a date range filter to the dashboard, then the system shows supplier performance metrics only for the selected timeframe, allowing analysis of trends over that period.

Setting Up Alerts for Performance Thresholds

Given an Inventory Manager is using the Supplier Performance Dashboard, when they set a performance threshold for on-time delivery rates at 90%, then the system should generate an alert if the selected supplier's delivery rate falls below this threshold at any time.

Exporting Supplier Data for Reporting

Given an Inventory Manager has accessed the Supplier Performance Dashboard, when they click the export button, then the system should generate a downloadable report containing all visible metrics in CSV format.

Assessing Historical Performance Data

Given an Inventory Manager selects a supplier on the Supplier Performance Dashboard, when they switch to the historical view option, then the dashboard displays a graph of key performance indicators (KPIs) over the past year, showing trends and patterns in supplier performance.

Identifying Areas for Improvement in Supplier Relationships

Given an Inventory Manager reviews the Supplier Performance Dashboard, when they notice a consistent pattern of order discrepancies for a specific supplier, then the system should prompt the user with actionable recommendations for addressing these issues.

Integrating Supplier Performance Data with Inventory Management Systems

Given that the Supplier Performance Dashboard is in use, when an Inventory Manager modifies supplier data in the dashboard, then the changes should automatically sync with the company's existing inventory management system without requiring manual updates.

Supplier Scorecard Generation

User Story: As an Inventory Manager, I want to generate performance scorecards for my suppliers, so that I can evaluate their performance and make informed decisions about future partnerships.
Description: The Supplier Scorecard Generation requirement allows users to automatically generate detailed performance reports for each supplier based on key metrics and analytics collected over time. This feature can compile data such as delivery timelines, quality of goods, and responsiveness to issues into a comprehensive scorecard format. This functionality is critical for providing a clear assessment of supplier relationships and performance, enabling Inventory Managers to make data-driven decisions on future orders and supplier negotiations. Furthermore, these scorecards can be shared with internal stakeholders for transparent communication regarding supplier performance.
Acceptance Criteria: Supplier Scorecard Generation for Q3 review

Given that I have access to the Supplier Performance Tracker and relevant supplier data, when I select a supplier and generate a scorecard for Q3, then the scorecard should display at least five key performance metrics such as delivery times, quality ratings, responsiveness, and overall score, in a clear and organized format.

Automated scorecard generation after data update

Given that supplier performance data has been updated in the system, when an Inventory Manager clicks on the ‘Generate Scorecard’ button, then the system must automatically compile the latest data into an updated scorecard without manual intervention.

Scorecard sharing with internal stakeholders

Given that I have generated a supplier scorecard, when I use the ‘Share’ function within the application, then the scorecard should be successfully emailed to selected internal stakeholders with a confirmation message displayed on the screen.

Customizable metrics in scorecard report

Given that I am generating a scorecard, when I select different performance metrics from a checklist, then the generated scorecard should reflect only the selected metrics in the final report.

User interface for scorecard generation

Given that I am on the Supplier Performance Tracker page, when I navigate to the scorecard generation section, then the interface should be user-friendly, allowing for easy selection of suppliers and metrics with clear call-to-action buttons and tooltips.

Performance benchmarking against previous periods

Given that I have generated a scorecard for a supplier, when I view the scorecard, then I should see a comparison of the current score against the scores from the previous two quarters, displayed visually through graphs or charts.

Historical Supplier Data Analysis

User Story: As an Inventory Manager, I want to analyze historical data on supplier performance, so that I can predict future issues and optimize my ordering strategy.
Description: The Historical Supplier Data Analysis requirement enables Inventory Managers to analyze past supplier data to identify trends and patterns that may affect future inventory decisions. By allowing users to access historical performance metrics, such as previous delivery timelines and stock availability, this feature supports predictive analytics within the platform. This analysis can highlight seasonal trends, recurring issues, and changes in supplier reliability over time, thereby empowering users to make informed strategic decisions regarding order placements and stock management. This requirement is crucial for enhancing the overall efficiency of inventory management, reducing costs, and improving supplier selection processes.
Acceptance Criteria: Supplier performance analysis during a quarterly review meeting.

Given that the Inventory Manager accesses the Historical Supplier Data Analysis feature, when they input the time frame for the past quarter, then the system should display a report summarizing supplier delivery times and stock availability along with visual trends of performance metrics over that period.

Identifying trends from historical supplier data for seasonal inventory planning.

Given that the Inventory Manager selects seasonal data for the past three years, when they request the trend analysis, then the system should generate a report highlighting any recurring trends in delivery times and stock availability, along with recommendations for adjusting reorder schedules based on those trends.

Evaluating supplier reliability before placing a new order.

Given that the Inventory Manager reviews the historical performance metrics of a potential supplier, when they analyze the delivery timelines and stock levels from the past six months, then the system should provide a clear rating of supplier reliability based on the collected data, allowing for informed decision-making.

Benchmarking supplier performance against industry standards.

Given that the Inventory Manager accesses industry benchmarks for supplier delivery times, when they compare their historical supplier data against these benchmarks, then the system should highlight any performance gaps and suggest potential actions to improve supplier relationships.

Generating alerts for underperforming suppliers based on historical data analysis.

Given that the Inventory Manager sets criteria for underperformance thresholds, when historical supplier data is analyzed, then the system should trigger real-time alerts for suppliers that fall beneath the set thresholds in delivery times or stock availability.

Creating a dashboard view of historical data for quick insights.

Given that the Inventory Manager requests a dashboard for quick insights on historical supplier performance, when the dashboard is generated, then it should display key metrics, charts, and a summary of trends that allows for immediate understanding and decision-making.

Integration with ERP Systems

User Story: As an Inventory Manager, I want the system to integrate with our existing ERP systems, so that I can ensure supplier data is accurate and up-to-date without manual input.
Description: The Integration with ERP Systems requirement allows the Supplier Performance Tracker feature to seamlessly connect with existing Enterprise Resource Planning (ERP) systems used by retailers. This integration enables automatic data exchange and synchronization, ensuring that supplier performance metrics are consistently updated across platforms. By eliminating manual data entry and reducing the risk of errors, it enhances the reliability of performance analytics and reporting. This requirement is critical for maintaining data integrity, improving operational workflows, and ensuring that Inventory Managers have access to accurate and timely supplier data for decision-making.
Acceptance Criteria: Integration with Existing ERP System to Automate Data Exchange

Given that the Supplier Performance Tracker is integrated with the ERP system, when supplier performance metrics are updated in the ERP, then the same metrics should automatically reflect in the BeaconLyte platform within 5 minutes without manual intervention.

Validation of Accurate Data Synchronization

Given that inventory managers access supplier performance data on BeaconLyte, when they compare metrics from the ERP and BeaconLyte platform, then the observed data should match with a tolerance of 1% variance to account for system latency.

Real-time Alerting for Supplier Performance Trends

Given that supplier performance is monitored in real-time, when there is a significant delay in delivery times beyond the agreed threshold, then an automated alert should notify the inventory manager within 10 minutes of the delay occurrence.

User Interface Workflows for Data Access

Given that the inventory manager is using the BeaconLyte platform, when they access the Supplier Performance Tracker section, then they should navigate through the interface with no more than 3 clicks to reach the desired supplier data.

Testing the Reliability of Data Updates Post Integration

Given that the integration has been completed, when a supplier updates their performance metrics in the ERP system, then the update should be received by BeaconLyte, and the data should be verified within a 1-hour window demonstrating at least 98% accuracy.

Reporting Supplier Performance Analytics

Given that the Supplier Performance Tracker is fully integrated, when an inventory manager generates a report on supplier performance, then the report should compile data correctly and reflect changes made within the last 24 hours, allowing for insights into trends.

User Authentication and Access Control for Data Security

Given that user roles are defined in the system, when an inventory manager logs into the BeaconLyte platform, then they should have access only to the supplier performance data relevant to their organization without visibility to unrelated data.

Multi-Location Inventory Monitor

The Multi-Location Inventory Monitor gives Inventory Managers the ability to track stock levels across various retail locations in real-time. This comprehensive feature enables better redistribution of stock where it's needed most, reducing excess inventory in less trafficked locations and maximizing overall efficiency.

Requirements

Real-Time Stock Level Updates

User Story: As an Inventory Manager, I want to receive real-time updates on stock levels across all locations so that I can make timely decisions to redistribute stock effectively and minimize excess inventory.
Description: This requirement facilitates real-time updates on stock levels across all retail locations. By incorporating APIs that continually monitor inventory levels, the Multi-Location Inventory Monitor ensures that inventory managers have access to the most current data. Benefits include improved decision-making regarding stock redistributions, reduced instances of stockouts, and minimized excess inventory. This feature will integrate seamlessly with existing inventory management systems, allowing for efficient data flow and analytics generation, ultimately enhancing overall operational efficiency.
Acceptance Criteria: Real-Time Update for Inventory Adjustment during Peak Hours

Given that the inventory levels at multiple locations are being tracked, when peak hours occur, then the stock levels should automatically refresh every 5 minutes to ensure inventory managers receive the latest data.

Integration with Existing Inventory Management Systems

Given that BeaconLyte is integrated with other inventory management systems, when data is updated in real-time, then the updated stock levels should reflect in both BeaconLyte and the legacy systems without delay.

Alert Notifications for Low Stock Levels

Given that stock levels are being monitored in real-time, when stock of any item falls below a predefined threshold, then an alert notification should be sent to the inventory manager in less than 1 minute.

Historical Data Comparison for Inventory Trends

Given that real-time data is available, when an inventory manager views the dashboard, then they should be able to compare current stock levels against historical data from the previous month to identify trends in stock movements.

User Access Control for Inventory Managers

Given that multiple users access the Multi-Location Inventory Monitor, when an inventory manager logs in, then they should only see stock level data for locations for which they have been granted access rights.

Performance Under High Data Load

Given multiple locations are monitored simultaneously, when the system processes updates during peak retail hours, then it should maintain a response time of less than 2 seconds for any dashboard request.

Visualization of Stock Redistribution Opportunities

Given real-time stock level updates, when the inventory manager accesses the dashboard, then they should be able to visualize suggested stock redistribution opportunities based on current demand patterns.

Customizable Alerts for Low Stock

User Story: As an Inventory Manager, I want to set up customizable alerts for low stock levels so that I can react quickly to potential stockouts and ensure optimal inventory levels across all stores.
Description: This requirement enables the creation of customizable alerts that notify inventory managers when stock levels fall below a preset threshold at any location. This feature allows managers to tailor alert settings according to their specific needs, improving responsiveness to stock shortages. Alerts can be communicated via email, SMS, or in-app notifications, ensuring that managers are informed promptly. This functionality integrates into the existing notification system of BeaconLyte and enhances user engagement with the platform by allowing proactive inventory management.
Acceptance Criteria: Alert when stock of a specific product falls below the predefined threshold across multiple retail locations.

Given that the inventory level of a product falls below the specified threshold, When the threshold is crossed, Then an alert must be triggered and sent to the designated inventory manager via email, SMS, and in-app notification with accurate product details and location.

Customization of alert settings by an inventory manager for different product categories.

Given that an inventory manager accesses the customizable alert settings, When they adjust the threshold levels and notification preferences for various product categories, Then the system must save these settings and apply them to future inventory assessments

Real-time notification system functionality upon triggering an alert for low stock.

Given that the low stock alert is triggered, When the alert is sent out, Then it should be received in real-time with no delays on all designated communication channels (email, SMS, and app) by the assigned inventory manager.

Responsiveness of the alert system under high load conditions with multiple alerts being triggered.

Given that multiple products across different retail locations fall below their respective stock thresholds, When alerts are simultaneously triggered, Then the system must be able to process and send all notifications within a response time of 5 seconds without loss of information.

User engagement tracked through notification interaction statistics post-implementation of the alert system.

Given that the customizable low stock alerts are implemented, When the inventory managers receive, view, and act upon the notifications, Then the platform should collect and display analytics on user engagement metrics such as open rates, response times, and action taken on alerts received within the dashboard.

Integration of the alert system with the existing notification infrastructure of BeaconLyte.

Given that the new customizable alerts feature is developed, When it is connected to the existing notification system, Then the alerts must function seamlessly without any disruption to current notification flows or systems, ensuring backward compatibility.

Effectiveness of alerts on reducing stock shortages and improving inventory turnover rates.

Given that the customizable alerts are actively being used, When evaluated over a period of 2 months, Then there should be a measurable reduction in stock shortages by at least 30% and an improvement in inventory turnover rates by at least 15% based on sales data from the analyzed period.

Centralized Dashboard for Overview

User Story: As an Inventory Manager, I want a centralized dashboard that displays inventory levels across all locations so that I can quickly assess stock status and make informed decisions efficiently.
Description: This requirement involves the development of a centralized dashboard that provides an overview of inventory levels across all locations. This dashboard will include visual representations, such as charts and graphs, allowing inventory managers to quickly assess stock status at a glance. The dashboard will display key performance indicators (KPIs) related to inventory management, including turnover rates and days of inventory on hand. This essential feature aims to improve usability and decision-making, enabling efficient inventory control and strategic planning.
Acceptance Criteria: Inventory Manager accessing the centralized dashboard to assess stock levels across multiple locations during peak shopping hours.

Given the Inventory Manager is logged into the system, when they navigate to the centralized dashboard, then they should see real-time stock levels displayed for all locations in graphical format with color-coded indicators reflecting stock status (e.g., low, sufficient, excess).

The Inventory Manager reviewing inventory KPIs on the dashboard to make decisions on stock redistribution between stores.

Given the Inventory Manager has accessed the centralized dashboard, when they view the KPIs such as turnover rates and days of inventory on hand, then these metrics should be automatically calculated and displayed based on the latest inventory data without the need for manual input.

Inventory Manager receiving real-time alerts for low stock levels while monitoring the dashboard.

Given the dashboard is actively displaying stock levels, when any location's stock falls below the defined threshold, then the Inventory Manager should receive an immediate notification or alert on the dashboard to take necessary action.

The Inventory Manager customizing the dashboard to focus on specific locations or products for tailored insights.

Given the Inventory Manager is on the centralized dashboard, when they select specific locations or products from the customization options, then the dashboard should update to reflect only the relevant data associated with those selections in real-time.

Multiple users accessing the centralized dashboard to monitor inventory simultaneously.

Given multiple Inventory Managers are accessing the dashboard at the same time, when each user views the dashboard, then they should all see real-time updates with no data lag or discrepancies in displayed inventory levels.

The Inventory Manager exporting the dashboard data for reporting purposes after reviewing stock levels and KPIs.

Given the Inventory Manager has completed their review, when they select the export option, then the dashboard data, including visual representations and KPI metrics, should be successfully exported in a predetermined format (e.g., CSV, PDF) without loss of information.

The system reflecting changes in inventory levels after stock redistribution actions have been taken.

Given the Inventory Manager has redistributed stock based on insights from the centralized dashboard, when they update the inventory records in the system, then the dashboard should reflect these changes in stock levels instantly.

Stock Redistribution Recommendations

User Story: As an Inventory Manager, I want to receive AI-driven recommendations for stock redistribution so that I can optimize inventory levels based on current sales trends and prevent stockouts in high-demand locations.
Description: This feature focuses on providing automated recommendations for stock redistribution based on real-time sales data and inventory levels. By utilizing AI algorithms, the Multi-Location Inventory Monitor can analyze patterns and suggest optimal movements of stock between locations. This capability enhances efficiency in inventory management by ensuring that products are available in the right places at the right times, ultimately improving customer satisfaction and reducing excess inventory at underperforming locations.
Acceptance Criteria: Multi-Location Inventory Monitor checks stock levels every hour.

Given the Multi-Location Inventory Monitor is operational, when stock levels are checked, then it should display real-time inventory data for each location without delay.

Inventory Manager receives stock redistribution recommendations.

Given that the inventory data is analyzed, when the system identifies stock discrepancies based on sales data, then it should generate recommendations for stock redistribution within 5 minutes.

Inventory Manager validates stock redistribution recommendations.

Given that the recommendations have been provided, when the Inventory Manager reviews the suggestions, then they should find at least 90% accuracy in product movement based on historical sales trends.

Stock redistribution actions are executed based on recommendations.

Given stock redistribution recommendations have been approved by the Inventory Manager, when actions are taken to move stock, then it should reflect in the inventory system within 10 minutes post-approval.

System alerts for low stock levels.

Given that stock levels drop below predefined thresholds at any location, when the system detects these levels, then it should send notifications to Inventory Managers immediately via email and dashboard alerts.

Performance of AI algorithms in predicting stock needs.

Given historical sales data, when the AI algorithms analyze the data, then they should predict stock needs with at least 85% accuracy in replenishment recommendations for the next 30 days.

Historical Data Analysis Tool

User Story: As an Inventory Manager, I want to analyze historical inventory data so that I can understand past trends and make better inventory decisions for future stock management.
Description: This requirement is aimed at developing a historical data analysis tool that provides insights into past inventory movements and sales trends across locations. Through this analytical capability, inventory managers can identify patterns, assess the effectiveness of past stock decisions, and make more informed predictions for future stock needs. This integration will enhance decision-making abilities and support strategic planning for inventory levels in relation to seasonal trends, promotional events, and changing consumer behavior.
Acceptance Criteria: Inventory Manager analyzes historical sales data for the past six months to predict inventory needs for the upcoming holiday season.

Given the Inventory Manager has access to the Historical Data Analysis Tool, when they filter data by location and select a date range of the past six months, then they should see visualization of sales trends and inventory movements for each location.

An Inventory Manager identifies underperforming stock levels in one location compared to the sales trends observed in another location.

Given the Inventory Manager has accessed the tool, when they compare inventory levels and historical sales data between two locations, then the tool should provide actionable insights and recommendations on stock redistribution.

The Inventory Manager provides a report on past inventory decisions and their outcomes during a quarterly review meeting.

Given the Inventory Manager uses the Historical Data Analysis Tool, when they generate a report summarizing past inventory trends and stock decisions, then the report should accurately reflect the data analyzed and include visual graphs to support their findings.

Inventory Managers want to assess the effectiveness of promotional events on inventory turnover across multiple locations.

Given the Inventory Manager has selected a promotional period in the Historical Data Analysis Tool, when reviewing the data for multiple locations, then they should see a clear comparison of inventory turnover rates before, during, and after the promotional events.

An Inventory Manager is monitoring inventory levels on a daily basis and receives alerts for any inconsistencies found in historical data.

Given the Inventory Manager has set alerts in the Historical Data Analysis Tool, when the tool detects discrepancies in inventory levels compared to historical data, then the Inventory Manager should receive an automatic notification alert detailing the issue and location.

The Inventory Manager uses the tool to prepare for an upcoming seasonal sale, analyzing trends from previous years.

Given the Inventory Manager accesses the Historical Data Analysis Tool, when they select previous seasonal sale periods, then the system should display relevant inventory data and sales performance metrics that aid in planning for the upcoming sale.

Custom Alert Thresholds

Custom Alert Thresholds enable Inventory Managers to personalize alert settings based on specific product characteristics or business needs. Users can define unique thresholds for different items, ensuring they receive tailored notifications that align with their operational strategies.

Requirements

Dynamic Threshold Configuration

User Story: As an Inventory Manager, I want to configure dynamic alert thresholds for different products so that I can receive timely notifications that are relevant to specific product characteristics and inventory levels.
Description: The Dynamic Threshold Configuration requirement allows users to create and modify alert thresholds for inventory items based on various product attributes such as sales trends, seasonality, and stock levels. This feature enhances operational efficiency by ensuring that inventory managers receive timely notifications that are relevant to their specific needs and business context. Users will have the ability to set thresholds that automatically adjust based on historical performance data, thereby reducing the risk of overstocking or stockouts, improving inventory accuracy, and optimizing the overall inventory management process.
Acceptance Criteria: User sets a custom alert threshold for a specific product based on sales trends and historical data.

Given the user is on the Dynamic Threshold Configuration page, when they select a product and input sales trends into the threshold settings, then the alert threshold should be saved successfully and displayed in the product’s alert settings.

User modifies an existing alert threshold for a seasonal product during the peak sales period.

Given the user has an existing threshold set for a seasonal product, when they adjust the threshold based on seasonal sales trends, then the system should update the threshold and trigger notifications for any stock level breaches as per the new threshold.

User receives an alert notification based on a dynamically adjusted threshold.

Given the user has set dynamic thresholds for inventory items, when the current stock level for an item falls below the dynamically set threshold, then the user should receive an immediate alert notification via their selected communication channel.

User attempts to set a threshold that conflicts with an existing system rule.

Given the user is on the threshold configuration page, when they enter a threshold value that conflicts with predefined stock rules, then the system should display an error message indicating the conflict and prevent the threshold from being saved.

User reviews the historical performance data of an item to inform their threshold settings.

Given the user clicks on the historical data analysis feature for a specific item, when they view the data trends, then they should see a clear graphical representation of sales trends, seasonality, and stock levels to inform their threshold setting decisions.

User wants to delete a custom threshold they previously set for a product.

Given the user is on the alert settings page, when they select a product with an existing custom threshold and choose to delete it, then the threshold should be removed from the system, and the user should receive a confirmation of the deletion.

User-Friendly Alert Management Interface

User Story: As an Inventory Manager, I want an intuitive interface for managing my alert settings so that I can easily configure and adjust my notifications without needing technical assistance.
Description: The User-Friendly Alert Management Interface requirement focuses on the design and implementation of an intuitive and easy-to-navigate interface for managing alert settings. Inventory Managers will be able to set, adjust, and review their custom alert thresholds through a streamlined dashboard that provides clear visual cues and guidance. This capability will improve user engagement and satisfaction by making it easy for users to tailor their alert preferences without requiring extensive training or support, ultimately leading to more effective inventory oversight and quicker response times to inventory changes.
Acceptance Criteria: Setting Custom Alert Thresholds for Inventory Levels

Given the Inventory Manager is on the User-Friendly Alert Management Interface, when they navigate to the custom threshold settings, then they should be able to set different alert levels for each product by entering numerical values for minimum and maximum inventory thresholds which are saved successfully.

Adjusting Existing Alert Thresholds

Given an Inventory Manager has previously set custom alert thresholds, when they return to the User-Friendly Alert Management Interface and adjust any existing thresholds, then the changes should be reflected immediately without errors, and confirmation should be shown on the interface.

Viewing Current Alert Thresholds

Given the Inventory Manager accesses the User-Friendly Alert Management Interface, when they select a product from the inventory list, then the current custom alert thresholds for that product should be displayed clearly without any confusion.

Receiving Notifications Based on Custom Alerts

Given custom alert thresholds are set for multiple products, when inventory levels fall below the defined minimum threshold, then the Inventory Manager should receive real-time notifications through the system as per the defined settings.

Error Handling in Alert Management

Given an Inventory Manager is using the User-Friendly Alert Management Interface, when they attempt to enter invalid values (such as negative numbers for thresholds), then the system should display an appropriate error message prompting them to correct the input before saving.

Gaining Insights Through Alert History

Given that alerts have been triggered based on threshold settings, when the Inventory Manager views the alert history section in the User-Friendly Alert Management Interface, then they should see a complete log of triggered alerts, including timestamps and affected products.

User Training and Support Accessibility

Given the User-Friendly Alert Management Interface is in use, when an Inventory Manager requires assistance, then they should have access to on-screen help resources or tooltips that provide guidance on setting and adjusting custom alert thresholds.

Historical Alert Performance Analytics

User Story: As an Inventory Manager, I want to analyze the performance of my alert settings over time so that I can refine my thresholds and improve my inventory management strategy.
Description: The Historical Alert Performance Analytics requirement involves providing users with insights into how effective their custom alert thresholds have been over time. Inventory Managers will have access to analytics that show the correlation between their alerts and inventory performance metrics, such as sales fluctuations and stock issues. This feature will enable users to refine their alert settings based on historical performance data, leading to more accurate inventory forecasting, better decision-making, and enhanced responsiveness to market changes.
Acceptance Criteria: Historical Alert Performance Review for Inventory Optimization

Given an active user with custom alert thresholds for products, when the user accesses the Historical Alert Performance Analytics dashboard, then they should see a visual representation of alert performance over the past six months, including correlation metrics for sales and stock issues.

Alert Performance Metrics Analysis Over Time

Given that an Inventory Manager has set specific custom alert thresholds, when the manager reviews historical analytics, then each alert should display its impact on inventory performance metrics, including accurate percentages indicating how often alerts prevented stockouts or overstock situations.

User Feedback and Alert Adjustment

Given that an Inventory Manager has analyzed the historical alert performance, when the user opts to adjust thresholds based on the provided analytics, then the system should allow seamless modification of thresholds with real-time updates to alert settings confirmed by the user.

Notifications of alert effectiveness

Given that an Inventory Manager has active alert thresholds, when viewing the historical alert performance, then the system must send notifications indicating which alerts were most effective in impacting inventory levels within a predefined period.

Customizable Reporting Options for Alert Performance

Given that the Inventory Manager is in the analytics interface, when the user selects specific timeframes and product categories, then the system should generate a report reflecting the historical performance metrics for those parameters, allowing for detailed analysis of alert effectiveness.

Interactive User Interface for Alert Analytics

Given an Inventory Manager using the analytics dashboard, when the manager interacts with data points on the dashboard, then each interaction should provide tooltips or additional information about the performance data of each custom alert threshold.

Export Functionality for Historical Analytics

Given that an Inventory Manager has reviewed the historical alert performance analytics, when the user selects the export option, then the data should be downloadable in multiple formats (CSV, PDF) for external reporting and analysis.

Multi-Channel Alert Notifications

User Story: As an Inventory Manager, I want to receive alert notifications through multiple channels so that I can respond quickly to inventory changes even when I'm away from my desk.
Description: The Multi-Channel Alert Notifications requirement ensures that users can receive alert notifications through various channels, such as email, SMS, and in-app notifications. This flexibility allows Inventory Managers to stay informed about critical inventory changes regardless of their location. By supporting multiple communication methods, this feature increases the likelihood that users will respond promptly to alerts, thus minimizing the potential impact of poor inventory decisions and improving the overall efficiency of inventory management.
Acceptance Criteria: Inventory Manager receives alerts on critical inventory changes while traveling to a vendor meeting.

Given the Inventory Manager has set custom alert thresholds for low stock on specific products, When the inventory level falls below the specified threshold, Then the Manager should receive an alert via email, SMS, and in-app notification simultaneously.

An Inventory Manager adjusts their alert preferences through their personal account settings before an important sales event.

Given the Inventory Manager is logged into their account, When they navigate to the alert settings and modify notification channels for specific products, Then the changes should be saved and reflected in all future alerts.

An Inventory Manager experiences a system outage and needs to ensure critical alerts are received through SMS.

Given the Inventory Manager has selected SMS as a preferred notification channel, When a critical inventory alert is triggered during the system outage, Then the Manager should receive the alert via SMS within 5 minutes.

An Inventory Manager needs to assess the effectiveness of custom alert thresholds after a month of usage.

Given the Inventory Manager has been using custom alert thresholds for 30 days, When they review the alert history, Then they should see a 90% response rate to alerts indicating how timely decisions were made based on notifications.

Multiple Inventory Managers are set to receive alerts for overlapping product categories for coordinated inventory management.

Given that multiple Inventory Managers have been assigned to the same product categories, When a low-stock alert is triggered, Then all assigned Managers should receive the alert through their selected notification channels at the same time.

An Inventory Manager wishes to pause alert notifications temporarily during stock audits.

Given the Inventory Manager is in the settings section of the application, When they select the option to pause notifications and specify a duration, Then no alerts should be sent to the Manager for the selected duration, and this status should revert automatically after the pause period ends.

Customizable Alert Categories

User Story: As an Inventory Manager, I want to categorize my inventory alerts to prioritize my response based on the urgency and relevance of the notifications I receive.
Description: The Customizable Alert Categories requirement allows users to categorize their alerts based on different operational priorities or specific product lines. This feature enables Inventory Managers to streamline their focus by organizing alerts into manageable categories, such as 'High Priority', 'Low Stock', or 'Sales Performance'. By being able to prioritize alerts, users can improve their response times and ensure that they are addressing the most critical inventory issues first, leading to enhanced decision-making and operational efficiency.
Acceptance Criteria: Inventory Manager defines custom alert categories for different product lines during the initial setup of the BeaconLyte dashboard.

Given an Inventory Manager is logged into BeaconLyte, When they navigate to the alert settings section, Then they should be able to create, edit, and delete custom alert categories for specific product lines.

Inventory Manager assigns specific alerts to the newly created categories for monitoring inventory performance.

Given an Inventory Manager has created custom alert categories, When they assign alerts to the categories, Then each alert should be successfully linked to the corresponding category as per the user’s selection.

Inventory Manager tests the notifications generated by the custom alert categories under different scenarios.

Given configured custom alert categories and corresponding alerts, When the inventory levels change to trigger an alert, Then the manager should receive notifications categorized correctly as per the defined alert categories.

Inventory Manager reviews the effectiveness of the categorized alerts in their daily operations.

Given Alert Categorization is live, When the Inventory Manager assesses the response times and decision-making processes post-implementation, Then they should report a measurable improvement in response times to critical alerts.

Inventory Manager seeks to modify existing alert categories based on changing operational needs.

Given an Inventory Manager is evaluating alert categories, When they decide to adjust existing categories or thresholds, Then the system should allow them to make modifications seamlessly without losing previous configurations.

Inventory Manager views the alerts dashboard to monitor real-time updates from categorized alerts.

Given a high-traffic inventory day, When the Inventory Manager accesses the alerts dashboard, Then they should see a clear, organized view of alerts based on the designated categories, enabling quick assessments of inventory conditions.

Inventory Manager collaborates with team members to improve alert definitions and categories based on feedback.

Given a team of Inventory Managers uses BeaconLyte, When they participate in a feedback review session, Then they should be able to suggest and implement changes to alert categories that enhance operational strategies collaboratively.

Inventory Turnover Visualizer

The Inventory Turnover Visualizer offers an interactive tool for assessing product turnover rates and how they relate to stock levels. By understanding turnover dynamics, Inventory Managers can make informed decisions on inventory strategies, enhancing overall operational efficiency.

Requirements

Real-time Inventory Analytics

User Story: As an Inventory Manager, I want to view real-time inventory analytics so that I can make timely and informed decisions regarding stock levels and turnover rates.
Description: The Real-time Inventory Analytics requirement will enable the Inventory Turnover Visualizer to provide up-to-the-minute data on product turnover rates versus current stock levels. This feature will leverage real-time data processing to deliver insights instantly, allowing Inventory Managers to react promptly to changing market conditions. By integrating with Cloud-based data services and utilizing machine learning algorithms, the system will highlight trends and anomalies, which will enhance decision-making processes. Ultimately, this requirement aims to offer users a clearer understanding of inventory performance, helping them to optimize stock levels and minimize overstock situations.
Acceptance Criteria: Real-time data updates for product turnover rates and stock levels.

Given that an Inventory Manager is viewing the Inventory Turnover Visualizer, when the stock levels or product turnover rates change, then the visualizer should update the displayed information within 5 seconds.

Integration with cloud-based data services for inventory data retrieval.

Given that the Inventory Turnover Visualizer is connected to cloud-based data services, when a request is made to retrieve current stock levels and turnover rates, then the data should be returned accurately and within 3 seconds, 95% of the time.

Highlighting trends and anomalies in inventory data.

Given that inventory data is analyzed, when the Inventory Turnover Visualizer identifies trends or anomalies, then the system should generate visual alerts on the dashboard to inform the Inventory Manager within 10 seconds of detection.

User accessibility and interface performance during peak usage.

Given that multiple Inventory Managers are accessing the Inventory Turnover Visualizer simultaneously, when all users attempt to retrieve data, then the system should maintain an average load time of under 2 seconds, ensuring all critical insights are accessible without degradation of performance.

User training and support materials for navigating the visualizer.

Given that a new user is introduced to the Inventory Turnover Visualizer, when they access the help documentation, then they should find comprehensive resources, including a step-by-step guide and video tutorials, to enable them to use the tool effectively within 5 minutes of access.

Accuracy of machine learning predictions for turnover rates.

Given that historical inventory data is available, when the machine learning algorithms run predictive analyses, then the system should accurately predict product turnover rates with a confidence level of 85% or higher based on past data.

Customization features for dashboard settings.

Given that an Inventory Manager is using the Inventory Turnover Visualizer, when they desire to customize their dashboard settings, then they should be able to adjust the layout, choose data display preferences, and save these settings within 2 minutes.

Customizable Dashboards

User Story: As a user, I want to customize my dashboard so that I can focus on the metrics that are relevant to my inventory management needs.
Description: This requirement involves the development of customizable dashboards that allow users to tailor the view of their inventory turnover data based on their preferences and strategic goals. Users will be able to select which metrics to display, arrange visual components like graphs and tables, and apply filters to focus on specific products or time frames. This functionality improves user engagement and satisfaction by enabling them to prioritize the information that matters most to them. The feature will employ a drag-and-drop interface for ease of use and will save user configurations to enhance future analysis.
Acceptance Criteria: Inventory Manager customizes their dashboard to prioritize product turnover metrics during a weekly inventory review.

Given the Inventory Manager is logged into the system, when they access the dashboard settings, then they should be able to select, arrange, and save their preferred metrics and visual components.

An Inventory Manager wants to apply filters to view inventory turnover for specific product categories over the last month.

Given the Inventory Manager has accessed the customizable dashboard, when they apply filters for a specific product category and a time frame of the last month, then the displayed metrics should update to reflect the selected criteria accurately.

End-user saves their customized dashboard view for future access after arranging various metrics and visual components.

Given that the user has arranged their dashboard settings, when they click 'Save Configuration', then their dashboard layout should be saved, and upon next login, the same configuration should be reflected.

A user tries to use the drag-and-drop feature to rearrange graphs on their customizable dashboard.

Given the user is viewing their customizable dashboard, when they drag a graph and drop it in a new position on the screen, then the graph should remain in the new position without reverting back upon refresh.

An Inventory Manager needs to add additional metrics to their existing dashboard.

Given that the user is on their customized dashboard, when they select additional metrics from the metric library and add them to the dashboard, then those metrics should be displayed alongside existing metrics without any layout issues.

A user attempts to return to the default dashboard view after making customizations.

Given that the user has made custom changes to their dashboard, when they select the 'Restore Default View' option, then the dashboard should revert to its original default settings without any retained customizations.

A user needs to access the dashboard from different devices without losing their configuration.

Given the user has saved their customized dashboard configuration, when they log into their account on a different device, then their saved dashboard should display the same metrics and layout as it did on the original device.

Predictive Turnover Forecasting

User Story: As an Inventory Manager, I want to have predictive insights on turnover rates so that I can preemptively adjust inventory levels according to future demand.
Description: The Predictive Turnover Forecasting requirement will utilize advanced algorithms to predict future inventory turnover rates based on historical sales data, market trends, and seasonality factors. This predictive capability will assist Inventory Managers in making proactive inventory decisions, ensuring that stock levels are optimized in advance of demand. By integrating machine learning models, the system will continuously learn from new data, improving its forecasts over time. The ultimate goal is to reduce stockouts and overstock scenarios, thereby increasing overall operational efficiency and profitability.
Acceptance Criteria: Inventory Manager reviews predictive turnover rates during a quarterly business review meeting to assess current stock levels and forecast future inventory needs.

Given historical sales data is entered into the system, when the Inventory Manager requests predictive turnover forecasts, then the system shall display accuracy rates of predictions over the last 12 months above 90%.

Inventory Manager sets alerts for predicted stockouts based on forecasting data prior to high-demand seasons (e.g., holidays, events).

Given that predictive models indicate a potential stockout, when a seasonal demand period is identified, then the system shall generate alerts at least 2 weeks in advance for any stock items predicted to run out.

Inventory Manager analyzes the correlation between marketing promotions and inventory turnover rates to optimize future campaigns.

Given the system has integrated marketing promotion data, when the Inventory Manager views the turnover visualizer, then the system shall display the impact of each promotion on turnover rates with clear visual differentiation for each campaign's effectiveness.

The inventory turnover rates are monitored through an automatic dashboard in real-time to enable quick decision-making.

Given that real-time analysis is enabled, when turnover rates fluctuate beyond predetermined thresholds, then the system shall update the dashboard and notify the Inventory Manager immediately.

Inventory Manager compares predictions with actual sales to measure the forecasting system's accuracy over time.

Given a time period has concluded (e.g., one month), when the Inventory Manager reviews the report, then the system shall show a comparison report with at least 80% of predictions falling within a 10% margin of actual sales.

Inventory Manager reviews machine learning model adjustments based on new sales data to enhance forecasting accuracy.

Given new sales data has been collected, when the Inventory Manager initiates a review, then the system shall display updated model performance metrics and adjustments made based on the latest data trends.

Alerts for Low Turnover Rates

User Story: As an Inventory Manager, I want to receive alerts for low turnover rates so that I can quickly address slow-moving inventory and adjust my strategies accordingly.
Description: This requirement will implement an alert system that notifies Inventory Managers when product turnover rates fall below acceptable thresholds. By setting configurable limits, users will get instant notifications via email or in-app messages, enabling them to take swift action on slow-moving inventory. This proactive monitoring will support effective inventory management and ultimately help in maximizing sales opportunities by reducing excess stock. The alerts will include contextual data to help managers assess potential actions or adjustments needed.
Acceptance Criteria: Notification triggers when a product's turnover rate falls below the configured threshold set by the Inventory Manager.

Given that I am an Inventory Manager with configured turnover thresholds, when the turnover rate of a product falls below this threshold, then I should receive an email notification within 5 minutes.

Contextual data is included in alert notifications for low turnover rates.

Given that I receive an alert notification, then the notification must include contextual data such as the product name, current turnover rate, and days since last sale.

Adjusting thresholds should update alert parameters in real-time.

Given that I am an Inventory Manager and I change the turnover threshold for a product, when I save the configuration, then the alert system should immediately apply the new threshold without delay.

Alert system should be resilient and not miss any alerts during peak operating hours.

Given that multiple products are falling below threshold limits during peak hours, when the alerts are triggered, then all relevant notifications should be delivered within 5 minutes without loss.

Users can customize notification settings to choose between email and in-app alerts.

Given that I am an Inventory Manager, when I configure my alert preferences, then I should have the option to receive notifications via email, in-app messages, or both.

The alert system provides a summary view of current products with low turnover rates.

Given that I access the alert dashboard, then I should see a summary list of products with current turnover rates below the configured thresholds, including options to act on those products.

Data Integration with Existing ERP Systems

User Story: As an Inventory Manager, I want to integrate the Inventory Turnover Visualizer with our existing ERP system so that I can streamline my data management and ensure accurate, up-to-date reporting.
Description: The Data Integration with Existing ERP Systems requirement will facilitate seamless integration of the Inventory Turnover Visualizer with popular ERP systems used by retailers. This functionality will enable automatic data synchronization, ensuring that inventory figures and sales data are always up-to-date within the visualizer. The integration will simplify user workflows by reducing manual data entry and errors, guaranteeing accurate reporting and analysis. Such integration will enhance user experience and trust in the insights provided by the visualizer.
Acceptance Criteria: Successful synchronization of inventory data from an ERP system to the Inventory Turnover Visualizer under normal operation conditions.

Given that the ERP is operational and connected, when the Inventory Turnover Visualizer is initiated, then the latest inventory data should be reflected accurately within 5 minutes.

Manual data entry update for stock levels is disabled to reduce errors and enhance accuracy.

Given that the data integration is effective, when a user attempts to manually update inventory figures, then an error message should inform them that manual updates are not permitted and suggest alternative actions.

Testing the automatic data synchronization during peak operational hours of the connected ERP system.

Given that the ERP system is processing high volumes of transactions, when inventory levels change, then those changes should be reflected in the Inventory Turnover Visualizer without any data loss or lag exceeding 2 minutes.

User verification of data accuracy after integration setup.

Given that the integration has been completed, when the Inventory Manager compares the visualizer data against the source data in the ERP system, then the figures should match with a tolerance of no more than 2% difference.

Handling data synchronization failures from the ERP system.

Given that the ERP is experiencing downtime, when the Inventory Turnover Visualizer attempts to sync data, then a notification should be generated for the user indicating the failure and suggesting retry options.

User access control for viewing synchronized data within different user roles.

Given that multiple users have different roles in accessing the Inventory Turnover Visualizer, when a user with read-only permissions logs into the system, then they should be able to view, but not edit, the synchronized inventory data.

User onboarding and training for utilizing data integration features.

Given that new users are onboarded to the Inventory Turnover Visualizer, when they complete the training module on data integration, then they should pass a competency assessment with a score of at least 80% before being granted access to the integration features.

Touchpoint Tracker

The Touchpoint Tracker captures and visualizes each customer interaction across various channels, allowing Retail Analysts to clearly see where customers engage with the brand. This feature highlights which touchpoints are most effective or underperforming, enabling users to refine strategies that enhance customer satisfaction and drive sales.

Requirements

Real-time Interaction Analytics

User Story: As a Retail Analyst, I want to see real-time analytics of customer interactions so that I can quickly identify trends and adjust marketing strategies accordingly.
Description: The requirement for Real-time Interaction Analytics involves developing functionality that captures and analyzes customer interactions across various touchpoints in real-time. This feature will provide Retail Analysts with up-to-the-minute insights on customer engagement, allowing them to identify which channels drive the most engagement and satisfaction. The implementation will involve integrating with existing analytics frameworks and ensuring that data is processed efficiently to deliver actionable insights quickly. The expected outcome is to empower users with the ability to respond promptly to trends and changes in customer behavior, ultimately enhancing the effectiveness of their marketing and operational strategies.
Acceptance Criteria: Real-time capture of customer interactions on the website during a live promotional event.

Given a promotional event is active, when a customer interacts with the website, then the interaction data should be captured and reflected on the analytics dashboard within 5 seconds.

Analysis of customer engagement across email campaigns in real-time.

Given an email campaign has been sent, when a user opens or clicks within the email, then the interaction should be recorded in the system and updated on the dashboard in less than 10 seconds.

Monitoring of social media interactions as they occur during a product launch.

Given a product launch is announced, when a customer engages with the brand's social media content, then the information should be logged in real-time and displayed on the analytics dashboard immediately.

Tracking customer engagement through mobile app notifications during peak shopping hours.

Given peak shopping hours occur, when a customer clicks on a mobile app notification, then the click event should be captured and displayed in the real-time analytics dashboard within 5 seconds.

Comparison of engagement metrics across different sales channels after a marketing campaign.

Given a marketing campaign runs for one week, when the campaign concludes, then the analytics should reflect comparative metrics of engagement across all channels within 12 hours.

Identifying underperforming touchpoints after implementing the Real-time Interaction Analytics.

Given a month passes post-implementation, when a Retail Analyst reviews the engagement dashboard, then they should be able to identify at least three touchpoints with engagement below the defined thresholds.

Touchpoint Performance Dashboard

User Story: As a Retail Analyst, I want a customizable dashboard that displays touchpoint performance metrics so that I can easily monitor engagement and effectiveness across channels.
Description: The Touchpoint Performance Dashboard requirement focuses on creating a comprehensive dashboard that visualizes performance metrics for each touchpoint. This dashboard will aggregate data collected from various customer interaction channels, providing a clear overview of which touchpoints are effective and which need improvement. It will include visual elements such as graphs and charts to illustrate performance trends over time. The implementation will require a user-friendly interface for Retail Analysts to customize their views and filter data according to their needs. This requirement is critical for enabling data-driven decision-making that can enhance customer satisfaction and increase revenue.
Acceptance Criteria: User views the Touchpoint Performance Dashboard to analyze customer interactions over the last quarter.

Given the user is logged into the BeaconLyte platform, when they select the Touchpoint Performance Dashboard, then they should see visualizations of performance metrics for all customer interaction channels for the last quarter.

Retail Analysts filter data in the Touchpoint Performance Dashboard by specific customer segments.

Given the user is on the Touchpoint Performance Dashboard, when they apply filters for specific customer segments, then the displayed metrics should update to show only the data relevant to those segments.

A Retail Analyst saves a customized view of the Touchpoint Performance Dashboard for future reference.

Given the user has customized their view on the Touchpoint Performance Dashboard, when they click the 'Save View' button, then their customization should be saved and accessible in the 'My Views' section.

The Touchpoint Performance Dashboard alerts the user to a significant drop in performance metrics for a key touchpoint.

Given the user is monitoring the Touchpoint Performance Dashboard, when there is a significant drop in performance metrics for any touchpoint, then the system should trigger a real-time alert to notify the user.

Users access help documentation for the Touchpoint Performance Dashboard.

Given the user is on the Touchpoint Performance Dashboard, when they click on the 'Help' icon, then they should be redirected to relevant help documentation specifically for the Touchpoint Tracker functionality.

Performance metrics are exported from the Touchpoint Performance Dashboard to a CSV file.

Given the user is on the Touchpoint Performance Dashboard, when they select the 'Export' option and choose CSV format, then a CSV file containing all visible performance metrics should be downloaded successfully.

The Touchpoint Performance Dashboard updates metrics in real-time as new customer data comes in.

Given the user is actively viewing the Touchpoint Performance Dashboard, when new customer interaction data is received, then the dashboard should refresh and present the most current metrics without requiring a manual refresh.

Automated Touchpoint Alerts

User Story: As a Retail Analyst, I want to receive automated alerts when touchpoint performance metrics change significantly so that I can promptly investigate and address any issues.
Description: The Automated Touchpoint Alerts requirement involves creating a system that generates alerts based on specific criteria related to customer interactions at touchpoints. This feature will notify Retail Analysts when performance metrics fall below or exceed a defined threshold, ensuring that they can take timely action. For instance, if a particular touchpoint sees a drop in engagement, an alert will prompt the analyst to investigate further. The implementation will require configuring the alert system to integrate seamlessly with the analytics platform and deliver notifications through preferred channels (e.g., email, SMS). This will enhance proactive management of customer engagement strategies.
Acceptance Criteria: Receiving Alerts for Underperforming Touchpoints

Given a predefined threshold for customer engagement, when engagement at any touchpoint falls below this threshold, then an automated alert should be generated and sent to designated Retail Analysts via email and SMS.

Receiving Alerts for Overperforming Touchpoints

Given a predefined threshold for customer engagement, when engagement at any touchpoint exceeds this threshold, then an automated alert should be generated and sent to designated Retail Analysts via email and SMS.

Configuring Alert Preferences for Retail Analysts

Given a Retail Analyst account, when the analyst configures their alert preferences for touchpoint metrics, then the system should save these preferences and apply them to the alert notifications sent post-configuration.

Real-Time Notification Delivery

Given an alert that has been triggered, when the notification is generated, then it must be delivered to the Retail Analyst's configured channels (email, SMS) within 5 minutes of the alert being triggered.

Testing Alert Thresholds

Given a set of defined thresholds, when the customer engagement metrics are tested against these thresholds, then the system should accurately generate alerts in all scenarios (below and above thresholds) as expected.

Logging Alert History for Analytics

Given that an alert has been generated, when alerts are triggered, then the system should log these alerts in the analytics dashboard for future reference and analysis by Retail Analysts.

Integration with Existing Systems

Given the existing analytics platform, when the automated touchpoint alert system is implemented, then it should seamlessly integrate without disruption to current functionalities and allow data sharing as required.

Historical Touchpoint Data Analysis

User Story: As a Retail Analyst, I want to analyze historical touchpoint data so that I can identify trends and make informed predictions for future strategies.
Description: The Historical Touchpoint Data Analysis requirement is aimed at developing functionalities that allow Retail Analysts to access and analyze historical data related to customer interactions across different touchpoints. This feature will enable users to identify long-term trends, seasonal impacts, and the effectiveness of previous campaigns. By storing and processing historical data, analysts can conduct in-depth analyses and generate reports that inform future strategies. The implementation will involve building databases to hold historical data securely and offering analytical tools that facilitate complex data querying and reporting. This requirement supports strategic planning and continuous improvement in marketing efforts.
Acceptance Criteria: Retail Analyst accesses historical touchpoint data from the dashboard to analyze customer interaction trends over the past year, focusing specifically on seasonal impact on sales.

Given the Retail Analyst is on the analysis dashboard, when they select the option to view historical touchpoint data from the last year and choose a specific season, then the dashboard should display detailed graphs and metrics that show customer interactions during that period, allowing for at least 5 data points per month.

A Retail Analyst generates a report on the effectiveness of previous campaigns utilizing historical touchpoint data to inform future strategies.

Given that the Retail Analyst inputs specific parameters for the campaign being analyzed, when they click on the generate report button, then the system should produce a comprehensive report within 60 seconds that includes metrics like engagement rate and sales lift, with the ability to export in PDF format.

The Retail Analyst wants to compare current touchpoint performance against historical data to identify areas needing improvement.

Given the Retail Analyst is viewing current touchpoint performance metrics, when they select the compare historical data option, then the system should overlay historical performance metrics for each touchpoint on the same visual graph, clearly indicating whether performance has improved or declined.

The system processes new historical data inputs for touchpoint interactions efficiently and securely.

Given that new historical touchpoint data is uploaded to the system, when the data upload is completed, then the system should confirm successful storage with a timestamp and ensure that the data can be accessed and queried within 5 minutes of the upload.

A Retail Analyst seeks to analyze customer interaction data through a customizable query interface.

Given the Retail Analyst opens the historical data query tool, when they build a query using at least three parameters (e.g., date range, touchpoint type, and campaign), then the system should return results that reflect the defined query, with response times not exceeding 10 seconds.

The Retail Analyst needs to visualize historical data trends for a specific touchpoint across multiple campaigns.

Given the Retail Analyst selects a touchpoint from the visualization tool, when they choose to display data for multiple campaigns, then the system should generate a line chart that accurately represents data trends over time for each selected campaign, allowing for at least 4 campaigns to be displayed simultaneously.

A Retail Analyst wants to set alerts based on specific thresholds of historical touchpoint performance indicators.

Given the Retail Analyst is in the alert settings interface, when they define a threshold for a key performance indicator (e.g., customer engagement rate) and save the alert, then the system should validate that the alert is set and notify the Analyst via email if the threshold is breached in subsequent data analyses.

Multichannel Integration Capability

User Story: As a Retail Analyst, I want the Touchpoint Tracker to integrate with multiple channels so that I can have a complete view of customer interactions in one place.
Description: The Multichannel Integration Capability requirement focuses on ensuring that the Touchpoint Tracker can seamlessly integrate with various customer interaction channels, including social media, email, online chat, and in-store interactions. This feature will allow for a holistic view of customer engagement by consolidating data from all touchpoints into one platform. The implementation will require working with APIs from each integration channel and ensuring data integrity and synchronization. This comprehensive integration is essential for providing Retail Analysts with an all-encompassing view of cross-channel performance and customer behavior, enabling them to refine their strategies accordingly.
Acceptance Criteria: Retail Analyst reviews customer interactions across social media platforms to identify engagement trends.

Given that the Touchpoint Tracker is integrated with social media channels, when a Retail Analyst accesses the dashboard, then all customer interactions from Facebook, Twitter, and Instagram should be displayed in real-time without data loss.

A Retail Analyst accesses the Touchpoint Tracker to consolidate data from email campaigns and online chat interactions.

Given that email and chat channels are integrated, when the Retail Analyst checks the interaction statistics for the last month, then the report should accurately reflect total engagement counts and response rates for both channels, showing less than a 5% discrepancy from actual values.

A Retail Analyst attempts to investigate in-store customer interactions using data from the Touchpoint Tracker.

Given that in-store interactions are integrated into the system, when the Retail Analyst selects the in-store channel, then all customer engagement metrics should be visually represented in the dashboard, with no errors in data presentation.

Retail Analyst seeks to identify the top-performing touchpoints across all channels in a single report.

Given that all channels are integrated, when the Retail Analyst generates a performance report, then the report should clearly rank touchpoints by engagement metrics, highlighting the top three and indicating areas needing improvement with a detailed breakdown of each.

A Retail Analyst experiences a system update that may affect channel integrations.

Given that a system update occurs, when the Retail Analyst logs back in, then they should receive a notification confirming the status of each channel integration, with a clear indication of any channels that may not be functioning properly and expected resolution times.

Retail Analyst reviews cross-channel data from the Touchpoint Tracker during a quarterly analysis meeting.

Given that the data is consolidated across all channels, when the Retail Analyst presents findings in a meeting, then the insights should reflect accurate and consistent data across all touchpoints, validated against historical performance metrics.

A Retail Analyst uses the Touchpoint Tracker to measure customer interaction before and after a marketing campaign.

Given that the marketing campaign has ended, when the Retail Analyst compares pre- and post-campaign interaction data, then the analysis should demonstrate a measurable increase in customer engagement across at least three identified touchpoints, with specific percentage increases recorded.

User Feedback Loop for Touchpoint Improvement

User Story: As a Retail Analyst, I want to gather customer feedback on touchpoints so that I can understand their experiences and improve engagement strategies accordingly.
Description: The User Feedback Loop for Touchpoint Improvement requirement aims to create a mechanism for gathering customer feedback regarding their experiences at different touchpoints. This feature will allow Retail Analysts to correlate feedback data with engagement metrics, providing insights into customer satisfaction and areas for improvement. The implementation will involve setting up feedback forms or surveys that customers can complete post-interaction, integrating this feedback into the existing analytics framework. This requirement is crucial for creating a responsive customer experience and refining strategies based on direct user input.
Acceptance Criteria: Customer Experience Enhancement through Feedback Collection

Given a customer has interacted with a touchpoint, when they receive a feedback survey post-interaction, then they should be able to submit their thoughts within 3 minutes without technical difficulties.

Feedback Data Integration into Analytics

Given feedback data is collected from customers, when this information is processed, then it should be accurately displayed on the Touchpoint Tracker dashboard alongside their corresponding engagement metrics.

Timeliness of Feedback Collection

Given a touchpoint interaction has occurred, when customers are surveyed for feedback, then 90% of feedback forms should be completed within 24 hours of the interaction.

Response Rate Optimization

Given feedback forms are made available, when measuring engagement, then the target response rate should be at least 30% of customers who interacted with a touchpoint.

Ability to Analyze Feedback for Improvement

Given collected feedback data, when Retail Analysts review it, then they should be able to identify actionable insights that suggest at least three specific touchpoints needing improvement.

User Satisfaction Measurement

Given feedback data related to customer interactions, when analyzing satisfaction levels, then 80% of customers should report a positive experience at the interaction touchpoints.

Survey Customization Options

Given the feedback mechanism, when Retail Analysts create surveys, then they should be able to customize at least five different parameters to tailor customer questions specific to touchpoint types.

Path Analyzer

The Path Analyzer illustrates the most common paths customers take from their first interaction to the final purchase. By understanding these customer journeys, Retail Analysts can identify key moments that influence buying decisions and optimize marketing efforts to guide customers more effectively toward conversion.

Requirements

Customer Journey Mapping

User Story: As a Retail Analyst, I want to visualize the typical customer journey so that I can identify key moments that influence buying decisions and enhance marketing efforts accordingly.
Description: The Customer Journey Mapping requirement involves creating a visual representation of the typical paths customers take from their first engagement to the final purchase. This feature will enable Retail Analysts to visualize and analyze customer interactions, allowing them to pinpoint critical touchpoints and opportunities for influencing customer behavior. By effectively mapping these journeys, businesses can better understand customer motivations and optimize their marketing strategies to facilitate smoother transitions through the buying process, thus increasing conversion rates and customer satisfaction.
Acceptance Criteria: Retail Analyst Visualizes Customer Journey After Implementing Path Analyzer

Given a Retail Analyst accesses the Path Analyzer dashboard, when they select a customer segment to analyze, then they should see a clear visual representation of the most common paths taken by customers from first engagement to purchase, with critical touchpoints highlighted.

Retail Analyst Identifies Key Influencing Touchpoints

Given the visualized customer journey, when the Retail Analyst examines the paths, then they should be able to pinpoint at least three key touchpoints that influence customer decisions, documented with corresponding data insights.

Retail Analyst Optimizes Marketing Strategy Based on Path Analyzer Insights

Given the identified key touchpoints, when the Retail Analyst formulates a marketing strategy, then the strategy must incorporate at least two actionable insights derived from the customer journey analysis.

Path Analyzer Provides Real-Time Data Updates

Given the Path Analyzer tool is operational, when new customer interaction data is logged, then the visual representation and identified paths should update in real-time to reflect the most current customer behavior.

Retail Analyst Generates a Report from Customer Journey Mapping

Given the Retail Analyst has completed their analysis, when they generate a report from the Path Analyzer, then the report should include visual maps, key touchpoints, and strategic recommendations based on the customer journeys analyzed.

Retail Analyst Shares Insights with Stakeholders

Given the Retail Analyst has gathered insights from the Path Analyzer, when they prepare a presentation for stakeholders, then the presentation should clearly illustrate customer paths and highlight key insights that can influence business decisions.

Path Effectiveness Metrics

User Story: As a Retail Analyst, I want to track and analyze effectiveness metrics for customer paths so that I can make data-driven decisions to improve sales conversions.
Description: The Path Effectiveness Metrics requirement entails developing a set of key performance indicators (KPIs) to quantitatively measure the effectiveness of various customer paths through the sales funnel. This will include metrics such as conversion rates at each stage, average time spent on the path, and dropout rates. By providing data-driven insights into the strengths and weaknesses of customer paths, Retail Analysts can make informed decisions about where to focus their optimization efforts to enhance sales outcomes.
Acceptance Criteria: Path Effectiveness Metrics Validation for Retail Analysts

Given a sample dataset of customer interactions, when the Path Effectiveness Metrics are generated, then the system should display the conversion rates, average time spent on each path, and dropout rates for each stage with a minimum accuracy of 95%.

Real-time Alerts for Path Effectiveness

Given the Path Analyzer is integrated into the retail analytics dashboard, when a significant drop in conversion rates is detected, then a real-time alert should be triggered and displayed on the dashboard within 5 minutes of detection.

Customizable KPI Dashboard Integration

Given a user access privilege to the BeaconLyte dashboard, when the Retail Analyst customizes the Path Effectiveness Metrics view, then the system should allow saving and retrieving these customized KPI settings without data loss or system errors.

Comparative Analysis of Customer Paths

Given multiple customer paths analyzed, when a report is generated for these paths, then the system should provide a comparative analysis report that includes conversion rates, average time, and dropout rates for each path side by side, allowing for easy identification of high and low-performing paths.

Historical Data Comparison for Path Effectiveness

Given the Path Effectiveness Metrics functionality, when historical data from the last quarter is compared with current data, then the system should generate insights that reflect percentage changes in conversion rates, time spent, and dropout rates between the two periods.

User Training and Documentation for Path Metrics

Given the new Path Effectiveness Metrics feature, when onboarding Retail Analysts, then comprehensive training materials and documentation should be provided detailing how to interpret and act on the KPIs effectively, with a satisfaction rate of at least 85% in feedback surveys post-training.

Interactive Path Exploration

User Story: As a Retail Analyst, I want to interactively explore customer journey data with various filters so that I can gain detailed insights into customer behaviors and adjust our strategies effectively.
Description: The Interactive Path Exploration feature allows users to interactively explore the customer journey data, manipulating filters such as time period, customer demographics, and specific channels used. This interactive visualization tool will enable Retail Analysts to dissect customer behavior patterns on a granular level, facilitating deeper insights into how different factors influence buying paths. This capability not only enhances the decision-making process but also supports targeted marketing strategies based on customer behavior insights.
Acceptance Criteria: User Interaction with Time Filters

Given a user on the Interactive Path Exploration dashboard, when they apply a time filter to view customer journeys from the last 30 days, then the displayed data should only show paths that include interactions occurring within that specific time frame.

Applying Demographic Filters

Given a Retail Analyst in the Interactive Path Exploration feature, when they select demographic filters (such as age, gender, location), then the customer journey paths presented must reflect only the selected demographic profile, excluding all others.

Filtering by Channel Usage

Given a user accessing the Interactive Path Exploration tool, when they filter customer journeys by specific channels (e.g., Mobile, Email, Web), then the paths displayed should accurately represent interactions that originated from the selected channel only.

Dynamic Data Updates in Real-Time

Given a user filtering customer journey data, when they adjust the filter parameters (time period, demographics, or channels), then the displayed data should update dynamically in real-time without requiring a page refresh.

Visual Representation of Customer Paths

Given the Interactive Path Exploration feature is displayed, when a user interacts with the visual representation of customer paths, then they should be able to hover over or click on elements to view detailed metrics such as conversion rates and drop-off points for each path.

Exporting Filtered Data

Given that a user has applied specific filters to analyze customer paths, when they choose to export the results, then the exported file should include only the filtered data in a readable format (CSV or Excel) and retain the structure of the visual representation.

Integration with Existing Systems

User Story: As a Retail Analyst, I want the Path Analyzer to integrate with our existing systems so that I can leverage holistic data insights from multiple sources for better decision-making.
Description: The Integration with Existing Systems requirement focuses on ensuring that the Path Analyzer can seamlessly connect with current retail systems, such as CRM and inventory management platforms. By facilitating real-time data sharing and synchronization, Retail Analysts can enhance the accuracy and relevance of the insights generated through the Path Analyzer. This integration is crucial for creating a comprehensive analytics environment where multiple data sources converge to inform strategic decisions.
Acceptance Criteria: Successful integration with existing CRM system for real-time data updates.

Given the CRM system is connected, when a customer profile is updated, then the Path Analyzer should reflect this change within 5 minutes.

Integration with inventory management systems to track product stock levels.

Given the inventory management system is in sync, when stock levels change, then the Path Analyzer should update the relevant customer recommendations in real time.

Path Analyzer's ability to capture and analyze customer interaction data from multiple channels.

Given that data from web, mobile app, and in-store interactions are collected, when analyzed, then the Path Analyzer should show a consolidated view of customer journeys across all platforms.

Ensuring the security and compliance of data integration from third-party platforms.

Given that data is being shared with third-party systems, when data is transferred, then all connections should adhere to GDPR and CCPA regulations.

Validation of real-time alerts for insights generated from integrated systems.

Given that the integration setup is complete, when significant patterns are detected, then the system should trigger real-time alerts to designated Retail Analysts within 10 seconds.

Providing an easy-to-navigate interface for managing integrations with different systems.

Given an analyst is in the integration management section, when they look for a specific system, then they should be able to find it easily within 3 clicks.

Testing system performance under load to ensure reliable data processing.

Given 1000 concurrent users are accessing the Path Analyzer, when they query data, then the system should respond within 2 seconds without errors.

Automated Reporting

User Story: As a Retail Analyst, I want to receive automated reports on customer path effectiveness so that I can quickly understand trends and make informed decisions without manual effort.
Description: The Automated Reporting requirement aims to implement a system that generates regular reports summarizing key findings from the Path Analyzer. These reports will provide Retail Analysts with essential insights into customer behavior trends and path effectiveness without the need for manual data processing. By automating reporting processes, the feature will enhance operational efficiency, ensuring timely dissemination of impactful insights to stakeholders for strategic planning.
Acceptance Criteria: Automated report generation for weekly customer behavior trends.

Given that a Retail Analyst accesses the reporting dashboard, when the scheduled time for report generation arrives, then the system should automatically generate and email the report summarizing customer behavior trends from the Path Analyzer for the past week.

Real-time alerts for significant changes in customer paths.

Given that the Path Analyzer has been updated with new data inputs, when a significant change in customer paths is detected, then the system should trigger an alert to the Retail Analysts via email and in-app notification within 5 minutes.

Cross-platform compatibility for automated reports.

Given that a Retail Analyst uses different devices (desktop, tablet, mobile), when they access the automated report, then the report should be properly formatted and fully accessible on all supported devices without loss of data integrity.

Customization options for report content and layout.

Given that a Retail Analyst wants to customize the report, when they select specific metrics and layout options, then the generated report should reflect their selections and allow for export in multiple formats (PDF, Excel, etc.).

Performance metrics for automated report generation.

Given that a Retail Analyst initiates the automated report generation, when the report processes, then it should complete within 2 minutes for standard reports and within 5 minutes for complex reports, confirmed by a success notification.

User feedback mechanism for report utilities.

Given that Retail Analysts have accessed the automated reports, when they complete a survey on usability and insights, then the system should record their feedback for future improvements to the reporting system.

Engagement Scoring

Engagement Scoring assigns value to different customer interactions based on their potential impact on sales and satisfaction. This feature helps Retail Analysts prioritize improvement initiatives by focusing on touchpoints that drive the most value, thus enhancing overall customer experience and loyalty.

Requirements

Dynamic Engagement Weighting

User Story: As a Retail Analyst, I want the Engagement Scoring to dynamically weight customer interactions so that I can prioritize which touchpoints to improve based on their actual impact on sales and satisfaction.
Description: Dynamic Engagement Weighting is a requirement that assigns varying weights to different types of customer interactions based on their relevance and impact potential on overall sales and satisfaction. This feature enhances the Engagement Scoring system by ensuring that high-value touchpoints, such as direct purchases or feedback interactions, are prioritized in analyses and reporting. It aims to provide Retail Analysts with refined insights that facilitate targeted strategies for improving customer experience. The system should continually update weights based on evolving customer behavior and market trends, ensuring accuracy in scoring and driving more effective engagement strategies.
Acceptance Criteria: Retail Analyst accesses the Engagement Scoring dashboard to review customer interaction data after the Dynamic Engagement Weighting feature has been implemented.

Given a Retail Analyst is logged into the BeaconLyte platform, When they select the Engagement Scoring dashboard, Then the dashboard displays interaction scores with differing weights assigned to each type based on their impact potential.

The system automatically updates engagement weights based on a new influx of customer data reflecting recent shopping trends.

Given the Dynamic Engagement Weighting system is live, When a new batch of customer interaction data is processed, Then the weights assigned to touchpoints reflect the latest customer behaviors and market trends with an update frequency of no less than once per hour.

A Retail Analyst generates a report analyzing the effects of Dynamic Engagement Weighting on customer satisfaction and sales.

Given the Retail Analyst has selected relevant metrics, When they generate an engagement report, Then the report shows improved customer scores on prioritized touchpoints, demonstrating a correlation between engagement weight changes and increased sales.

The system receives feedback from customers after implementing the Dynamic Engagement Weighting feature, and the weights are adjusted accordingly.

Given customer feedback has been input into the system, When the feedback is processed, Then the weights for lower-scoring touchpoints are adjusted to reflect their new relevance based on qualitative data.

An administrator tests the accuracy of the weights assigned to different customer interactions using a sample dataset.

Given the administrator accesses the weighting function, When they input a sample dataset, Then the system should return weights that accurately reflect the defined criteria for each type of customer interaction within a tolerance level of 5% accuracy.

Real-time Engagement Dashboard

User Story: As a Retail Analyst, I want a real-time dashboard that shows engagement scores and trends so that I can quickly assess customer interactions and respond proactively to improve customer loyalty.
Description: The Real-time Engagement Dashboard requirement involves creating an intuitive, user-friendly dashboard that displays current engagement scores, trends, and actionable insights at a glance. This dashboard will empower Retail Analysts to monitor customer interactions in real-time, enabling them to make immediate and informed decisions about engagement strategies. Key features will include customizable views, alerts for significant changes in scores, and integration with predictive analytics to forecast future engagement trends. The dashboard will be an essential tool for quick analysis, facilitating agile responses to customer behavior shifts and enhancing overall service delivery.
Acceptance Criteria: Engagement Analyst reviews the dashboard to assess current engagement scores during a weekly team meeting.

Given the Engagement Analyst is logged into the platform, when they access the Real-time Engagement Dashboard, then they should see current engagement scores display accurately in real-time without delay.

An Engagement Analyst sets up alerts for significant changes in customer interaction scores.

Given that the Engagement Analyst is on the dashboard, when they configure alerts for scores that drop below a specified threshold, then alerts should be sent via email within 5 minutes of the score change.

A Retail Analyst customizes the dashboard to focus on the most relevant engagement metrics for their retail environment.

Given the Retail Analyst is on the dashboard, when they select and save their preferred metrics layout, then the dashboard should display the customized view on their next login without data loss.

Engagement scores from the dashboard are compared against historic data to identify trends.

Given the Retail Analyst has access to historical engagement data, when they view the dashboard, then they should see a visual representation of current scores compared to the last 30 days, with the ability to drill down into specific time frames.

A Retail Analyst uses predictive analytics alongside real-time engagement scores to forecast future trends.

Given the Retail Analyst is viewing the dashboard, when they apply predictive models to the current engagement data, then the dashboard should generate forecasts with a confidence interval visible within 10 seconds.

Customer feedback mechanisms integrated into the dashboard are evaluated for effectiveness based on engagement scores.

Given the dashboard has integrated customer feedback, when the Retail Analyst reviews the feedback alongside engagement scores, then they should be able to correlate at least 80% of customer satisfaction comments with declines or improvements in scores.

Predictive Engagement Analysis

User Story: As a Retail Analyst, I want to use predictive analysis on engagement data to forecast future customer interactions so that I can implement proactive strategies for customer retention.
Description: The Predictive Engagement Analysis requirement focuses on developing an analytical model that leverages historical engagement data to predict future customer interaction trends. By utilizing machine learning algorithms, this feature will allow Retail Analysts to anticipate which customers are likely to engage or disengage based on past behaviors. The insights gained will enable targeted retention strategies, enhancing customer loyalty and potentially increasing sales through more effective engagement initiatives. This predictive capability integrates seamlessly with existing analytics tools in the BeaconLyte platform, providing a holistic view of customer engagement and its implications for sales.
Acceptance Criteria: Retail Analysts need to analyze customer engagement patterns over the past year to identify trends and anomalies that can inform future engagement strategies.

Given the historical engagement data for the last 12 months, when the predictive engagement analysis model is executed, then the output should accurately reflect trends that have statistically significant correlations with past engagement behaviors and sales outcomes.

After implementing the predictive engagement analysis, the Retail Analyst wants to validate that the model accurately predicts customer disengagement within the next quarter based on previous behaviors.

Given a sample of customers identified as at risk of disengagement, when the model is applied, then at least 80% of the identified customers should have shown similar disengagement patterns in the historical data used to train the model.

The Retail Analyst receives an alert from the predictive model indicating a high likelihood of customer disengagement among a specific customer segment.

Given that the predictive model has flagged a specific segment of customers as likely to disengage, when the Retail Analyst reviews the engagement scoring metrics for that segment, then the scores should indicate lower engagement levels compared to other segments and align with the predicted disengagement risk.

The Retail Analyst wants to ensure that the predictive engagement analysis tool integrates seamlessly with existing dashboard functionalities for real-time decision-making.

Given that the predictive engagement analysis tool has been developed, when the Retail Analyst accesses the dashboard, then the predictive insights should be incorporated into the existing analytics dashboard without any errors or disruptions in existing data visualization.

Prior to a campaign launch, the Retail Analyst conducts a trial run to ensure that the predictive engagement analysis can produce actionable recommendations based on the latest customer data.

Given the latest customer engagement data is available, when the Retail Analyst runs the predictive engagement analysis, then the output should yield at least three actionable recommendations for targeted engagement strategies to improve retention.

The Retail Analyst needs to present the predictive engagement analysis results to stakeholders, requiring that the results are easily understandable and actionable.

Given the results of the predictive engagement analysis, when the Retail Analyst prepares a presentation, then the findings should include clear visualizations and a summary of actionable insights that stakeholders can easily comprehend and implement.

The predictive engagement analysis needs to evaluate its performance over time to ensure continuous improvement and relevance to engagement strategies.

Given that the predictive engagement analysis model has been deployed, when the model is evaluated after rolling three months of customer interaction data, then it should demonstrate improvements in predictive accuracy of at least 10% compared to initial deployment outputs.

Segmentation-based Engagement Insights

User Story: As a Retail Analyst, I want to analyze engagement scores by customer segments so that I can tailor strategies for different groups and enhance their engagement with our brand.
Description: Segmentation-based Engagement Insights is a requirement that introduces the capability to analyze and score engagement data based on pre-defined customer segments. This feature will enable Retail Analysts to filter and assess engagement scores by various customer demographics or behaviors, providing insights into different segments' specific interactions. The outcome should identify high-value or at-risk segments, allowing tailored engagement strategies to enhance customer experience and satisfaction. This segmentation capability will facilitate targeted marketing efforts, thereby improving retention and conversion rates.
Acceptance Criteria: Retail Analyst uses the Segmentation-based Engagement Insights feature to filter engagement scores by customer demographics during a quarterly review meeting.

Given a data set with customer interactions segmented by demographics, when the Retail Analyst applies filters for age and location, then the engagement scores displayed must accurately reflect the selected segments with a response time of less than 2 seconds.

A Retail Analyst conducts a campaign based on insights from the Segmentation-based Engagement Insights feature to target high-value segments over a three-month period.

Given the identification of high-value segments through engagement scoring, when the Retail Analyst implements a targeted marketing campaign, then the campaign results should yield at least a 15% increase in conversions from the identified segments compared to the previous quarter.

Customer support uses the insights from the Segmentation-based Engagement Insights feature to respond to at-risk segments identified through their engagement scores.

Given the list of at-risk segments generated from the engagement scoring analysis, when customer support reaches out to those segments, then at least 50% of contacted customers should report a positive change in sentiment based on follow-up surveys within two weeks of contact.

A Retail Analyst reviews the performance of different customer segments using the Segmentation-based Engagement Insights feature to optimize inventory management.

Given the engagement scores and customer behavior data, when the Retail Analyst analyzes the information to adjust inventory levels, then the adjustments must reflect a 20% reduction in stockouts for high-engagement segments within the next quarter.

Using the Segmentation-based Engagement Insights feature, a Retail Analyst presents the insights to the marketing team to design a new engagement strategy for a poorly performing segment.

Given insights on a poorly performing segment, when the marketing team develops a new engagement strategy based on those insights, then the strategy must include at least 3 specific actions tailored to improve engagement and should be implemented within one month of the analysis.

A Retail Analyst checks the accuracy of scoring algorithms for engagement insights based on customer feedback.

Given customer engagement feedback data, when the Retail Analyst compares it against the engagement scores generated by the system, then the accuracy of the scoring algorithm must show a minimum correlation coefficient of 0.8 or higher for the correct identification of customer satisfaction levels.

The Segmentation-based Engagement Insights feature is tested for usability across various devices by Retail Analysts in real-time analytics sessions.

Given the feature is designed for cross-device usability, when Retail Analysts access the Segmentation-based Engagement Insights on different devices (desktop, tablet, mobile), then they must experience consistent performance and functionality across all devices with no more than 2 usability complaints reported during testing.

Automated Reporting for Engagement Metrics

User Story: As a Retail Analyst, I want automated reports on engagement metrics so that I can spend less time on data gathering and more time on implementing strategies to enhance customer engagement.
Description: The Automated Reporting for Engagement Metrics requirement involves creating a system that automatically generates comprehensive reports on engagement scores and trends at scheduled intervals. Retail Analysts will benefit from this feature as it eliminates the need for manual data compilation and analysis, allowing for more efficient use of time and resources. Reports will include insights into customer interactions, recommendations for improvements, and success metrics that align with business objectives. This automation of reporting processes will enhance the overall efficiency of the team and improve the strategic decision-making process.
Acceptance Criteria: Automated reporting system generates engagement metrics weekly for Retail Analysts to evaluate customer interactions and performance trends.

Given that the reporting system is active, when the scheduled time arrives, then a comprehensive report containing engagement scores and trends is automatically generated and stored in the designated location.

Retail Analysts receive automated reports that provide recommendations to improve engagement based on the latest customer interaction data.

Given the automated report has been generated, when the Retail Analysts access the report, then they can view actionable recommendations tailored to customer engagement metrics.

The automated reporting system integrates seamlessly with existing data systems to pull engagement data without manual intervention.

Given that the reporting system is connected to existing data sources, when it runs the automated report, then it retrieves the latest engagement data accurately and efficiently.

The automated report provides historical engagement data for comparison and trend analysis by Retail Analysts.

Given that a report is generated, when Retail Analysts open the report, then it displays historical engagement scores alongside current metrics for trend analysis.

The automated reporting system sends alerts if engagement scores fall below a predefined threshold during any reporting cycle.

Given the engagement scores are calculated, when scores fall below the threshold, then an alert is automatically triggered and sent to the relevant Retail Analysts.

The generated reports include visual representations (charts/graphs) of engagement metrics for easier interpretation by Retail Analysts.

Given that a report is generated, when accessed by Retail Analysts, then it includes visual elements that illustrate engagement metrics for quick understanding and analysis.

Retail Analysts can customize the parameters of the engagement report before automation for better alignment with business objectives.

Given that the Retail Analysts have access to the reporting system, when they customize the report parameters, then the automation respects these parameters in the next scheduled report generation.

Integration with CRM Systems

User Story: As a Retail Analyst, I want the Engagement Scoring feature integrated with our CRM system so that I can access a complete view of customer interactions and enhance our relationship management strategies.
Description: The Integration with CRM Systems requirement aims to connect the Engagement Scoring feature with existing Customer Relationship Management (CRM) systems used by retailers. This integration ensures that engagement data can flow seamlessly between systems, providing a unified view of customer interactions. Retail Analysts will be able to leverage comprehensive customer profiles that include engagement histories, enabling a more personalized approach to customer interactions and strategy formulation. The goal is to enhance the utility of both systems and ensure that engagement scores inform broader relationship management efforts.
Acceptance Criteria: Successful Data Synchronization Between Engagement Scoring and CRM Systems

Given the Engagement Scoring feature is integrated with the CRM system, when a customer interaction is logged in the CRM, then the engagement score must automatically update to reflect the new data within 5 minutes.

Real-Time Access to Comprehensive Customer Profiles

Given the integration with the CRM is implemented, when a Retail Analyst views customer profiles, then they should see updated engagement histories and scores displayed in real-time without requiring manual refresh.

Error Handling and Data Validation During the Integration Process

Given that data is being exchanged between the Engagement Scoring feature and the CRM system, when an error occurs in data synchronization, then the system must log the error and notify relevant stakeholders within 10 minutes.

User Permission Settings for Data Access

Given the system integration is complete, when a Retail Analyst logs into the platform, then they should only have access to customer engagement data for clients they are authorized to view based on established permission settings.

Performance Metrics for Engagement Data Exchange

Given the integration is live, when measuring performance, then the average data exchange time between the Engagement Scoring feature and CRM should not exceed 2 seconds during peak usage periods.

Seamless User Experience for Retail Analysts

Given that engagement data is integrated, when a Retail Analyst navigates between the Engagement Scoring and CRM systems, then the transition should be seamless with no need for re-authentication within the session.

Feedback Insights

Feedback Insights integrates customer feedback data into the mapping process, offering Retail Analysts a comprehensive view of customer sentiments related to specific touchpoints. This feature enables users to directly correlate feedback with customer journey stages, guiding targeted improvements that address customer concerns effectively.

Requirements

Real-time Sentiment Analysis

User Story: As a Retail Analyst, I want to see real-time sentiment analysis of customer feedback so that I can quickly address any emerging issues and enhance customer satisfaction.
Description: The Real-time Sentiment Analysis requirement focuses on implementing an AI-driven module that continuously analyzes customer feedback data as it is collected. This feature will provide Retail Analysts with the ability to view live sentiment scores and trends associated with specific retail touchpoints. By offering a dynamic dashboard that visualizes sentiment in real-time, this requirement enhances the ability to respond swiftly to customer issues and sentiments, fostering improved customer relationships and informed decision-making. The implementation of this module is essential for maintaining relevance in customer engagement strategies and improving overall service delivery.
Acceptance Criteria: Real-time sentiment scores are displayed on the dashboard as feedback is collected.

Given that customer feedback is being submitted, when a new feedback entry is processed, then the sentiment score should be updated on the dashboard within 5 seconds.

Retail Analysts can filter sentiment data by specific touchpoints in real-time.

Given that the Retail Analyst is on the sentiment analysis dashboard, when they select a specific touchpoint from the filter options, then the dashboard should refresh to display sentiment data exclusively for that touchpoint within 3 seconds.

The dynamic dashboard visually represents sentiment trends over time.

Given that feedback data has been collected over a defined period, when the Retail Analyst views the sentiment trends section of the dashboard, then the trends should correctly display changes in sentiment scores over time with appropriate visual indicators (e.g., graphs, color changes) for at least the last 30 days.

Retail Analysts receive real-time alerts for negative sentiment spikes.

Given that the sentiment analysis module is active, when a sentiment score for any touchpoint drops below a predefined threshold, then an alert notification should be sent to the Retail Analyst immediately, and the notification should be logged in the system.

Historical sentiment data can be compared with current trends.

Given that the Retail Analyst accesses the sentiment analysis dashboard, when they request a comparison between current and historical sentiment data for a selected touchpoint, then the dashboard should generate a report that clearly highlights differences in sentiment scores over the selected timeframe, with supporting visualizations.

Sentiment analysis results are accurate and reliable.

Given that customer feedback is analyzed, when the analysis is performed, then the sentiment scores should correctly reflect the sentiment expressed in at least 90% of the customer feedback based on a manual review of a sample set.

Retail Analysts can customize alerts based on sentiment thresholds.

Given that the Retail Analyst is configuring their dashboard settings, when they set a custom threshold for alert notifications, then the system should allow them to save these settings and generate alerts based on the new threshold criteria, effective immediately.

Feedback Correlation Mapping

User Story: As a Retail Analyst, I want to correlate customer feedback with the customer journey stages so that I can identify specific touchpoints for improvement.
Description: This requirement entails the development of a feature that correlates feedback data with different stages of the customer journey. By integrating advanced mapping tools, Retail Analysts will be able to pinpoint exactly how customer sentiments fluctuate throughout their interactions with the brand. This feature will serve as a pivotal resource for identifying critical touchpoints that require enhancements or changes. The mapping will help prioritize actions by showcasing where customer experiences are falling short, allowing for targeted interventions that can significantly improve retention and satisfaction rates.
Acceptance Criteria: Integration of customer feedback data into the feedback correlation mapping tool.

Given that a Retail Analyst has access to the Feedback Insights feature, when they import customer feedback data, then the tool should successfully map the feedback to specific customer journey stages, visually indicating the correlation on the dashboard.

Real-time updates of feedback correlations for instant analytics.

Given that customer feedback is received, when the feedback is integrated into the system, then it should automatically reflect updates on the correlation mapping within 5 minutes without manual intervention.

Visualization of customer sentiment trends over various touchpoints.

Given that a Retail Analyst is using the correlation mapping feature, when they select a specific touchpoint, then the system should display a clear visualization of customer sentiment trends related to that touchpoint over the last three months.

Comparison of feedback sentiments against retention rates.

Given that feedback sentiment data is available, when a Retail Analyst generates a report, then they should be able to view a correlation between feedback sentiments and customer retention rates, highlighting critical touchpoints that need attention.

User accessibility and ease of use of the feedback correlation mapping tool.

Given that a Retail Analyst logs into the platform, when they navigate to the feedback correlation mapping feature, then they should find the user interface intuitive and easy to navigate, with help documentation readily accessible.

Performance testing under heavy data load.

Given that significant volumes of customer feedback are integrated, when the feedback correlation mapping tool is in use, then it should maintain a response time of less than 2 seconds for data retrieval and visualization.

Custom Feedback Reports

User Story: As a Retail Analyst, I want to create custom feedback reports so that I can analyze specific aspects of customer feedback in detail and inform our strategy effectively.
Description: The Custom Feedback Reports requirement involves creating customizable reporting tools that allow Retail Analysts to generate detailed reports based on specific parameters of customer feedback. This functionality will enable users to tailor the reports to focus on relevant metrics, trends, or specific timeframes, thereby providing deeper insights into customer behavior and sentiments. Such tailored reports will empower decision-makers by equipping them with actionable insights to drive strategic improvements and marketing efforts, ensuring that the overall service remains agile and responsive to customer needs.
Acceptance Criteria: Retail Analyst generates a custom feedback report to evaluate customer satisfaction trends over the past quarter.

Given the Retail Analyst has access to the Custom Feedback Reports tool, when they select specific parameters including date range, feedback type, and metrics, then a detailed report is generated displaying the requested information correctly.

Retail Analyst customizes a feedback report to include only negative feedback scores from last month for specific product categories.

Given the Retail Analyst selects the negative feedback option and specific product categories, when the report is generated, then it accurately reflects only the negative feedback data for the selected categories and period.

Retail Analyst tests the export functionality of the customized feedback report into multiple formats.

Given the Retail Analyst has generated a custom feedback report, when they choose to export the report, then they should be able to export it successfully in PDF, Excel, and CSV formats without any data loss or error messages.

Retail Analyst accesses historical feedback data to compare it with current data in the generated report.

Given that the Retail Analyst has set parameters for historical data comparison in the Custom Feedback Reports tool, when the report is generated, then the report must include clear visual comparisons (charts/graphs) showing the differences in customer feedback over designated timeframes.

Retail Analyst shares a custom feedback report with team members for collaborative analysis.

Given that the Retail Analyst has generated a report and clicks the share option, when they enter the email addresses of the team members, then an email is sent with a link to the report that is accessible and functional for all listed recipients.

Segmentation Dashboard

The Segmentation Dashboard allows Retail Analysts to categorize customer journeys based on various demographics, behaviors, or preferences. By analyzing segmented data, users can tailor marketing strategies to distinct groups, ensuring more personalized communication and improved customer engagement.

Requirements

User Segmentation Criteria

User Story: As a Retail Analyst, I want to define custom segmentation criteria so that I can analyze and target distinct customer groups more effectively.
Description: The User Segmentation Criteria requirement involves the ability to define and customize various filters based on demographics, behavior, and preferences. Retail Analysts should be able to create distinct segments through intuitive interfaces, ensuring that the segmentation process is user-friendly and aligns with diverse marketing strategies. This functionality is crucial as it forms the backbone of the Segmentation Dashboard's analysis capabilities, allowing for personalized customer engagement and more effective marketing communications.
Acceptance Criteria: Retail Analysts are using the Segmentation Dashboard to categorize customer journeys during a quarterly marketing planning session. They need to create segments based on factors such as age, purchase history, and engagement level to tailor their campaigns for the upcoming holiday season.

Given that the Retail Analyst is on the Segmentation Dashboard, when they apply filters for demographics, behaviors, and preferences, then they should be able to create distinct customer segments that reflect the applied criteria and display relevant segment statistics.

A Retail Analyst wants to modify an existing customer segment by adding a new criterion for email engagement levels. The goal is to refine the targeting strategy for a specific promotional campaign.

Given that an existing customer segment is selected, when the Retail Analyst adds a new filter for email engagement levels and saves the changes, then the updated segment should reflect the new criteria in real-time and remain accessible for further analysis.

In preparation for a product launch, a Retail Analyst needs to visualize customer segments to present to the marketing team. They want to ensure that the dashboard accurately displays the segment breakdowns graphically to facilitate discussions.

Given that the Retail Analyst has created multiple customer segments, when they view the Segmentation Dashboard, then each segment should be visually represented using clear, distinct graphs or charts that show size, demographics, and engagement metrics at a glance.

Retail Analysts are using the Segmentation Dashboard to analyze customer behavior trends over the past year. They need to ensure that the created segments can be tracked and revised based on evolving customer preferences.

Given that the Retail Analyst has access to historical data, when they generate a report for the existing customer segments, then the report should accurately reflect the most updated segment characteristics and allow for adjustments based on new data insights.

A Retail Analyst aims to export the segmented customer data from the Segmentation Dashboard for use in an external marketing tool. They must ensure that the export includes all relevant details as per the marketing strategy requirements.

Given that the analyst has finalized their customer segments, when they initiate an export process, then the exported dataset should include all selected segment details such as demographics, behaviors, and preferences in a compatible file format for external use.

During a team meeting, a Retail Analyst needs to demonstrate how custom filters can enhance segmentation for the marketing department's campaign effectiveness review.

Given that the Retail Analyst has set up multiple filters on the Segmentation Dashboard, when they present to the team, then they should be able to engage the audience with live examples showing the impact of each filter on segment creation and performance.

Dynamic Dashboard Filters

User Story: As a Retail Analyst, I want to apply dynamic filters on the dashboard so that I can easily adjust views and analyze different customer segments in real-time.
Description: The Dynamic Dashboard Filters requirement allows users to apply real-time filters to the Segmentation Dashboard. This feature enables Retail Analysts to interactively modify their view of customer segments based on selected criteria, providing immediate insights and flexibility in analyzing different segments. The benefit is that it enhances user engagement with the dashboard, allowing for rapid hypothesis testing and decision-making based on live data.
Acceptance Criteria: Retail Analyst wishes to filter the Segmentation Dashboard to view only customers aged 18-24 who have made a purchase in the last month.

Given the Segmentation Dashboard is open, When the Retail Analyst selects the age group filter for '18-24' and the purchase history filter for 'Last Month', Then the dashboard should update to show only the segments that meet these criteria.

A Retail Analyst wants to apply multiple filters simultaneously, for example, filtering by age group and location to analyze purchase behavior across different regions.

Given multiple filters are available on the Segmentation Dashboard, When the Retail Analyst selects both the age group filter for '25-34' and the location filter for 'Northeast', Then the dashboard should comprehensively update to reflect data for customers who are both in the '25-34' age range and located in the 'Northeast'.

Analysis goal is to validate that cleared filters return to the original dashboard data view.

Given the Segmentation Dashboard is filtered by specific criteria, When the Retail Analyst clicks the 'Clear Filters' button, Then the dashboard should revert to display all customer segments without any filters applied.

Retail Analyst needs to verify that the dashboard updates in real-time when filters are applied, ensuring immediate data visualization.

Given the Segmentation Dashboard is being used, When the Retail Analyst applies a new filter, Then the dashboard must update to reflect the selected filter results within five seconds.

A Retail Analyst wishes to save and name a specific filter configuration for future use.

Given the Segmentation Dashboard displays filtered criteria, When the Retail Analyst opts to save the current filter settings and enters a unique name, Then the system should store this configuration and allow the Analyst to easily access it later.

Retail Analyst aims to understand whether the dashboard appropriately handles invalid filter entries (e.g., selecting an unsupported demographic).

Given the Segmentation Dashboard is operational, When the Retail Analyst enters an invalid filter criterion and submits it, Then an error message should be displayed indicating the filter is not supported, and the dashboard should remain unchanged.

Segment Comparison Tool

User Story: As a Retail Analyst, I want to compare multiple customer segments so that I can identify performance differences and adjust marketing strategies accordingly.
Description: The Segment Comparison Tool requirement facilitates the comparison of multiple customer segments side-by-side within the Segmentation Dashboard. This capability will allow Retail Analysts to assess performance metrics for various segments, identifying strengths and weaknesses and informing strategy adjustments. The tool is vital for identifying trends among different segments, leading to data-driven decision-making and optimized marketing efforts.
Acceptance Criteria: Retail Analysts need to compare customer segments based on their purchasing behavior over the last quarter to tailor marketing strategies accordingly.

Given the Segment Comparison Tool is open, When the Retail Analyst selects two or more customer segments for comparison, Then the system should display a side-by-side view of the selected segments' performance metrics (e.g., sales volume, average transaction value).

During a marketing strategy meeting, the Retail Analyst uses the Segment Comparison Tool to demonstrate the differences in engagement between high-value customers and regular customers.

Given the Analyst has selected high-value customers and regular customers, When the comparison is generated, Then the dashboard should highlight key differences in engagement metrics such as email open rates and promotional response rates.

A Retail Analyst wants to identify underperforming segments that need additional marketing support.

Given the Segment Comparison Tool is active, When the Analyst applies filters to view segments based on performance, Then the system should display segments that fall below a defined performance threshold clearly marked for quick identification.

The Retail Analyst needs to export the findings from the Segment Comparison Tool for reporting purposes after comparing different segments.

Given that the comparison results are displayed, When the Analyst selects the export option, Then the tool should allow exporting the comparison results in CSV or PDF format with all relevant metrics included.

The Segment Comparison Tool should allow Retail Analysts to visualize trends over time for each selected segment during quarterly reviews.

Given the segments are selected for comparison over multiple time periods, When the Analyst selects the time frame, Then the trends should display in a visual format (e.g., line graph or bar chart) to facilitate analysis.

When the Retail Analyst uses the Segment Comparison Tool, they expect user-friendly navigation to switch between different segments effortlessly.

Given the Analyst is viewing the Segment Comparison, When they click on a different segment in the navigation pane, Then the system should update the comparison details without delay and retain the selected performance metrics format.

Exportable Reports Feature

User Story: As a Retail Analyst, I want to export detailed reports from the dashboard so that I can share insights with my team and stakeholders effectively.
Description: The Exportable Reports Feature requirement allows users to generate and export detailed reports based on segmented data from the Segmentation Dashboard. Analysts should be able to create comprehensive PDFs or Excel files that document insights, trends, and comparisons, which can be shared with stakeholders for streamlined decision-making processes. This feature enhances collaboration and communication by allowing analysts to present their findings clearly and efficiently.
Acceptance Criteria: As a Retail Analyst, I want to generate an exportable report from the Segmentation Dashboard after analyzing customer data, so that I can share insights with stakeholders in a comprehensive format.

Given that I have access to the Segmentation Dashboard, when I select segmented data and choose to export the report, then I should receive a fully formatted PDF or Excel file containing all relevant insights, trends, and comparisons, ready for distribution.

As a Retail Analyst, I need to customize the content of the exportable report to include specific metrics and visualizations that are most relevant to my stakeholders.

Given that I am on the Exportable Reports feature, when I customize the report settings to select specific metrics and visual elements, then the generated export should accurately reflect these customizations without any data loss or formatting errors.

As a Retail Analyst, I want to ensure that the generated reports can be exported in both PDF and Excel formats, offering flexibility for different stakeholder needs.

Given that I select the export function for the report, when I choose either PDF or Excel format, then the system should allow for successful export in the selected format without errors, and the exported document should maintain all formatting and data integrity.

As a Retail Analyst, after generating the exportable report, I want to review the report for any discrepancies or errors before sharing it with my team.

Given that I have generated a report and am viewing it in the preview mode, when I compare the report with the original segmented data on the dashboard, then there should be no discrepancies in the insights displayed and all data should match accurately with the source.

As a Retail Analyst, I want to easily access and export previous reports so that I can track changes over time and analyze trends effectively.

Given that I am in the Exportable Reports section, when I navigate to the archive of previous reports, then I should be able to select and export any report from the archive without any data loss or access issues.

As a Retail Analyst, I need to ensure that my exported reports adhere to data privacy regulations and do not include sensitive customer information.

Given that I am exporting a report from the Segmentation Dashboard, when I generate the report, then it should exclude any sensitive data elements in accordance with data privacy policies, and this compliance should be verifiable via a compliance check mechanism.

AI-Powered Insight Generation

User Story: As a Retail Analyst, I want the dashboard to generate AI-driven insights so that I can quickly understand trends and make data-backed decisions without extensive manual analysis.
Description: The AI-Powered Insight Generation requirement enables the dashboard to automatically suggest insights based on analyzed customer segments and their behaviors. Using machine learning algorithms, the system should identify patterns and provide actionable recommendations for marketing strategies. This functionality enhances the product's value by leveraging AI to deliver deeper insights and reduce the manual analysis workload for Retail Analysts.
Acceptance Criteria: Retail Analyst uses the Segmentation Dashboard to view customer segments based on demographics such as age, location, and spending behavior.

Given the Segmentation Dashboard is open, when the Retail Analyst selects demographic filters, then the dashboard should accurately reflect the corresponding customer segments within 5 seconds.

The Dashboard provides AI-generated insights after a Retail Analyst inputs customer segment data for analysis.

Given the user inputs a customer segment for analysis, when the analysis is complete, then the system should display at least two AI-generated actionable insights relevant to the segment within 10 seconds.

A Retail Analyst reviews marketing strategy recommendations based on AI-generated insights from segmented customer data.

Given the AI has processed the customer data, when the Retail Analyst navigates to the recommendation section, then the system should present recommendations prioritized by expected impact and supported by relevant data visualization.

The system dynamically updates insights based on real-time data changes from customer behaviors observed in the Segmentation Dashboard.

Given that customer behavior data changes in real-time, when the change is detected, then the AI should refresh the insights within 5 minutes, ensuring the recommendations are based on the latest data.

A Retail Analyst exports the AI-generated insights and recommendations for presentation to the marketing team.

Given the Retail Analyst selects the export option, when they confirm the format (PDF/Excel), then the system should generate a downloadable document containing all insights and recommendations within 3 minutes.

The system logs and tracks interactions for AI-generated insights usage by the Retail Analysts for performance evaluation.

Given a Retail Analyst interacts with AI-generated insights, when the interaction occurs, then the system should record the event with a timestamp and user ID for future analysis of usage patterns.

The Segmentation Dashboard includes a help feature that guides users on how to utilize AI-powered insights effectively.

Given the Retail Analyst accesses the help feature, when they request assistance on AI insights, then the system should display a comprehensive guide outlining step-by-step instructions with examples.

Conversion Funnel Visualizer

The Conversion Funnel Visualizer depicts the customer journey as a funnel, highlighting stages where potential buyers drop off. This visual representation aids Retail Analysts in pinpointing weaknesses in the buying process, allowing for targeted interventions that enhance conversion rates and overall sales performance.

Requirements

Dynamic Funnel Analysis

User Story: As a Retail Analyst, I want to dynamically analyze the conversion funnel so that I can identify weaknesses and improve our sales performance based on real-time data.
Description: The Dynamic Funnel Analysis requirement involves the real-time evaluation of user behavior throughout the conversion funnel. This includes tracking metrics at each stage of the funnel, such as traffic sources, user interactions, and drop-off points. By dynamically analyzing this data, the system will provide actionable insights that can help pinpoint specific weaknesses in the current buying process. This feature benefits retailers by allowing them to make data-driven decisions to enhance user experience and conversion rates. Its integration into the BeaconLyte platform will allow retail analysts to quickly identify problems and experiment with interventions to improve the sales process.
Acceptance Criteria: Real-time user behavior tracking within the conversion funnel.

Given I am a Retail Analyst, When I access the Dynamic Funnel Analysis feature, Then I should see real-time metrics for traffic sources, user interactions, and drop-off points at each stage of the funnel displayed on my dashboard.

Actionable insights provision from dynamic analysis of the conversion funnel data.

Given the system has tracked user interactions, When the analysis is performed, Then the system should provide a list of actionable insights identifying specific weaknesses in the conversion funnel based on the dynamically analyzed data.

Integration assurance with existing retail analytics systems.

Given that BeaconLyte is integrated with other retail systems, When a user accesses the Dynamic Funnel Analysis, Then the data displayed should accurately reflect inputs from the integrated systems without discrepancies.

User-friendly visualization of conversion funnel stages.

Given I am viewing the conversion funnel visualizer, When I scroll through the stages of the funnel, Then I should easily identify the stages with the highest drop-off rates using color coding and percentage indicators.

Comparison of historical data against current funnel performance.

Given I have historical data available, When I initiate a comparison report, Then the system should provide a visual comparison between current and historical conversion rates at each funnel stage.

Testing the performance under varying traffic conditions.

Given the system is operational, When I simulate varying traffic flow through the funnel, Then the analysis should accurately reflect changes in user behavior and drop-off points based on different traffic scenarios.

Alerts for significant changes in funnel metrics.

Given I have set threshold parameters for key metrics, When the dynamic analysis detects significant deviations in the metrics, Then an alert should be sent to my dashboard notifying me of potential issues requiring immediate attention.

Customizable Visualization Options

User Story: As a Retail Analyst, I want customizable visualization options for the conversion funnel so that I can present data in a way that resonates with my audience and highlights critical insights.
Description: The Customizable Visualization Options requirement ensures that users can tailor the visual representation of the conversion funnel according to their specific needs. Retail analysts should have the capability to adjust the visuals through a variety of templates, colors, and presentation styles. This flexibility will enable users to highlight different stages of the funnel effectively and present data in a way that best communicates their findings to stakeholders. This feature addresses the diverse preferences of users and aids in clearer communication of insights, ultimately enhancing the decision-making process.
Acceptance Criteria: Retail analyst customizes the conversion funnel visual by selecting a different template and changing the color scheme to improve clarity during a presentation to stakeholders.

Given the user is on the Conversion Funnel Visualizer, When the user selects a new template and changes the color scheme, Then the visual representation updates to reflect the selected options immediately without performance issues.

A retail analyst needs to present data using a specific presentation style that meets their company’s branding guidelines during a strategy meeting.

Given the user is on the Conversion Funnel Visualizer, When the user selects a presentation style that aligns with their branding guidelines, Then the visual changes according to the selected style, and must retain all relevant data and labeling clearly and accurately.

Users utilize the visualization customization options to create reports for a quarterly business review, highlighting critical stages in the funnel with distinct colors and labels.

Given the user has access to the visualization customization options, When the user applies specific colors to various stages of the funnel and adds clear labels, Then the final report generated must accurately reflect these customizations in a readable format appropriate for presentation.

A retail analyst revisits previously saved visualizations with customized options and finds all custom settings retained for further analysis.

Given the user has previously customized the conversion funnel visualization, When the user reopens the visualizer, Then all customization settings (templates, colors, presentation styles) should be exactly as they were saved without any loss or corruption of data.

Retail analysts conduct a training session to teach team members how to use the customizable features of the Conversion Funnel Visualizer.

Given the training session is initiated, When team members utilize the Conversion Funnel Visualizer during the training, Then all customization options (templates, colors, presentation styles) should be accessible and functional for all participants, demonstrating ease of use for beginners.

Automated Anomaly Detection

User Story: As a Retail Analyst, I want automated anomaly detection in the conversion funnel so that I can quickly identify and address issues impacting conversion rates as they arise.
Description: The Automated Anomaly Detection requirement focuses on the implementation of machine learning algorithms that automatically detect unusual patterns in the conversion funnel data. This feature will alert retail analysts to potential issues such as unexpected drop-offs or spikes in traffic, enabling prompt investigations and necessary adjustments. By proactively identifying anomalies, retailers can react quickly to potential problems, ensuring smoother operations and maintaining high conversion rates. This integration into the BeaconLyte platform enhances the overall intelligence of the analytics provided to retailers.
Acceptance Criteria: Automated anomaly detection alerts when unusual patterns are identified in the conversion funnel data.

Given that the system has processed conversion funnel data, when it detects an anomaly (such as a drop-off rate exceeding 20% compared to the average), then it should trigger an alert to the retail analyst.

Retail analysts review alerts generated by the automated anomaly detection feature within the BeaconLyte platform.

Given that an alert has been triggered, when a retail analyst accesses the alert dashboard, then they should see the details of the anomaly, including metrics such as drop-off rate, time frame, and affected segments.

The machine learning algorithms are trained and optimized for detecting anomalies in various retail scenarios.

Given that the system has been trained on at least 3 months of historical conversion funnel data, when it is deployed, then it should accurately detect at least 90% of known anomalies in a test set of new data.

Retail analysts receive notifications about detected anomalies in real-time.

Given that an anomaly has been detected, when the alert is triggered, then the system should send real-time notifications via email and in-app alerts to the designated retail analysts.

The system allows retail analysts to customize the sensitivity of anomaly detection parameters.

Given that a retail analyst accesses the anomaly detection settings, when they adjust the sensitivity level (low, medium, high), then the system should reflect those changes in the frequency and type of alerts generated.

Performance of the automated anomaly detection feature is measured against expected benchmarks.

Given that the automated anomaly detection system is running for a specified period, when its performance is analyzed, then it should demonstrate an improvement in conversion rates by at least 10% over a baseline measurement taken before implementation.

Segmented User Journey Insights

User Story: As a Retail Analyst, I want segmented user journey insights so that I can understand how different customer groups interact with the funnel and optimize marketing strategies accordingly.
Description: The Segmented User Journey Insights requirement allows retail analysts to explore the conversion funnel based on user segments, such as demographics, purchasing behavior, and more. This capability will enable users to understand how different groups of customers interact with the funnel, providing deeper insights into customer preferences and behavior. By analyzing these segmented insights, retail analysts can tailor marketing strategies and optimize the customer experience for different user types, ultimately leading to higher conversion rates and enhanced customer satisfaction.
Acceptance Criteria: Retail analyst opens the Conversion Funnel Visualizer and selects a specific user segment to analyze, such as 'Purchase History: High Value Customers'.

Given the retail analyst is on the Conversion Funnel Visualizer page, when they select the 'High Value Customers' segment, then the funnel should update to display the relevant data for that segment, including conversion rates at each stage.

Retail analyst navigates to the Segmented User Journey Insights section and applies multiple filters to segment user data.

Given the retail analyst is in the Segmented User Journey Insights section, when they apply filters for 'Demographics: Ages 18-25' and 'Behavior: First-Time Purchasers', then the system should correctly display insights specific to these filtered segments.

Retail analyst reviews the funnel drop-off points to identify where significant user segment abandonment occurs during the purchase process.

Given the retail analyst has applied a user segment filter, when they analyze the drop-off report, then the system should highlight and provide percentages for the top three stages where buyers from that segment drop off.

Retail analyst compares conversion rates between different user segments to determine the effectiveness of targeted marketing strategies.

Given the retail analyst has selected two different segments, when they generate a comparison report, then the system should display the conversion rates side by side and the percentage difference between the segments.

Retail analyst exports segmented insights data for presentation to the marketing team.

Given the retail analyst has generated a report for a specific user segment, when they click the export button, then the system should download a CSV file of the insights with all relevant data included.

Retail analyst sets up real-time alerts for significant changes in conversion rates for specific user segments.

Given the retail analyst is in the alert settings, when they configure an alert for a 10% drop in conversion rate for 'Repeat Customers', then the system should correctly save this alert setting and trigger notifications when the condition is met.

Integration with A/B Testing Tools

User Story: As a Retail Analyst, I want integration with A/B testing tools so that I can implement and measure the effectiveness of different strategies within the conversion funnel.
Description: The Integration with A/B Testing Tools requirement will allow retail analysts to connect the conversion funnel visualizer with existing A/B testing platforms. This feature will support the testing of different designs, messaging, or processes within the funnel to determine which variations yield the best conversion rates. By enabling direct feedback from A/B tests, retail analysts can make informed adjustments based on empirical data, enhancing the efficacy of the sales funnel and maximizing conversion opportunities.
Acceptance Criteria: Retail analyst successfully integrates the Conversion Funnel Visualizer with an A/B testing tool to initiate a test on design variations of the funnel.

Given the integration is complete, when the retail analyst selects an A/B testing tool from the interface, then the connection should be established without errors, allowing for the selection and configuration of test parameters.

Retail analyst runs A/B tests using different funnel designs and monitors real-time performance metrics through the Visualizer.

Given an A/B test is running, when the analyst navigates to the Conversion Funnel Visualizer, then they should see real-time metrics updated for each variation, including conversion rates and drop-off points.

Retail analyst analyzes the results from the A/B tests through the Conversion Funnel Visualizer to determine the most effective design.

Given A/B test results are available, when the retail analyst accesses the analysis report generated by the Visualizer, then they should be able to view comparative metrics, visual representations, and actionable insights to make informed decisions.

Retail analyst makes adjustments to the conversion funnel based on A/B test results obtained from the integrated tools.

Given the analyst has identified a winning variation from A/B testing results, when they apply those changes to the conversion funnel setup, then the updated funnel should reflect the new design and functionalities without issues.

Retail analyst receives alerts on significant changes in conversion rates during A/B testing.

Given the A/B testing is in progress, when the conversion rates vary significantly from the baseline, then the system should trigger alerts to the retail analyst with clear indications of the variation's performance status.

Retail analyst connects multiple A/B testing tools to the Conversion Funnel Visualizer for enhanced testing options.

Given the integration capabilities exist, when the retail analyst attempts to connect multiple A/B tools simultaneously, then they should be able to successfully link and configure each tool without system errors or conflicts.

Behavioral Heatmap

The Behavioral Heatmap visualizes customer engagement intensity across different touchpoints over time. By identifying peak interaction moments, Retail Analysts can optimize marketing efforts and operational strategies to capitalize on high-engagement periods, fostering deeper connections with customers.

Requirements

Real-Time Data Integration

User Story: As a Retail Analyst, I want real-time data integration so that I can view the most current customer engagement metrics and respond swiftly to trends.
Description: The Real-Time Data Integration requirement enables BeaconLyte to seamlessly aggregate data from various retail systems. This functionality ensures that customer interactions, sales data, and inventory levels are continuously updated in the Behavioral Heatmap. By consolidating data streams in real-time, retailers can access the most current insights, empowering them to make immediate, informed decisions. This integration not only enhances the accuracy of the heatmap visualizations but also promotes a more agile marketing strategy, fostering timely engagement with customers based on their latest behaviors and preferences.
Acceptance Criteria: Real-time data is captured from point-of-sale systems during high traffic periods, ensuring that all customer transactions are reflected immediately in the Behavioral Heatmap.

Given that a customer makes a purchase at the point-of-sale, when the transaction occurs, then the data should update in the Behavioral Heatmap within 5 seconds of the transaction completion, showing the engagement intensity immediately.

Retail analysts access the Behavioral Heatmap dashboard to evaluate the impact of a recent marketing campaign during peak interaction periods.

Given that a marketing campaign was launched, when retail analysts access the dashboard, then they should view the updated Behavioral Heatmap showing engagement levels for the campaign period, with accurate visualization reflecting real-time data for each touchpoint.

Inventory levels are updated in real-time to reflect changes based on customer interactions and purchases, impacting the Behavioral Heatmap.

Given that inventory levels change due to sales or restocks, when an order is processed, then the Behavioral Heatmap should reflect the updated inventory data in real-time, affecting customer engagement visualizations accordingly.

The Behavioral Heatmap provides insights on customer engagement immediately following a promotional event to evaluate its effectiveness.

Given a promotional event has concluded, when the Behavioral Heatmap is accessed, then it should display engagement metrics during and after the event with real-time data reflecting customer interactions identified through the event.

Multiple data sources such as online and offline sales channels are integrated into the Behavioral Heatmap to provide a comprehensive view of customer engagement.

Given data from various retail system sources is available, when the Behavioral Heatmap is generated, then it should aggregate data from all sources and display a unified view of customer behaviors across all channels in real-time.

The system sends alerts to retail analysts when engagement exceeds predefined thresholds during specific periods.

Given engagement levels exceed a predefined threshold, when the data is processed in real-time, then alerts should be sent to retail analysts immediately to notify them of this significant engagement spike for timely intervention.

Customizable Time Frames

User Story: As a Retail Analyst, I want to customize time frames for the heatmap analysis so that I can focus on periods of interest and derive actionable insights from various engagement phases.
Description: The Customizable Time Frames requirement allows Retail Analysts to adjust the time periods for which the Behavioral Heatmap visualizes customer engagement data. Users can select specific date ranges (daily, weekly, monthly) or create custom time frames that align with their operational needs. This capability will enable retailers to analyze customer engagement patterns over different intervals, helping them to identify long-term trends as well as short-term spikes in activity. This flexibility enhances the utility of the Behavioral Heatmap, allowing for more tailored marketing and operational strategies.
Acceptance Criteria: Retail Analyst selects a daily timeframe to analyze customer engagement on a specific date, such as a holiday or promotional event.

Given the Behavioral Heatmap feature is active, when the Retail Analyst selects the daily option and specifies a date, then the heatmap should display customer engagement intensity for that specific day accurately.

Retail Analyst chooses a weekly timeframe to review engagement trends over a week, comparing performance across different weekdays.

Given the Behavioral Heatmap feature is enabled, when the Retail Analyst selects the weekly option for a date range, then the system should generate and display engagement data for each day within that week, highlighting peaks and troughs appropriately.

Retail Analyst creates a custom timeframe to analyze customer engagement during a sales campaign that spanned multiple months.

Given the Behavioral Heatmap functionality is in use, when the Retail Analyst defines a custom date range for the campaign, then the heatmap should reflect accurate engagement data for the selected months, presenting insights that facilitate analysis of promotional effectiveness.

Retail Analyst applies the monthly timeframe to evaluate long-term customer engagement trends.

Given the Behavioral Heatmap feature is functioning, when the Retail Analyst selects the monthly option, then the platform should accurately display aggregated engagement metrics for each month within the specified range, allowing for comparative analysis.

Retail Analyst utilizes filters to segment data during specified time frames based on customer demographics.

Given the filters are set, when the Retail Analyst adjusts the time frame to a specific date range, then the heatmap must display the engagement data filtered by the selected demographics accurately reflecting the specified timeframe.

Retail Analyst reviews the Behavioral Heatmap at the end of a designated period to assess marketing strategies' effectiveness.

Given the report generation feature is active, when the Retail Analyst selects a time frame for the heatmap, then they should be able to generate a report summarizing the engagement findings and insights for that period, with clear visualizations.

Engagement Metric Filters

User Story: As a Retail Analyst, I want to apply filters to engagement metrics so that I can hone in on the specific customer behaviors that impact my marketing strategies.
Description: The Engagement Metric Filters requirement introduces the ability to filter customer interaction data based on specific metrics, such as clicks, purchases, or social media interactions. This feature allows Retail Analysts to refine their analysis of engagement intensity and understand what actions correlate with high engagement periods. By applying these filters, users can focus on the most relevant interactions that drive performance, streamlining their decision-making process. This targeted approach enhances insights, enabling more strategic marketing and operational decisions.
Acceptance Criteria: Retail Analyst applies filters to analyze engagement metrics for email marketing campaigns to identify the most effective touchpoints.

Given a Retail Analyst is on the Behavioral Heatmap dashboard, When they apply the 'Clicks' filter, Then the system should display only those interactions where customers clicked on links in marketing emails, allowing for an accurate analysis of engagement.

Retail Analyst seeks to understand the correlation between social media interactions and customer purchases to enhance marketing strategies.

Given the Retail Analyst refreshes the Behavioral Heatmap, When they select 'Purchases' and 'Social Media Interactions' filters simultaneously, Then the system should generate a visual representation of both metrics over time, highlighting their correlation.

Retail Analyst reviews engagement metrics for different products to determine which items attract more customer interaction.

Given the Retail Analyst is viewing the Behavioral Heatmap, When they filter by 'Product Category', Then the dashboard should update to show engagement metrics specific to the selected product category, enabling targeted insights.

Retail Analyst uses engagement metrics to assess the impact of a seasonal marketing campaign and make data-driven decisions for future campaigns.

Given the Retail Analyst applies the 'Purchases' filter to the last three months, When they analyze the data, Then they should be able to identify spikes in customer purchases corresponding to specific marketing campaigns.

Retail Analyst wants to track customer interactions over a specific timeframe to improve future marketing efforts.

Given the Retail Analyst is on the Behavioral Heatmap, When they set a custom date range and apply engagement filters, Then the dashboard should refresh to display engagement metrics specific to that timeframe, ensuring relevant data analysis.

Retail Analyst needs to filter engagement metrics by customer demographics to understand their preferences better.

Given a Retail Analyst selects demographic filters (e.g., age, location), When they apply these filters, Then the Behavioral Heatmap should update to reflect engagement metrics for the selected demographic, providing targeted insights for marketing strategies.

Retail Analyst examines the overall effectiveness of different engagement actions to refine operational strategies.

Given the Retail Analyst applies multiple engagement action filters, When they view the results, Then the system should highlight which actions (e.g., clicks, purchases, shares) had the highest engagement scores, allowing for strategic operational adjustments.

Visual Analytics Dashboard

User Story: As a Retail Analyst, I want a visual analytics dashboard so that I can easily interpret engagement data and share actionable insights with my team.
Description: The Visual Analytics Dashboard requirement encompasses the creation of an intuitive, user-friendly dashboard that displays the Behavioral Heatmap and associated analytics in a visually appealing format. This dashboard should support easy navigation and provide comprehensive visual data representations, including graphs and charts, to reflect customer engagement intensity over time. A well-designed dashboard enhances user experience, making it easier for Retail Analysts to interpret data quickly and efficiently. It is critical for presenting insights that drive marketing and operational decisions in a digestible format.
Acceptance Criteria: Visualizing customer engagement over a defined period using the Behavioral Heatmap on the Visual Analytics Dashboard.

Given that the user selects a date range, when the user accesses the Behavioral Heatmap, then the dashboard should display visual representations of customer engagement intensity for that date range using color-coded signals.

Navigating through different data visualizations on the Visual Analytics Dashboard.

Given that the dashboard is loaded, when the user clicks on different sections such as 'Engagement Over Time' or 'Peak Interaction Periods', then the dashboard should dynamically update to display the respective visual analytics without any delay.

Accessing real-time alerts on customer engagement metrics via the Visual Analytics Dashboard.

Given that the analytics dashboard has been integrated with alert systems, when customer engagement metrics exceed predefined thresholds, then the dashboard should show real-time notifications with actionable insights prominently displayed.

Customizing the dashboard layout and visual elements to fit user preferences.

Given that the user is on the Visual Analytics Dashboard, when the user personalizes the dashboard by rearranging widgets or selecting different chart types, then the dashboard should retain these customizations for future sessions without requiring reconfiguration.

Comparing customer engagement metrics across different campaigns on the Visual Analytics Dashboard.

Given that the user selects multiple campaigns, when the user views the comparative analysis section, then the dashboard should present the engagement metrics side by side in a clear, visual format such as a bar chart or line graph.

Exporting visual data representations from the Visual Analytics Dashboard.

Given that the user wants to share insights, when the user selects the export option, then the dashboard should allow the user to export the current view as a PDF or image file, preserving the visual integrity of the data presented.

Automated Reporting Alerts

User Story: As a Retail Analyst, I want automated reporting alerts so that I can be promptly informed of significant changes in customer engagement metrics and react accordingly.
Description: The Automated Reporting Alerts requirement enables users to set up notifications based on predefined thresholds or significant changes in customer engagement metrics visualized in the Behavioral Heatmap. When certain metrics exceed or fall below set levels, alerts will be generated and sent to Retail Analysts. This function fosters proactive decision-making by ensuring that relevant stakeholders are notified about critical changes in real-time, allowing them to take immediate action to capitalize on high engagement or address potential issues.
Acceptance Criteria: Retail Analysts receive notifications for customer engagement metrics exceeding predefined thresholds during peak shopping seasons.

Given a Retail Analyst has set specific engagement thresholds, When customer engagement metrics cross these thresholds during the specified period, Then an automated alert is sent to the Retail Analyst's designated communication channel.

Retail Analysts are alerted when customer engagement metrics fall below acceptable levels for three consecutive days.

Given a Retail Analyst has defined minimum acceptable thresholds for engagement metrics, When metrics fall below these thresholds for three consecutive days, Then an automated alert is generated and sent to the Retail Analyst.

Retail Analysts want to customize notification settings to receive alerts only for specific metrics of interest.

Given a Retail Analyst accesses the notification settings, When they customize their alert preferences for specific metrics, Then only the selected metrics trigger alerts when predefined conditions are met.

Retail Analysts need to review the historical performance of alerts triggered by customer engagement changes.

Given a Retail Analyst requests a report on historical alerts, When the report is generated, Then it includes the date, time, metric changes, and the specifics of the alerts fired for the last three months.

Retail Analysts require a way to test the alert system functionality before deploying it live.

Given a Retail Analyst initiates a test alert through the reporting tool, When the alert system processes the test scenario, Then the system successfully sends a test alert and logs the action without impacting live metrics.

Retail Analysts want to receive alerts on multiple platforms including email and mobile app notifications.

Given a Retail Analyst has subscribed to alerts across different platforms, When a metric condition triggers an alert, Then the alert is sent simultaneously to both email and mobile app notifications as configured by the user.

Trend Analyzer

The Trend Analyzer empowers Category Managers by providing advanced visualizations of sales trends over time. By dynamically showcasing shifts in product performance, this feature enables users to quickly identify emerging patterns and seasonal fluctuations, allowing for timely adjustments in inventory and promotional strategies that align with consumer demand.

Requirements

Dynamic Sales Visualization

User Story: As a Category Manager, I want to visualize sales trends in real-time so that I can make timely decisions regarding inventory and promotions based on current consumer demand.
Description: The Dynamic Sales Visualization requirement aims to provide real-time graphical representations of sales data across different categories, allowing Category Managers to quickly identify trends in product performance. This requirement encompasses various charting options like line graphs, bar charts, and heatmaps that adapt based on user-defined parameters, such as time frames and product categories. By enabling users to visualize data in an intuitive manner, this feature enhances decision-making processes and supports proactive inventory management and promotional planning, ultimately boosting sales performance and customer satisfaction.
Acceptance Criteria: Dynamic Sales Visualization displays real-time sales data for a specific product category, allowing the Category Manager to assess performance during a seasonal sale period.

Given the Category Manager selects a product category and a specific time frame, when the Dynamic Sales Visualization is generated, then the displayed graph should accurately represent the sales data for that time frame using a line graph format.

A Category Manager needs to compare sales trends between two categories to make informed inventory decisions.

Given the Category Manager selects two product categories and specifies the same time frame, when the Dynamic Sales Visualization is executed, then the resulting chart should show both categories' sales trends side by side for easy comparison.

The Category Manager wants to identify spikes in sales during a promotional campaign using bar charts.

Given the Category Manager specifies the promotional campaign period, when the Dynamic Sales Visualization is created, then the bar chart should clearly depict sales spikes compared to non-promotional periods, with appropriate labeling for clarity.

During a product review meeting, the Category Manager needs to present sales trends to stakeholders using a heatmap for quick reference.

Given the Category Manager selects a product category and a specified time frame, when the Dynamic Sales Visualization is generated using a heatmap, then the heatmap should provide an intuitive overview of sales density, with a color gradient representing different sales levels.

The Category Manager requires the ability to adjust time frames on-the-fly to analyze sales trends quickly.

Given the Category Manager is viewing the Dynamic Sales Visualization, when they change the time frame using a dropdown menu, then the visualization should refresh automatically and display the updated sales data promptly without any errors.

A Category Manager wants to save a specific visualization layout for future reference and analysis.

Given the Category Manager customizes a visualization and selects the save option, when they retrieve the saved layout later, then the system should accurately restore the visualization to its last saved state, including any specific filters or parameters set.

Information security needs to be ensured while accessing the Dynamic Sales Visualization.

Given the Category Manager attempts to access the Dynamic Sales Visualization, when they log in with valid credentials, then the system should display the visualization in compliance with access control policies, ensuring that the data is secure and only available to authorized personnel.

Seasonal Pattern Detection

User Story: As a Category Manager, I want to receive alerts on predicted seasonal trends so that I can prepare and adjust my inventory and marketing strategies in advance.
Description: The Seasonal Pattern Detection requirement focuses on implementing algorithms that analyze historical sales data to identify recurring seasonal trends for various products. By leveraging machine learning techniques, this feature will detect changes in consumer behavior over time and predict future sales patterns. This predictive capability allows Category Managers to plan their inventory and marketing strategies around anticipated demand shifts, ensuring that products are available when needed and that promotional campaigns are timed for maximum effectiveness.
Acceptance Criteria: Seasonal sales pattern analysis for holiday products.

Given a dataset of historical sales data for a specific product category during the holiday season, When the Seasonal Pattern Detection is executed, Then it should accurately identify the peak sales weeks and predict sales for the upcoming holiday period with at least 85% accuracy based on previous trends.

Analyzing sales data post-promotion to assess effectiveness.

Given a product that has undergone a specific promotional campaign, When the Seasonal Pattern Detection algorithm is applied to the sales data of that product, Then it should highlight any significant increases in sales volume or shifts in purchasing patterns during and after the promotion period compared to previous years.

Visual representation of seasonal trends on the dashboard.

Given that the Seasonal Pattern Detection analysis has been completed, When a Category Manager accesses the Trend Analyzer dashboard, Then it should display visual graphs that represent identified seasonal trends for each product, with clearly labeled axes and legends for easy interpretation.

Real-time alerts for emerging seasonal trends.

Given that the Seasonal Pattern Detection feature is active, When detection of emerging trends occurs based on recent sales data, Then it should trigger real-time alerts to the Category Manager with specific recommendations for inventory adjustments or promotional actions within 24 hours.

User feedback loop for refining trends detection algorithms.

Given multiple Category Managers have used the Seasonal Pattern Detection feature, When user feedback is collected on the accuracy and relevance of detected trends, Then at least 70% of the feedback should indicate satisfaction regarding the feature's predictions, enabling continuous improvement of the algorithms.

Comparison of predictive accuracy before and after algorithm updates.

Given that an update to the detection algorithms has been implemented, When sales data is analyzed across multiple past seasons, Then the predictive accuracy of the updated Seasonal Pattern Detection should be at least 10% higher than the previous accuracy level over the same historical data set.

Integration with existing inventory management systems.

Given that the Seasonal Pattern Detection feature has provided predicted inventory needs for the next season, When this data is sent to the existing inventory management system, Then it should seamlessly integrate, reflecting the updated inventory requirements without any discrepancies within 2 hours of prediction generation.

Customizable Dashboard

User Story: As a Category Manager, I want to customize my dashboard with the most relevant KPIs so that I can focus on the data that impacts my decision-making process the most.
Description: The Customizable Dashboard requirement enables users to personalize their interface by choosing which KPIs and metrics are displayed prominently. Users can select from a range of predefined widgets and arrange them according to their preferences. This feature enhances user experience by allowing Category Managers to focus on the most relevant data for their specific needs, ultimately providing them with a tailored view that aids in quicker analysis and strategy formulation.
Acceptance Criteria: As a Category Manager, I want to personalize my dashboard by selecting specific KPIs to display, so I can focus on the most relevant metrics for my product category.

Given I am logged into the BeaconLyte platform, when I access the customizable dashboard feature, then I should be able to select and deselect KPIs from a list of predefined options and have those changes reflected immediately on my dashboard.

As a Category Manager, I want to rearrange the widgets on my dashboard, so I can prioritize the information that is most important to me.

Given I am on my customized dashboard, when I drag and drop the widgets to rearrange them, then the new arrangement should be saved automatically and persist when I log back in.

As a Category Manager, I want to revert my dashboard to the default settings, so I can start fresh if I don't like my current configuration.

Given I have customized my dashboard, when I click the 'Reset to Default' button, then my dashboard should return to the original default layout and selected KPIs.

As a Category Manager, I want to save different layouts of my dashboard, so I can switch between views easily for different analyses.

Given I have multiple customized layouts saved, when I switch between them, then the dashboard should reflect the selected layout and its corresponding KPIs and widget arrangement without any delays.

As a Category Manager, I want to receive visual alerts on my dashboard for significant changes in key metrics, so I can act quickly to market shifts.

Given I have set threshold values for certain KPIs, when those values exceed or drop below the thresholds while I am viewing the dashboard, then a visual alert should appear on the dashboard indicating this change.

Automated Reporting

User Story: As a Category Manager, I want automated reports on product performance so that I can easily access insights without spending time on manual data collection.
Description: The Automated Reporting requirement seeks to generate regular reports that summarize sales trends, inventory levels, and promotional performance without manual intervention. This feature will allow users to schedule reports, select the metrics to be included, and choose output formats, streamlining the reporting process and saving valuable time. By having automated access to crucial business insights, Category Managers can make informed decisions based on up-to-date information without the hassle of manual data extraction.
Acceptance Criteria: Automated reporting is scheduled to run every Monday at 9 AM, providing Category Managers with updated sales trends and inventory levels for the previous week.

Given the scheduling system is set up, when the report is due to run, then the report is generated automatically and saved in the selected output format (CSV, PDF).

A Category Manager customizes a report by selecting specific metrics, such as sales trends and promotional performance, for a quarterly overview.

Given the metrics selection interface is functional, when the user selects specific metrics and saves the report settings, then the report includes only the selected metrics in the generated output.

The Automated Reporting feature is tested for accuracy to ensure that the output data matches the current database records.

Given the latest data is available in the database, when the automated report is generated, then the report accurately reflects the current sales trends and inventory levels as per the database records.

A Category Manager receives an email notification when a scheduled automated report is successfully generated and available for download.

Given the automated reporting system is functioning, when a report is generated, then an email notification is sent to the Category Manager with the download link included.

The Category Manager reviews the generated report for load performance to ensure it opens quickly and without errors.

Given the generated report is accessed by the Category Manager, when the report is opened, then it loads within 5 seconds without any display errors.

The user interface allows Category Managers to seamlessly navigate through the report scheduling and customization options without confusion.

Given the user interface is designed for ease of use, when a Category Manager accesses the scheduling options, then they can intuitively navigate, schedule, and customize reports without needing additional assistance.

Reports can be exported in multiple formats, ensuring accessibility for various user needs.

Given the report generation is complete, when the user selects the export option, then the report can be successfully exported in at least three different formats (CSV, PDF, XLSX).

Real-time Alert System

User Story: As a Category Manager, I want to receive real-time alerts for critical sales and inventory metrics so that I can respond quickly to changes in consumer behavior.
Description: The Real-time Alert System requirement implements a system to notify Category Managers about significant sales fluctuations, inventory shortages, or other critical metrics that require immediate attention. This feature will allow users to set alert thresholds for key performance indicators (KPIs), ensuring they are promptly informed of any unusual activity that could impact sales or inventory management. By receiving timely alerts, users can react swiftly to both opportunities and threats in the marketplace.
Acceptance Criteria: Category Manager receives a notification for a significant drop in sales for a specific product category during a key sales period.

Given the Category Manager has set a sales drop threshold of 20%, when daily sales data shows a drop of 25% from the previous week, then the system sends an immediate alert via email and in-app notification to the Category Manager.

Category Manager sets custom alert thresholds for inventory levels to ensure timely restocking.

Given the Category Manager defines a minimum inventory threshold of 50 units, when the inventory level falls to 40 units, then the system triggers an alert indicating that restocking is needed, sent via email and in-app notification.

Category Manager wants to track performance metrics for seasonal products to optimize promotions.

Given that the Category Manager has selected a seasonal product with a specific sales target, when sales drop below the defined target for two consecutive weeks, then the system sends an alert to the Category Manager highlighting the need for promotional strategies.

Category Manager is alerted about unusual spikes in sales for specific products to maximize opportunities.

Given the Category Manager has configured alerts for sales spikes over 30%, when a product's sales increase by 40% over the last 24 hours, then the system sends an alert to the Category Manager with relevant details about the sales surge.

Category Manager monitors alerts to ensure they are actionable and relevant for decision-making.

Given a list of alerts generated in the last 30 days, when the Category Manager reviews the alerts, then at least 90% of the alerts should lead to actionable insights or changes in strategy based on the data provided.

System provides summary reports of alert performance for category managers to review effectiveness.

Given a reporting period set by the Category Manager (monthly or quarterly), when the reports are generated, then the system includes metrics on the number of alerts triggered, the response time, and the actions taken, ensuring that all data is accessible and clear.

Category Manager receives alerts for multiple KPIs simultaneously to manage overall performance.

Given the Category Manager has set alerts for sales, inventory, and customer satisfaction metrics, when any of these KPIs breach their respective thresholds, then the system sends aggregated alerts for all triggered KPIs to the Category Manager’s preferred communication channel.

Collaboration Tools Integration

User Story: As a Category Manager, I want to collaborate with my team directly within the analytics platform so that we can align our strategies and make informed decisions collectively.
Description: The Collaboration Tools Integration requirement focuses on enabling seamless interaction between Category Managers and other stakeholders through integrated communication tools. This feature will support comments, annotations, and sharing of trend analysis results within the platform, promoting collaboration on inventory decisions and promotional strategies. By fostering communication among team members, this integration enhances collective decision-making and aligns business strategies across departments, thus ultimately improving the overall effectiveness of retail operations.
Acceptance Criteria: Category Managers are collaborating on inventory adjustments during a quarterly review meeting using the Trend Analyzer feature to discuss sales trends and required stock changes based on collaborative insights.

Given that a Category Manager is reviewing sales data, when they use the collaboration tools to add comments and annotations on specific trends, then other team members should receive notifications of these comments in real-time.

During a weekly products performance analysis, stakeholders are using the integrated communication tools to share insights derived from the Trend Analyzer with team members from different departments.

Given that the Trend Analyzer displays sales trends, when a stakeholder shares the trend analysis link via the collaboration tool, then all invited team members should have access to view and discuss the specific analysis in real-time.

When the Category Manager encounters an unexpected dip in sales for a particular product, they use the trend analysis to discuss possible causes and solutions with their team through the collaboration tools.

Given that a transaction report shows a significant drop in sales, when the Category Manager initiates a discussion using the collaboration tool, then all stakeholders should be able to contribute their insights and attach relevant data or examples.

The marketing department needs to align promotional strategies based on the insights shared via the Trend Analyzer, facilitating discussions on promotional content and timing.

Given that the marketing team accesses the trend analysis, when they utilize the collaboration tools to draft and share promotional strategies, then they should be able to incorporate real-time feedback from the Category Managers before finalizing any content.

During an inventory planning session, the Category Manager uses comments to highlight critical trends, asking for input from the finance team regarding budget implications.

Given that critical trends are highlighted with comments, when the finance team views these comments, then they should be able to respond directly within the collaboration tool, ensuring all decisions are informed by current financial data.

As part of the post-mortem analysis for a failed campaign, team members use collaboration tools to dissect the reasons for the outcome and strategize future actions based on trend data.

Given that the post-mortem meeting is initiated, when the team discusses the insights from the Trend Analyzer, then all members should be able to propose and document actionable strategies in shared notes that everyone can access.

Competitive Benchmarking

Competitive Benchmarking allows Category Managers to compare the performance of their product categories against key competitors. This feature aggregates pricing, promotion strategies, and sales data, giving users actionable insights to enhance their competitive edge and adjust strategies based on real-time market dynamics.

Requirements

Dynamic Pricing Analysis

User Story: As a Category Manager, I want to receive real-time pricing analysis so that I can adjust my product prices proactively in response to competitor actions and market shifts to maximize profitability.
Description: The Dynamic Pricing Analysis requirement will enable retailers to analyze and adjust their pricing strategies in real-time based on market trends and competitor pricing. This feature will utilize machine learning algorithms to process vast amounts of sales and pricing data, allowing users to optimize their prices dynamically to enhance competitiveness and profitability. Ultimately, this requirement aims to empower retailers with actionable insights that facilitate timely decision-making and foster adaptive pricing strategies, directly impacting sales performance and market positioning.
Acceptance Criteria: Real-time Price Adjustment Based on Competitor Pricing Trends

Given that a competitor's price drops by 10% for a specific product category, when the Dynamic Pricing Analysis feature processes this change, then the system should recommend a corresponding price adjustment that maintains a competitive edge while ensuring profitability.

Integration with Existing Pricing Strategies

Given that a retailer has a set pricing strategy already in place, when the retailer inputs this data into the Dynamic Pricing Analysis, then the system should integrate the existing strategy with real-time analytics and provide updated pricing recommendations based on current market conditions and competitor actions.

User-Friendly Dashboard for Pricing Insights

Given that a Category Manager accesses the Competitive Benchmarking dashboard, when they select the 'Dynamic Pricing Analysis' option, then the dashboard should display real-time pricing insights, competitor comparisons, and recommended price adjustments visually in a user-friendly format.

Automated Alerts for Price Changes

Given that the Dynamic Pricing Analysis monitors market changes, when a significant price change occurs (e.g., greater than 5% fluctuation), then the system should send an automated alert to the designated users to inform them about the pricing opportunity and recommended actions.

Historical Price Impact Analysis

Given that the Dynamic Pricing Analysis has access to historical sales data, when a user requests an analysis, then it should provide insights into how past price changes have impacted sales volume and overall profitability for similar products.

Performance Metrics After Price Adjustments

Given that changes have been made to pricing based on the Dynamic Pricing Analysis, when the Category Manager reviews performance metrics one month later, then the system should display an increase in sales volume by at least 10% and improved profit margins compared to the previous period.

User Access Control for Pricing Analysis Features

Given that multiple users access the Dynamic Pricing Analysis, when a user attempts to make changes to the pricing recommendations, then the system should enforce user access controls based on their role, preventing unauthorized modifications.

Promotional Strategy Insights

User Story: As a Category Manager, I want to analyze the effectiveness of my promotional campaigns in comparison to my competitors so that I can optimize future promotions based on proven successes and best practices.
Description: The Promotional Strategy Insights requirement will provide users with comprehensive analytics around the effectiveness of their promotional campaigns compared to those of their competitors. This feature will aggregate historical and real-time data to assess key performance indicators (KPIs) such as customer engagement, sales uplift, and return on investment (ROI) for promotions. By allowing users to benchmark their promotional strategies against industry standards, this requirement will help retailers refine their marketing approaches and improve overall campaign effectiveness, driving better customer engagement and increased sales.
Acceptance Criteria: Users can access Promotional Strategy Insights to evaluate the effectiveness of their promotional campaigns against competitors in real-time.

Given a user is logged into BeaconLyte, when they navigate to the Promotional Strategy Insights section, then they should see a dashboard displaying comparative analytics of their promotional campaign effectiveness against industry benchmarks.

Category Managers can analyze the impact of multiple promotional campaigns over a specified period to identify trends in effectiveness and ROI.

Given that a Category Manager selects a date range for analysis, when they generate a report, then they should receive data showing sales uplift and customer engagement metrics for each promotional campaign during that period.

Users receive actionable insights from the Promotional Strategy Insights that highlight necessary adjustments based on competitive analysis.

Given that competitive promotion data is available, when the user accesses the insights report, then it should provide recommendations for promotional adjustments based on competitor performance and market trends.

Users must have the ability to filter promotional analytics by product category, enabling focused analysis on specific segments.

Given that a user is on the Promotional Strategy Insights dashboard, when they apply filters by product category, then the displayed analytics should update to reflect the selected product categories only.

The system should visualize data trends over time, allowing users to track the progression of their promotional effectiveness compared to competitors.

Given historical promotional data is available, when a user views the trend analysis section, then they should see a graph visualizing their promotional effectiveness and competitive benchmarks over the chosen timeframe.

Users are able to export Promotional Strategy Insights reports for further analysis or sharing with team members.

Given a user is viewing the Promotional Strategy Insights dashboard, when they click on the export button, then they should receive a download of the report in a universally accessible format (e.g., CSV or PDF).

Competitor Sales Tracking

User Story: As a Category Manager, I want to track my competitors' sales performance so that I can make informed decisions about my pricing and inventory strategies to stay competitive in the market.
Description: The Competitor Sales Tracking requirement will enable retailers to monitor key competitors' sales performance metrics continuously. This feature will involve collecting and analyzing data regarding competitors' sales figures, market share, and product performance over time. By integrating with market data APIs, this requirement will provide users with actionable insights into market positioning and help identify emerging trends. The goal is to equip retailers with critical information to make informed decisions regarding product placement, inventory management, and competitor response strategies.
Acceptance Criteria: Competitor Sales Metrics Visibility for Retail Managers

Given a Retail Manager has accessed the Competitor Sales Tracking feature, When the user selects a specific competitor, Then the system should display sales figures, market share data, and product performance metrics for that competitor in a clear and actionable manner.

Real-time Data Integration for Accurate Insights

Given the Competitor Sales Tracking system is live, When the market data APIs provide new sales information, Then the system should automatically update the displayed metrics without user intervention, ensuring that data is current and accurate.

Alerts for Significant Market Changes

Given the Competitor Sales Tracking feature is activated, When a key competitor's sales figures change significantly (e.g., a decrease/increase of 10% or more), Then an alert should be generated and sent to the Retail Manager via email and within the dashboard.

Comparative Analysis Against Internal Sales Data

Given the Competitor Sales Tracking feature is utilized, When the Retail Manager selects a comparison between competitor metrics and internal sales data, Then the system should display a side-by-side analysis allowing the user to easily spot differences.

Customizable Dashboard Widgets for Monitoring Metrics

Given a Retail Manager wishes to customize their analytics dashboard, When the user adds or removes widgets related to competitor sales metrics, Then the system should allow changes to be saved and reflected immediately on the dashboard.

Export Functionality for Sales Data Reports

Given that a Retail Manager has analyzed competitor sales data, When the user chooses to export the data, Then the system should provide options to download the report in multiple formats (CSV, PDF) without data loss.

User-Friendly Visualization of Sales Trends

Given a Retail Manager is using the Competitor Sales Tracking feature, When the user views sales data over time, Then the system should present clear visualizations (charts/graphs) that highlight sales trends, allowing for easy interpretation of market behavior.

Market Trend Visualization

User Story: As a Category Manager, I want to visualize market trends easily so that I can quickly understand shifts in consumer preferences and modify my strategies accordingly.
Description: The Market Trend Visualization requirement is designed to provide a customizable dashboard feature that visualizes evolving market trends and consumer behavior patterns. Users can access comparative graphs, heat maps, and trend indicators that summarize large datasets into easily interpretable images. This will enhance users' ability to understand shifts in market dynamics quickly and derive actionable insights for strategic planning. By offering a more intuitive representation of data, this feature will support Category Managers in identifying opportunities and risks, thereby optimizing their product strategies and decisions.
Acceptance Criteria: Category Manager views the Market Trend Visualization dashboard during a quarterly assessment meeting to analyze consumer behavior insights and trends over the past six months.

Given the user is a Category Manager, when they access the Market Trend Visualization dashboard, then they should see customizable graphs illustrating consumer behavior and market trends for the selected product categories over the specified timeframe.

A Category Manager customizes the dashboard filters to view specific product segments and promotional periods to gather targeted insights for the upcoming marketing strategy.

Given the user is on the Market Trend Visualization dashboard, when they apply filters for specific product segments and promotional periods, then the dashboard should refresh and display only the relevant data visualizations corresponding to the chosen filters.

During a strategy planning session, a Category Manager utilizes heat maps to identify high-performing regions for targeted promotions based on recent market trends.

Given the user is viewing the Market Trend Visualization dashboard, when they select the heat map feature, then they should be able to view distinct regional performance indicators, highlighting areas of high and low sales performance in real-time.

A Category Manager reviews trend indicators to assess the impact of recent promotions on sales performance in various categories before the next stock order.

Given the user clicks on the trend indicators section of the dashboard, when they analyze the sales data over the last promotional period, then they should see clear visualizations indicating upward or downward trends in sales, along with comparative data to previous periods.

A Category Manager shares insights from the Market Trend Visualization dashboard with the marketing team to inform upcoming campaign strategies.

Given the user has completed their analysis on the Market Trend Visualization dashboard, when they export the dashboard data, then the exported report should include all visualizations, data points, and comparative analyses clearly formatted for presentation to stakeholders.

A Category Manager monitors real-time alerts regarding sudden shifts in market trends and consumer behaviors from the Market Trend Visualization feature.

Given the user has enabled real-time alerts, when significant changes in consumer behavior or market trends are detected, then the system should send a notification or alert to the user detailing the changes and suggested actions.

Competitor Sentiment Analysis

User Story: As a Category Manager, I want to analyze competitor sentiment so that I can understand consumer opinions about their products and develop strategies to differentiate my offerings effectively.
Description: The Competitor Sentiment Analysis requirement will utilize natural language processing (NLP) to analyze online reviews, social media mentions, and other sources of consumer feedback regarding competitors' products. This feature will provide insights into public perception and sentiment around competitors’ offerings, allowing retailers to gauge brand reputation in real time. By consolidating this qualitative data, the requirement will help users identify strengths and weaknesses in competitor positioning, enabling more informed strategic decisions around product development and marketing efforts.
Acceptance Criteria: User searches for sentiment analysis of specific competitor products.

Given a user accesses the Competitor Sentiment Analysis feature, when they enter a specific competitor product, then the system should return an analysis report displaying sentiment scores, trends over time, and a summary of customer feedback for that product.

User needs to compare sentiment scores of multiple competitor products.

Given a user is in the Competitor Sentiment Analysis dashboard, when they select multiple competitor products, then the system should display a comparative sentiment score chart that highlights differences in public perception for the selected products.

User wants to receive real-time alerts about changes in competitor sentiment.

Given a user has set up alerts for a specific competitor, when there is a significant change in sentiment (e.g., increase or decrease of more than 15%), then the system should send an email notification and in-app alert to the user.

User reviews documented sentiment data to inform marketing strategy.

Given a user accesses the historical sentiment data, when they filter results by date range, then the system should display sentiment trends and insights that can be exported to CSV or integrated into marketing reports.

User seeks to understand the main themes of positive and negative sentiments.

Given a user views the sentiment report for a competitor, when they click on 'View Themes', then the system should display key themes derived from customer feedback, categorized by positive and negative sentiments.

User requires insights into sentiment from different geographic regions.

Given a user is analyzing a specific competitor's product, when they select geographic filters, then the system should present sentiment data segmented by region, showing how feedback varies across different areas.

User needs to ensure data accuracy and relevance of sentiment analysis.

Given a user accesses the Competitor Sentiment Analysis, when they check the data sources, then the system should provide a transparent view of the data sources used for sentiment analysis and last updated timestamps for verification.

Promotion Effectiveness Tracker

The Promotion Effectiveness Tracker evaluates the impact of various promotional activities on product category performance. By analyzing sales data and customer engagement metrics, this feature helps Category Managers identify which promotions yield the best results, enabling them to optimize future marketing efforts and maximize ROI.

Requirements

Sales Data Analyzer

User Story: As a Category Manager, I want to analyze past sales data related to promotions so that I can understand which campaigns were successful and replicate their effectiveness in future marketing efforts.
Description: The Sales Data Analyzer requirement involves developing a tool that aggregates and analyzes historical sales data, providing Category Managers with insights into past promotional campaigns. This tool will help in understanding trends and patterns, allowing retailers to make data-driven decisions on future promotions. The functionality includes user-friendly visualization of sales trends, comparison of promotional periods, and exporting reports for further analysis. This feature plays a crucial role in enhancing the visibility of how past promotions have impacted sales, optimizing marketing strategies, and ultimately boosting revenue.
Acceptance Criteria: Sales Data Trend Visualization for Category Managers

Given historical sales data is available, when a Category Manager accesses the Sales Data Analyzer, then the tool should display visual graphs representing sales trends over specified promotional periods.

Comparative Analysis of Promotional Campaigns

Given multiple promotional campaigns are recorded, when a Category Manager selects two campaigns for comparison, then the tool should provide a side-by-side analysis of sales performance metrics for both campaigns.

Exporting Sales Reports for Further Analysis

Given the analysis is complete, when a Category Manager clicks on the export button, then the tool should generate a downloadable report in PDF format that includes all visualizations and insights derived from the sales data.

Real-time Data Integration with Existing Systems

Given that the Sales Data Analyzer is integrated with other retail systems, when new sales data is entered, then the tool should automatically update visualizations and reports within a maximum of 5 minutes after data entry.

User-Friendly Interface for Category Managers

Given the tool is designed for Category Managers, when they navigate through the Sales Data Analyzer, then the interface should allow them to easily access different features with no more than 2 clicks for any major functionality.

Performance Metrics Display During Promotions

Given a promotional period is active, when the Sales Data Analyzer is accessed, then it should display real-time metrics of sales performance related to the ongoing promotion prominently on the dashboard.

User Training and Support for Sales Data Analyzer

Given that the tool has been implemented, when new users are onboarded, then there should be training sessions scheduled, and user guides should be made accessible to ensure users can utilize the tool effectively.

Customer Engagement Metrics

User Story: As a Marketing Analyst, I want to track customer engagement metrics for promotions so that I can measure the effectiveness of our marketing efforts and adjust strategies accordingly to improve customer reach and response.
Description: The Customer Engagement Metrics requirement focuses on tracking and analyzing customer interactions with promotional content across various channels. This includes measuring engagement rates such as click-through rates, conversion rates, and customer feedback. The feature aims to deliver insights on how customers are responding to different promotions, thus helping to tailor marketing strategies that resonate better with the target audience. By understanding customer engagement, retailers can refine their promotional strategies to improve effectiveness and enhance customer satisfaction.
Acceptance Criteria: As a Category Manager, I want to view the engagement metrics for a specific promotional campaign to assess its effectiveness and identify areas for improvement.

Given a selected promotional campaign, when I access the Customer Engagement Metrics dashboard, then I should see the engagement rates including click-through rates, conversion rates, and customer feedback for that campaign.

As a retailer utilizing the Promotion Effectiveness Tracker, I need to analyze the historical engagement metrics to evaluate which promotional strategies have been most successful.

Given a specified time period, when I run a report on historical promotional campaigns, then I should receive a summary of engagement metrics that include a comparison of different campaigns' performance.

As a marketing analyst, I want to receive alerts whenever a promotional campaign's engagement rates fall below a predetermined threshold so that I can take timely action.

Given a promotional campaign has been launched, when the click-through rate or conversion rate drops below the defined threshold, then an alert should be triggered and sent to the marketing analyst's dashboard.

As a stakeholder reviewing promotional performance, I want to see a visual representation of engagement metrics over time to quickly identify trends or anomalies in customer interactions.

Given I am on the Customer Engagement Metrics dashboard, when I select the time range, then I should see a graph depicting engagement metrics over that time period with clear data points for each campaign.

As a Category Manager, I want to be able to filter engagement metrics by different customer segments to understand how various demographics respond to promotions.

Given I am viewing the Customer Engagement Metrics, when I apply filters for specific customer segments, then the metrics displayed should update to reflect only the data for the selected segments.

As a marketer, I need to track the feedback collected from customers regarding promotions to understand their sentiments and further refine my strategies.

Given I have collected customer feedback through surveys, when I access the Customer Engagement Metrics report, then I should see a section summarizing customer feedback with sentiment analysis results.

ROI Calculator

User Story: As a Financial Analyst, I want to calculate the ROI of various promotional activities so that I can determine which promotions provide the best financial return and inform future marketing budgets effectively.
Description: The ROI Calculator is a requirement that provides Category Managers with the ability to calculate the return on investment for each promotional activity executed. This tool will pull in sales data and promotional costs to provide a clear picture of profitability. It will include features to conduct scenario analysis, allowing managers to forecast potential outcomes based on different promotional strategies. The ROI Calculator is essential for decision-making, as it aids in prioritizing future promotions based on their financial effectiveness and strategic alignment with business goals.
Acceptance Criteria: Category Manager uses the ROI Calculator to evaluate the effectiveness of a recent promotional campaign during a strategic planning meeting.

Given that the Category Manager has entered sales data and promotional costs into the ROI Calculator, When they run the calculation, Then they should receive an accurate ROI percentage displayed clearly on the dashboard.

Category Manager conducts scenario analysis using the ROI Calculator to predict the outcomes of various promotional strategies for the upcoming quarter.

Given that the Category Manager selects different promotional scenarios from a pre-defined list, When they adjust the relevant sales and cost variables, Then the ROI Calculator should provide updated projections for each scenario reflecting potential ROI outcomes.

The Category Manager needs to compare the ROI of multiple promotions to guide future marketing strategies.

Given that the Category Manager has multiple promotional activities uploaded into the ROI Calculator, When they run a comparison analysis, Then the system should display a side-by-side ROI comparison for each promotional activity in an easily digestible format.

The Category Manager wants to save and revisit the promotion analysis conducted using the ROI Calculator.

Given that the Category Manager has completed calculations and analysis on the ROI Calculator, When they save the report, Then the system should successfully store the report with a timestamp for future access.

The Category Manager is working remotely and needs to access the ROI Calculator from a different device.

Given that the Category Manager has logged into the BeaconLyte account from a different device, When they access the ROI Calculator, Then they should be able to retrieve their previously saved analysis and reports without data loss.

The Category Manager encounters an error when entering promotional costs into the ROI Calculator for a campaign.

Given that the Category Manager inputs invalid data formats for promotional costs, When they attempt to submit the details, Then the ROI Calculator should display an error message indicating the specific formatting issues that need to be corrected.

The Category Manager requires assistance while using the ROI Calculator for the first time.

Given that the Category Manager is using the ROI Calculator for the first time, When they hover over the help icons present on the interface, Then tooltips should appear providing contextual assistance for each section of the calculator.

Dashboards for Real-time Insights

User Story: As a Category Manager, I want a dashboard that displays real-time data on promotional effectiveness, so that I can quickly identify trends and make informed decisions about future marketing strategies.
Description: This requirement entails developing personalized dashboards that display real-time analytics on promotion effectiveness. These dashboards will allow users to visualize data through charts, graphs, and key performance indicators (KPIs) relevant to promotions. The feature ensures that Category Managers and stakeholders can quickly access and interpret vital information, facilitating timely marketing decisions. It integrates seamlessly with other features such as the Sales Data Analyzer and Customer Engagement Metrics to offer a comprehensive view of promotional performance.
Acceptance Criteria: Real-time Dashboard Visualization for Promotion Effectiveness

Given the user is a Category Manager accessing the dashboard, when they select a specific promotional campaign, then they should see real-time sales data and customer engagement metrics represented through charts and graphs with up-to-date information reflecting the last 24 hours.

Integration Validation with Sales Data Analyzer

Given the dashboard is open, when the user views the promotional performance, then sales data should seamlessly integrate from the Sales Data Analyzer, ensuring that the figures match those presented in the Sales Data Analyzer module.

Filter and Customization Functionality

Given the user is on the promotional effectiveness dashboard, when they apply filters for date range, product category, or promotion type, then the displayed metrics and visualizations should update accordingly to reflect the selected filters accurately.

KPI Alerts and Notifications System

Given the dashboard displays key performance indicators, when a KPI falls below a predefined threshold, then the user should receive a real-time alert via email or in-dashboard notification to take timely action.

Comparative Analysis Feature Availability

Given the promotional effectiveness dashboard is live, when the user selects two or more promotional campaigns, then they should be able to view a side-by-side comparison of metrics such as total sales, ROI, and customer engagement for those campaigns.

User Access Control and Permissions

Given the user is logging into the BeaconLyte platform, when they attempt to access the promotional effectiveness dashboard, then the system should verify their role and permissions, only granting access to authorized Category Managers and stakeholders.

Integration with Existing Data Systems

User Story: As a Data Administrator, I want to ensure the Promotion Effectiveness Tracker integrates with our existing data systems so that we can consolidate analytics and improve the accuracy of our insights, ensuring better strategic decisions.
Description: The Integration with Existing Data Systems requirement focuses on ensuring that the Promotion Effectiveness Tracker can connect and operate with the retailer’s current sales and inventory management systems. This integration will allow for the seamless flow of data, eliminating silos and enhancing the accuracy of analytics generated by the Promotion Effectiveness Tracker. Effective integration is critical as it ensures that analysts and managers have access to consolidated data, which is vital for comprehensive analysis and decision-making.
Acceptance Criteria: Successful integration with existing sales and inventory management systems for real-time data flow.

Given that the Promotion Effectiveness Tracker is deployed, When data is pushed from the sales system, Then all relevant sales data should appear in the Promotion Effectiveness Tracker dashboard within 5 minutes.

Validation of data accuracy post-integration for reliable analytics reporting.

Given that the data integration is complete, When a sales report is generated, Then the report should reflect at least 98% accuracy when compared to the source sales data.

Ability for users to customize data inputs from various existing systems during the initial setup.

Given that a Category Manager is setting up the Promotion Effectiveness Tracker, When they select data sources from existing systems, Then they must be able to customize at least 3 unique data points annually for accurate promotion tracking.

Monitoring of promotional performance through integrated data visualization tools.

Given that the integration with existing data systems is functional, When promotional campaigns are evaluated, Then the system should provide insights on at least 5 key performance indicators (KPIs) in the dashboard.

Error handling during data connections to ensure smooth user experience and reduce downtime.

Given that an error occurs during data synchronization, When this happens, Then the system should alert the user within 1 minute and provide potential resolutions.

Support functionality for exporting integrated data for external reporting purposes.

Given that the Promotion Effectiveness Tracker has been configured, When the user requests a data export, Then the system should allow export in at least 3 formats (e.g., CSV, Excel, PDF) within 2 minutes.

User training effectiveness post-integration regarding the use of integrated data for promotional analysis.

Given that user training has been conducted, When a follow-up quiz is administered, Then at least 85% of users should score above 80% to demonstrate understanding of tracking promotion effectiveness using integrated data.

Customer Sentiment Analysis

Customer Sentiment Analysis harnesses natural language processing to aggregate and analyze customer feedback regarding product categories. By providing insights into customer satisfaction and preferences, this feature enables Category Managers to fine-tune their offerings and respond proactively to consumer sentiments, thereby improving brand loyalty.

Requirements

Sentiment Data Aggregation

User Story: As a Category Manager, I want to aggregate customer feedback into product categories so that I can understand customer sentiment and improve my offerings accordingly.
Description: The Customer Sentiment Analysis feature requires the implementation of a robust natural language processing (NLP) system to aggregate customer feedback from multiple data sources, such as social media, reviews, and surveys. This functionality must effectively categorize feedback into product categories, processing both qualitative and quantitative data. The aggregation process will enhance data accuracy and provide actionable insights, enabling Category Managers to make informed decisions. The expected outcome includes a comprehensive understanding of customer sentiment trends, supporting strategic adjustments in product offerings and marketing strategies.
Acceptance Criteria: Sentiment data is aggregated from multiple sources including social media, customer reviews, and surveys to provide a comprehensive view of customer sentiment towards product categories.

Given sentiment data from social media, reviews, and surveys are collected, when processed by the NLP system, then the data should be accurately categorized into product categories with a minimum accuracy rate of 90%.

Category Managers need to access aggregated sentiment data through the BeaconLyte dashboard to inform their strategic decisions regarding product offerings.

Given that sentiment data is aggregated and categorized, when a Category Manager accesses the dashboard, then they should see an updated sentiment analysis report that includes at least five key metrics related to customer feedback for each product category.

Real-time alerts should notify Category Managers of significant changes in customer sentiment, allowing for timely responses to potential issues or opportunities.

Given that customer sentiment data is aggregated, when the sentiment score for any product category changes by more than 15% in a given week, then an alert should be sent to the Category Manager’s email within 24 hours of the change.

The sentiment analysis feature should handle both qualitative feedback, such as written reviews, and quantitative feedback, such as rating scores, effectively in the aggregation process.

Given a mixture of qualitative and quantitative feedback sources, when the NLP system processes the data, then it should correctly categorize and incorporate both types of feedback into the sentiment analysis without data loss or misclassification.

The aggregated sentiment data should be presented in a visual format within the dashboard to facilitate easy understanding and analysis by Category Managers.

Given that sentiment data is aggregated, when displayed on the dashboard, then it should include visual representation elements such as graphs and heatmaps that accurately reflect sentiment trends and changes for each product category.

User permissions and access controls must be implemented to ensure that only authorized personnel can view or modify the sentiment data.

Given the user roles defined in the system, when a Category Manager attempts to access sentiment data, then the system should enforce permissions such that only those with the appropriate roles can view or edit the data.

The performance of the NLP system in terms of processing speed and resource usage must be assessed to ensure it meets the required standards for real-time analysis.

Given a set of performance benchmarks, when the NLP system processes customer feedback data, then it should complete the aggregation and analysis within 60 seconds for a dataset of 1,000 entries, using no more than 70% of available system resources.

Sentiment Trend Analysis

User Story: As a Category Manager, I want to analyze sentiment trends over time so that I can proactively adapt my strategies to better meet customer needs.
Description: This requirement involves the ability to analyze aggregated sentiment data over time to identify trends and patterns in customer satisfaction and preferences. It will enable the platform to provide visualizations such as graphs or charts that illustrate sentiment changes related to specific products or categories. This functionality is vital for proactive decision-making, allowing Category Managers to anticipate market shifts and adapt strategies in a timely manner. The outcome will contribute to enhanced brand loyalty and better inventory management based on evolving customer preferences.
Acceptance Criteria: Customer Sentiment Trend Visualization Over Time

Given relevant historical customer feedback data, when a Category Manager accesses the Sentiment Trend Analysis feature, then they should be able to view a clear graphical representation of sentiment changes over a selected date range for specific products.

Real-time Alerts for Significant Sentiment Changes

Given that sentiment analysis is continuously updated, when there is a significant change in customer sentiment for a product category, then the system should send an automatic alert to the Category Manager's dashboard.

Comparative Analysis of Product Categories

Given multiple product categories and their sentiment data, when a Category Manager selects two or more categories for comparison, then they should receive a detailed report comparing sentiment trends across the selected categories.

Integration with Existing Dashboard

Given the Category Manager's existing dashboard setup, when the Sentiment Trend Analysis feature is enabled, then the sentiment visualizations should seamlessly integrate and become a part of the existing dashboard without requiring additional configuration.

Exporting Sentiment Reports for External Use

Given the sentiment analysis data on the platform, when a Category Manager chooses to export sentiment reports, then the reports should be downloadable in multiple formats such as PDF and CSV.

User Access and Permissions for Sentiment Analysis

Given the platform's user management structure, when a new user is granted access to the Sentiment Trend Analysis feature, then the system should ensure that their user role allows them to view and analyze sentiment trends according to established permission levels.

Real-time Sentiment Alerts

User Story: As a Category Manager, I want to receive real-time alerts about significant sentiment changes so that I can respond quickly to customer feedback and improve satisfaction.
Description: Implementing a real-time alert system will notify Category Managers of significant changes in customer sentiment for specific product categories. This feature will help managers react promptly to emerging issues or opportunities, enabling them to maintain customer satisfaction and loyalty. The alerts should be customizable, allowing managers to set thresholds for receiving notifications based on their priorities. This requirement is essential for improving responsiveness to customer feedback and enhancing decision-making agility within the retail environment.
Acceptance Criteria: Alert Trigger for Significant Sentiment Drop

Given that a significant drop in customer sentiment is detected for a specific product category, when the sentiment score falls below the predefined threshold set by the Category Manager, then a real-time alert should be sent to the designated managers via email and mobile notification.

Alert Customization Features

Given a Category Manager accessing the alert settings, when they adjust the thresholds for receiving notifications, then the system should save these settings and reflect them in the alert system without requiring a system restart.

Timeliness of Sentiment Alerts

Given that a change in customer sentiment occurs, when the alert is triggered, then the system should notify the Category Managers within 5 minutes of detecting the significant change.

Alert Frequency Control

Given that multiple alerts are generated within a short period, when the same product category is involved, then the system should consolidate these alerts to a single notification to avoid alert fatigue for the Category Managers.

Historical Sentiment Data Access

Given that a Category Manager receives an alert, when they click on the alert notification, then the system should display historical sentiment data for the relevant product category for the past 30 days.

Integration with Dashboard

Given the customization of alerts, when the Category Manager accesses their personalized dashboard, then they should see a widget displaying their current alert settings and sentiment trends for the monitored categories.

Multi-user Notification System

Given a Category Manager sets up alerts for multiple users, when an alert is triggered, then all designated users should receive the notification simultaneously through their preferred channels (email, SMS, in-app notifications).

Sentiment Analysis Dashboard

User Story: As a Category Manager, I want a dashboard that displays sentiment metrics so that I can easily visualize and understand customer sentiment for my product categories.
Description: The requirement entails creating an intuitive, user-friendly dashboard that displays key metrics related to customer sentiment for product categories. The dashboard should allow Category Managers to visualize sentiment data, including overall sentiment scores, trends, and category comparisons. This central hub of information will facilitate faster decision-making by providing essential insights at a glance, reinforcing the integration of sentiment analysis within BeaconLyte’s existing analytics framework. The expected outcome is improved access to information, enabling better strategic planning.
Acceptance Criteria: Dashboard Visualization for Customer Sentiment Data

Given that the Category Manager has accessed the Sentiment Analysis Dashboard, when they select a specific product category, then the dashboard should display the overall sentiment score, trends over the last 30 days, and a comparative analysis with two other categories.

User Interactivity and Customization of the Dashboard

Given that the Category Manager is on the Sentiment Analysis Dashboard, when they interact with the filters for time range and product category, then the dashboard should update in real-time to reflect the selected filters, allowing for custom views of sentiment data.

Real-Time Updates of Sentiment Scores

Given that sentiment data is being collected continuously, when the dashboard is refreshed by the Category Manager, then the dashboard should present the most recent sentiment scores and trends without any lag or data discrepancies.

Accessibility of Sentiment Data Across Different User Roles

Given that various users with different roles access the platform, when a Category Manager views the Sentiment Analysis Dashboard, then they should see metrics specific to their product categories while users in different roles should see accordingly scoped metrics without unauthorized data access.

Intuitive Design and User Experience of the Dashboard

Given that the Sentiment Analysis Dashboard is being used by a new Category Manager, when they first open the dashboard, then they should be able to navigate through the features and metrics without requiring external help, indicating a user-friendly design.

Export Functionality for Sentiment Analysis Data

Given that the Category Manager needs to present sentiment data in a report, when they select the export option on the dashboard, then the dashboard should allow them to export the displayed sentiment data into a .csv or .xlsx format for external use.

Integration with Existing Retail Systems

Given that the Category Manager uses BeaconLyte along with other operational systems, when they access the Sentiment Analysis Dashboard, then the sentiment data must seamlessly integrate with the existing product management or customer relationship management systems, ensuring consistency and eliminating data silos.

Feedback Sentiment Categorization

User Story: As a Category Manager, I want customer feedback to be categorized by sentiment so that I can better understand my customers’ opinions and adjust my strategies accordingly.
Description: This requirement focuses on enhancing the NLP model to accurately categorize feedback into predefined sentiment categories, such as positive, neutral, and negative sentiments. This categorization will facilitate better analysis and understanding of customer opinions regarding various aspects of product categories. It is critical for ensuring the accuracy of insights derived from the sentiment analysis process. Implementing this feature will help Category Managers refine their strategies based on clear and reliable customer feedback, leading to improved product offerings and customer satisfaction.
Acceptance Criteria: Feedback Sentiment Categorization for Product Review Analysis

Given a set of product reviews from customers, when the NLP model is applied, then it should categorize at least 90% of the reviews accurately into positive, neutral, or negative sentiment categories based on predefined keywords and phrases.

Customer Feedback Sentiment Report Generation

Given categorized customer feedback, when a report is generated, then it should display the percentage of feedback in each sentiment category (positive, neutral, negative) with a minimum of 95% accuracy reflected in the dashboard metrics.

Real-Time Feedback Monitoring

Given incoming customer feedback in real-time, when the sentiment categorization algorithm processes this data, then it should categorize feedback with a delay of no more than 2 seconds, ensuring timely insights for category managers.

Feedback Sentiment Trend Analysis

Given categorized sentiment data over a three-month period, when the trend analysis is performed, then it should provide a clear visualization of sentiment changes over time, identifying any significant spikes or drops in customer sentiment.

Multi-Language Feedback Analysis

Given customer feedback provided in different languages, when the NLP model is used, then it should accurately categorize sentiments across at least three major languages (English, Spanish, French) with a 90% accuracy rate.

Category Growth Forecasting

The Category Growth Forecasting tool uses historical sales data and predictive analytics to estimate future category performance. By identifying potential growth opportunities, this feature equips Category Managers with the foresight needed to plan effective strategies that maximize profitability and customer satisfaction.

Requirements

Historical Data Integration

User Story: As a Category Manager, I want to automatically integrate historical sales data from multiple sources so that I can have a comprehensive understanding of past performance when forecasting future category growth.
Description: The Historical Data Integration requirement mandates the automatic collection and integration of historical sales data from various sources, including POS systems and e-commerce platforms. This functionality will ensure that the Category Growth Forecasting tool has access to comprehensive datasets needed for accurate forecasting. The integration should support various data formats and ensure data integrity during transfer. It is essential for establishing a reliable baseline for growth predictions and will enable Category Managers to make informed decisions based on a complete view of past performance.
Acceptance Criteria: Historical Sales Data integration from POS system for Category Growth Forecasting.

Given the system is connected to a legitimate POS source, When the data is requested, Then the system should automatically extract sales data within the last 3 years without human intervention.

E-commerce platform data integration for Category Growth Forecasting.

Given the integration configuration is set correctly, When the e-commerce sales data is fetched, Then all relevant data including transaction details and timestamps should be accurately retrieved and formatted into the database.

Validation of data integrity during historical data transfer.

Given that data is being transferred from multiple sources, When data integrity checks are performed, Then no discrepancies should exist in sales numbers before and after the transfer process, with integrity validation logs maintained for review.

Support for various data formats in historical data integration.

Given the system is configured to accept data uploads, When different formats (CSV, JSON, XML) of historical sales records are uploaded, Then the system should accurately parse and incorporate the data without errors or loss of information.

Settings for historical data integration should be configurable by users.

Given that users have access to the integration settings, When a user modifies the data sources or formats for integration, Then the changes should reflect immediately in the forecasting tool without requiring a system restart.

Real-time alerting for historical data integration failures.

Given the historical data integration process is operational, When a failure occurs during data integration, Then a real-time alert should be sent to the designated users via email and an in-app notification.

Comprehensive logging of historical data integration activities.

Given that data integration activities are processing, When logging is enabled, Then the system should record all integration events, including successful uploads, errors encountered, and timestamps for each event.

Predictive Analytics Algorithm

User Story: As a Category Manager, I want a predictive analytics algorithm that analyzes past sales data to identify trends so that I can forecast future category growth accurately and plan my strategies accordingly.
Description: The Predictive Analytics Algorithm requirement focuses on developing a robust algorithm that analyzes historical sales data and identifies trends, patterns, and anomalies that could impact future category performance. This algorithm should incorporate machine learning techniques to continuously improve its predictions based on new incoming data. The successful implementation of this algorithm will enhance the accuracy of category growth forecasts and equip managers with actionable insights to optimize inventory and marketing strategies.
Acceptance Criteria: Category Manager inputs historical sales data to the Predictive Analytics Algorithm and requests a forecast for the next quarter.

Given the historical sales data is correctly uploaded, when the forecast request is initiated, then the system should return category growth forecasts with a minimum accuracy of 85% based on historical trends.

The Predictive Analytics Algorithm processes real-time data and adjusts the growth forecasts accordingly.

Given real-time sales data is available, when the algorithm processes the data, then it should update the growth forecasts within 30 minutes and flag significant changes to the Category Manager.

A Category Manager reviews the output of the Predictive Analytics Algorithm to determine action steps for inventory optimization.

Given the growth forecast is generated and accessible, when the Category Manager reviews the forecast, then actionable insights must be presented that are easily understandable and prioritized for decision-making.

The Predictive Analytics Algorithm identifies anomalies in historical sales data that could impact forecasts.

Given historical sales data is analyzed, when an anomaly is detected, then the system should alert the Category Manager with a detailed report of the anomaly and suggested adjustments to the forecasts.

Integration of the Predictive Analytics Algorithm with the existing inventory management system for real-time decision support.

Given the inventory management system and the Predictive Analytics Algorithm are integrated, when a forecast is generated, then the inventory system should automatically suggest stock adjustments based on predicted category performance.

Category Managers receive training on utilizing the Predictive Analytics Algorithm effectively.

Given a training session is conducted, when Category Managers complete the training, then they should demonstrate proficiency in generating forecasts and interpreting results through a practical assessment.

Ongoing evaluation and feedback loop for improving the Predictive Analytics Algorithm.

Given a cycle of continuous feedback has been established, when new sales data is processed, then the algorithm should adapt its predictions accordingly, demonstrating improved accuracy over consecutive quarters by at least 5%.

Customizable Dashboard

User Story: As a Category Manager, I want to customize my dashboard to focus on key metrics and visualizations that are relevant to my category so that I can efficiently track performance and make informed decisions.
Description: The Customizable Dashboard requirement enables Category Managers to create personalized views of the growth forecasts with key metrics and visualizations relevant to their specific categories. Users should be able to select from various data visualizations, such as graphs, charts, and tables, and arrange them according to their preferences. This highly customizable interface will promote deeper insights into category performance and allow for tailored reporting, enhancing user engagement and decision-making efficiency.
Acceptance Criteria: Category Manager wants to arrange their dashboard to prioritize the most relevant growth forecasts for the upcoming quarter, including sales trends and customer engagement metrics, to make strategic decisions quickly.

Given a Category Manager is on the Customizable Dashboard, when they select and arrange at least three different data visualizations (graphs, charts, or tables) specific to their category preferences, then the dashboard should reflect these changes in real-time without any delays.

A Category Manager accesses the dashboard to generate insights from the category growth forecasts and intends to export the customized view as a report for team sharing and discussion.

Given the Category Manager has customized their dashboard, when they choose to export the view to a PDF or Excel file, then the export functionality should successfully create a file that accurately reflects the current dashboard layout and data visualizations.

Category Managers need to compare growth forecasts across different time periods to identify trends and adjust strategies accordingly.

Given a Category Manager is viewing their Customizable Dashboard, when they select different time periods for comparison (e.g., last month vs. last quarter), then the dashboard should update to display the relevant growth forecast data side by side with clear visual differentiation.

A Category Manager sets their dashboard preferences for automatic updates of the growth forecasts every week to ensure they are seeing the most current data.

Given that the Category Manager has set the dashboard for weekly updates, when a new week begins, then the dashboard should automatically refresh to present the latest data without manual intervention.

A Category Manager is utilizing the dashboard to monitor category performance and wants to utilize filtering options to isolate data from specific product lines or promotions to refine insights.

Given the Category Manager is using the Customizable Dashboard, when they apply filters to view data for a specific product line, then the dashboard should only display data and visualizations relevant to the selected filter criteria.

Real-time Alerts for Growth Opportunities

User Story: As a Category Manager, I want to receive real-time alerts when significant growth opportunities are identified so that I can act quickly and capitalize on potential profit drivers.
Description: The Real-time Alerts for Growth Opportunities requirement establishes a notification system that alerts Category Managers when significant growth opportunities are identified based on the predictive analytics outcomes. This includes alerts for unexpected increases in demand, changes in customer behavior, or emerging market trends that could impact category performance. Implementing this feature will ensure that managers can respond swiftly to opportunities, thereby enhancing their ability to capitalize on market dynamics.
Acceptance Criteria: Receiving Alerts for Increased Demand in High-Performing Categories

Given that the predictive analytics have identified an unexpected increase in demand in a high-performing category, when a Category Manager logs into BeaconLyte, then they should receive a real-time alert notification detailing the specific product and the percentage increase in demand for that category.

Notifications for Changes in Customer Behavior

Given that the predictive analytics detects a significant change in customer behavior that may affect category performance, when the change is logged, then an alert should be sent to the Category Manager within one minute, summarizing the key changes and potential impacts on sales performance.

Alerts for Emerging Market Trends Impacting Sales

Given that there is an emerging market trend identified through data analysis, when the trend is confirmed, then the Category Manager should receive an alert with actionable insights within ten minutes, including suggested strategies for leveraging the trend.

Threshold-Based Alerts for Sales Performance Changes

Given that sales performance data shows a drop below a predefined threshold, when this condition is met, then a real-time alert should be generated and sent to the Category Manager, including the specific sales figures and a recommendation for immediate action.

Confirmation of Alert Receipt by Category Managers

Given that an alert has been generated, when a Category Manager receives the alert notification, then there should be an acknowledgment feature that allows them to confirm receipt of the alert, which is logged in the system.

Customization of Alert Settings by Category Managers

Given that a Category Manager wants to tailor their notification preferences, when they access their settings in BeaconLyte, then they should be able to customize alert thresholds, notification types, and channels (email, SMS, in-app) without any issues.

Test Alerts for System Functionality Check

Given that the development team wants to ensure the alert system is functioning correctly, when a test alert is triggered during system testing, then the alert should appear accurately in the designated notification area within five seconds, confirming system reliability.

Reporting and Export Functionality

User Story: As a Category Manager, I want to generate and export reports from the forecasting tool so that I can share insights and collaborate with my team on category strategies efficiently.
Description: The Reporting and Export Functionality requirement allows users to generate reports based on the growth forecasting insights and export them in various formats such as PDF, Excel, or CSV. This feature is vital for enabling Category Managers to share insights with stakeholders and collaborate on strategy development. A user-friendly interface should facilitate easy customization of report content, format, and distribution options, streamlining the reporting process and enhancing team collaboration.
Acceptance Criteria: Category Manager generating a weekly performance report to evaluate category growth based on predictive data.

Given that the Category Manager has selected the 'Weekly Performance Report' template, When they click on the 'Generate Report' button, Then the system should produce a report that includes graphs and tables displaying sales data, growth predictions, and trends for the last 4 weeks.

Category Manager exporting a report to share with senior management for strategic discussions.

Given that the Category Manager has finalized the report, When they select the 'Export' option and choose 'PDF' format, Then the system should create a PDF file that retains all original formatting and is downloadable without errors.

Category Manager customizing the content of a report before export.

Given that the Category Manager is on the report customization screen, When they select or deselect specific metrics and modify the title, Then the system should update the report preview in real-time without glitches and save the changes for exporting.

User collaborating with team members by sharing a report via email directly from the reporting tool.

Given that the Category Manager has selected a report and clicked on 'Share via Email', When they enter the recipient's email address and hit 'Send', Then the system should deliver the report to the specified email address without errors and confirm the action to the user.

Category Manager filtering the data in a report based on specific criteria prior to export.

Given that the Category Manager is in the report generation interface, When they apply filters for 'Category A' and 'Last Month' sales, Then only the data relevant to those filters should display in the report generation preview before exporting.

Ensuring that reports generated can be accessed by authorized users only.

Given that a report has been generated, When another user who lacks authorization attempts to access it, Then they should receive a '403 Forbidden' error message indicating insufficient permissions to view the report.

User attempting to generate a report with incomplete data fields.

Given that the Category Manager has not filled required fields in the report generation form, When they click on the 'Generate Report' button, Then the system should prompt an error message highlighting the missing fields and prevent the report from being generated until completed.

User Training and Support Resources

User Story: As a Category Manager, I want access to training materials and support resources for the forecasting tool so that I can learn how to use it effectively and optimize my category strategies.
Description: The User Training and Support Resources requirement focuses on providing comprehensive onboarding and training materials for Category Managers who will use the Category Growth Forecasting tool. This includes video tutorials, user manuals, and interactive training sessions that cover the tool's features and best practices. Proper training will ensure users can leverage the tool effectively, enhancing their ability to make data-driven decisions and improving overall adoption rates.
Acceptance Criteria: Category Manager initiates the user onboarding process for the Category Growth Forecasting tool by accessing the training materials.

Given the Category Manager has logged into BeaconLyte, When they navigate to the User Training section, Then they should see a list of at least 5 video tutorials, 3 user manuals, and an option to schedule interactive training sessions.

A Category Manager participates in an interactive training session about the Category Growth Forecasting tool.

Given the Category Manager attends the training session, When the session concludes, Then they should receive a feedback form, and at least 90% of participants rate the session as satisfactory or higher.

A Category Manager utilizes the user manuals available for the Category Growth Forecasting tool during their first week of usage.

Given the Category Manager accesses the user manuals, When they refer to the relevant sections for guidance, Then they should complete at least 3 key tasks in the tool without requiring additional assistance from support resources.

The support resources are assessed for comprehensiveness and relevance for Category Managers using the tool.

Given the support materials are reviewed by a team of Category Managers, When they evaluate the content, Then at least 85% of them should confirm that the materials meet their needs and expectations for onboarding and usage of the tool.

A Category Manager downloads and reviews a user manual from the training resources section.

Given the Category Manager selects the user manual for the Category Growth Forecasting tool, When they open the document, Then it must contain clear instructions and examples on at least 10 different features of the tool.

Compliance with data security during the onboarding training of the Category Growth Forecasting tool is evaluated.

Given the Category Manager is accessing the training resources, When they submit their personal data for registration, Then the system must ensure data encryption and comply with GDPR guidelines without any security incidents.

The effectiveness of the training materials is measured after a month post-training.

Given the Category Managers have completed their training, When their adaptation to the tool is evaluated through performance metrics, Then at least 75% of them should demonstrate improved decision-making capabilities, reflected in their ability to generate accurate forecasts compared to their previous methods.

Interactive Visual Dashboards

Interactive Visual Dashboards transform raw data into engaging, intuitive visualizations that highlight key performance metrics for each product category. This user-friendly feature enhances decision-making by allowing Category Managers to drill down into data for deeper insights, facilitating strategic initiatives through clear, accessible information.

Requirements

Dynamic Filtering Options

User Story: As a Category Manager, I want to apply dynamic filters to the visual dashboard so that I can view specific data relevant to my product category and make informed decisions.
Description: The Dynamic Filtering Options requirement allows users to customize the data displayed on their dashboards by applying various filters based on date ranges, product categories, and performance metrics. This functionality enhances user experience by enabling Category Managers to focus on the most relevant data for their strategic initiatives. The implementation will involve integrating a user-friendly interface that provides real-time filtering capabilities, ensuring quick access to the desired insights without overwhelming users with unnecessary information. The outcome is an empowered user who can make informed decisions based on specific subsets of data, ultimately driving better business results.
Acceptance Criteria: Category Managers need to filter their dashboard data by specific product categories to assess performance metrics related to sales and inventory levels during a quarterly review meeting.

Given I am on the Interactive Visual Dashboard, when I apply a filter for 'Electronics' as the product category, then the dashboard should update to show only the data associated with Electronics products.

A Category Manager wants to analyze sales performance over a specific period to determine the effectiveness of a recent marketing campaign.

Given I am viewing the dashboard, when I set a date range filter from '2024-01-01' to '2024-03-31', then the dashboard should reflect only the sales data for that specified date range.

The Category Manager is interested in comparing the performance metrics of different product categories to identify which category is underperforming.

Given I have selected multiple product categories, when I apply the comparison filter, then the dashboard must display a side-by-side comparison of the performance metrics of each selected category.

During a strategy meeting, a Category Manager wishes to view customer satisfaction metrics for specific products to align better with consumer preferences.

Given I have applied a filter for customer satisfaction metrics and specific product categories, when I review the dashboard, then only the metrics relevant to the selected products must be shown.

A Category Manager regularly reviews inventory turnover rates to make informed restocking decisions and to minimize excess inventory.

Given I apply filters for inventory turnover rates, when I check the dashboard, then it should present only the data pertaining to products with turnover rates below the selected threshold.

The Category Manager needs to reset all applied filters to start a new analysis session without refreshing the page.

Given I am on the Interactive Visual Dashboard with filters applied, when I click on the 'Reset Filters' button, then all filters should return to their default states, displaying all available data.

The Category Manager wants to track the success of different promotions over various time frames to refine their strategies.

Given I have selected a specific promotional filter, when I apply a date range, then the dashboard must display only the sales data related to the selected promotion within that date range.

Exportable Reports Feature

User Story: As a Category Manager, I want to export dashboard data into reports so that I can share insights with my team and stakeholders easily.
Description: The Exportable Reports Feature allows users to download selected dashboard data as reports in various formats such as PDF and Excel. This requirement is key for Category Managers who need to share insights and findings with stakeholders or incorporate the data into presentations. The implementation will focus on ensuring that the report generation process is seamless and that the exported data maintains its visual integrity and accuracy. The anticipated outcome is a streamlined reporting process that enhances collaboration and decision-making across teams.
Acceptance Criteria: Category Managers generate a report from the dashboard to share insights with executives after a weekly sales review meeting.

Given the user is logged into BeaconLyte and has navigated to the Interactive Visual Dashboard, when they select the 'Export' option and choose PDF or Excel format, then the report should be generated successfully without errors and downloaded to the user's device.

A Category Manager needs to ensure that the report reflects accurate data post any dashboard updates, especially after new inventory items have been added.

Given the user exports the report immediately after updates are made to the dashboard, when they compare the exported report with the dashboard metrics, then the report must accurately reflect the most current data and visual integrity.

Monthly performance reviews require that reports are shared seamlessly with teams and stakeholders for collaborative discussions.

Given the user has generated a report in PDF format, when they share the report via email or upload it to a project management tool, then stakeholders should be able to access and open the report without any compatibility issues or loss of formatting.

A Category Manager wants to generate a report to analyze seasonal trends for the upcoming holiday period.

Given the user selects specific date ranges and product categories before exporting the report, when they click 'Generate', then the report produced should only include the selected data set and maintain clarity and organization in its presentation.

Before releasing the report, a Category Manager needs to preview the document to verify that all information is accurately displayed.

Given the user initiates the report export, when they select the 'Preview' option before final download, then the preview must accurately display all key metrics and visual elements as they will appear in the final report.

Users need to understand how to use the export feature effectively, especially with new functionalities being added.

Given that the user is unfamiliar with the export functionality, when they access the help guide or tutorial section related to report generation, then they should find comprehensive instructions and visual aids that clearly explain how to use the export feature effectively.

A Category Manager needs to ensure that reports exported maintain compliance with data privacy regulations when sharing with external stakeholders.

Given that the user exports a report containing sensitive data, when they generate the report, then the system must prompt the user to confirm compliance with data sharing policies and ensure that any sensitive information is appropriately handled or excluded from the export.

Real-Time Data Refresh

User Story: As a Category Manager, I want the dashboard to refresh in real time so that I always have access to the latest performance data for my products.
Description: The Real-Time Data Refresh requirement ensures that the data displayed on the interactive dashboards is consistently updated in real-time, providing users with the latest insights. This functionality is critical as it allows Category Managers to monitor performance metrics and make timely decisions based on current data rather than outdated information. The implementation will involve integrating a robust data pipeline and notifications to alert users when new data is available. The expected outcome is improved accuracy in decision-making and a more responsive user experience.
Acceptance Criteria: Real-time Data Updates During Peak Hours

Given the dashboard is in use during peak business hours, when new data is received, then the dashboard should refresh within 5 seconds to reflect the latest performance metrics without manual intervention.

User Notification for Data Updates

Given that new data has been integrated into the system, when the data refresh occurs, then all active users should receive a notification alerting them that updated data is available for view.

Data Integrity on Refresh

Given that the dashboard is displaying data, when the data is refreshed in real-time, then the accuracy of the updated metrics should be verified against the source database to ensure no discrepancies greater than 1% exist.

Performance Impact Assessment

Given that the dashboard features real-time data refresh, when multiple users access the dashboard simultaneously, then the system should maintain performance levels with a response time of less than 2 seconds for all users.

User Control Over Refresh Rate

Given that a user accesses the dashboard, when they adjust the settings, then they should have the option to set the data refresh rate to intervals of 5, 10, or 15 minutes, and the dashboard should reflect this change immediately.

Historical Data Comparison

Given that new data is available, when refreshing the dashboard, then users should have the option to view historical data trends alongside the real-time metrics to facilitate comparative analysis.

Error Handling for Data Refresh Failures

Given that a data refresh attempt fails due to connectivity issues, when the error occurs, then users should be notified with a clear error message and the system should attempt to refresh the data again after 1 minute.

User Access Control

User Story: As an Administrator, I want to control user access levels so that I can protect sensitive data and ensure compliance.
Description: The User Access Control requirement provides the ability to define and manage access levels for different users within the platform. This ensures that sensitive data is only accessible to authorized personnel, enhancing security and compliance. The implementation will allow administrators to set permissions based on roles (e.g., Category Managers, Analysts, Executives), ensuring that each user has the appropriate level of access. The expected outcome is a secure platform that maintains data integrity and protects user information, fostering trust in the application.
Acceptance Criteria: Category Manager Attempts to Access Monthly Sales Data.

Given a Category Manager role, When they log into the BeaconLyte platform, Then they should have access to the monthly sales analytics dashboard and be able to view all pertinent data related to their assigned product categories.

Analyst Tries to Access HR-Related Analyses.

Given an Analyst role, When they attempt to access the HR analytics dashboard, Then they should receive an access denied message indicating that this data is not within their role permissions.

Executive Requests to View All Product Performance Metrics.

Given an Executive role, When they log into the BeaconLyte platform, Then they should have access to all product performance metrics across different categories without restrictions.

Administrator Modifies User Permissions for Category Manager.

Given an Administrator role, When they change the access level of a user from Category Manager to Analyst, Then that user should no longer have access to the dashboards and data exclusive to Category Managers.

User Attempts to Access Dashboard after Permission Change.

Given a user whose permissions have been changed, When they log into the BeaconLyte platform, Then they should see the updated permissions reflected in their access, limiting them to their new role's capabilities.

User Access Log is Checked for Security Compliance.

Given the security team, When they review the user access log, Then they should be able to see a complete history of access attempts, including successful and denied logins, for compliance validation.

User Profile Update to Change Role.

Given the need to update user profiles, When an Administrator changes a user's role, Then the system should immediately reflect the role change and update their access rights accordingly, ensuring they have the correct permissions.

Interactive Data Annotations

User Story: As a Category Manager, I want to add annotations to data points on the dashboard so that I can share insights with my team directly within the context of the data.
Description: The Interactive Data Annotations requirement enables users to add comments or notes directly onto specific data points within the visual dashboards. This functionality allows Category Managers to annotate insights or share observations with team members, enhancing collaborative analysis. The implementation will focus on user-friendly annotation tools that maintain clarity and organization. The expected outcome is improved communication and collaboration among team members through easily accessible and contextual commentary within the dashboards.
Acceptance Criteria: Category Managers are reviewing product performance in the interactive visual dashboards during the weekly team meeting. They identify trends and insights and want to annotate specific data points to share observations and notes with their team members.

Given a data point on the visual dashboard, when a Category Manager clicks on the point, then they should see an option to add an interactive annotation that includes a text box for comments and the ability to tag other team members.

A Category Manager wants to annotate a data point about decreased sales in a specific product category. They need to ensure that their annotation is saved and accessible to other team members in future sessions.

Given that a Category Manager has entered an annotation on a data point, when they save the annotation, then the annotation should be stored in the system and retrievable by any user with access to that dashboard.

During a collaborative analysis session, multiple Category Managers are accessing the visual dashboards simultaneously to review customer feedback and sales data. They want to see each other's annotations to enhance their discussions and decision-making processes.

Given multiple annotations made by different Category Managers, when they are viewing the same data point, then all annotations should be displayed in a way that clearly identifies the contributor and the timestamp of the annotation.

A Category Manager realizes they need to modify a previously made annotation after receiving feedback from another team member. They want to ensure that the changes are preserved and that the team is notified of the update.

Given an existing annotation, when a Category Manager edits the annotation and saves the change, then the updated annotation should reflect the changes and notify relevant team members of the updated information.

To avoid clutter and maintain clarity, Category Managers need to delete unnecessary annotations that are no longer relevant to the data insights.

Given an existing annotation, when a Category Manager selects the delete option, then the annotation should be removed permanently from the dashboard, and confirmation of deletion should be provided.

In preparation for a quarterly review, the Category Manager wants to ensure that all annotations related to a specific campaign are aggregated and summarized for presentation purposes.

Given multiple annotations associated with a campaign, when the Category Manager accesses the summary feature, then they should see a consolidated view of all annotations related to that campaign, sorted by date and contributor.

Lifecycle Performance Insights

Lifecycle Performance Insights provide an overview of product lifecycles within each category, allowing Category Managers to assess the performance of new versus established products. This feature aids in identifying trends such as product seasonality and lifecycle stages, supporting better product management and strategy development.

Requirements

Real-time Data Visualization

User Story: As a Category Manager, I want to see real-time visualizations of product lifecycle performance so that I can make quick decisions based on the most current data.
Description: The Real-time Data Visualization requirement mandates the development of dynamic graphs and charts that update in real time to reflect current product lifecycle performance. This functionality will allow Category Managers to instantly view metrics such as sales trends, inventory levels, and stock turnover rates for both new and established products. By incorporating filters and customizable views, users can drill down into specific periods or categories, enabling more informed decision-making. This feature is crucial for quickly identifying underperforming products or assessing the success of newly launched items, thus fostering a proactive approach to inventory and product management.
Acceptance Criteria: Category Manager views the real-time dashboard to analyze the current sales performance of newly launched products during a promotional period.

Given that the Category Manager accesses the dashboard, When they select the 'New Products' filter and the specified promotional date range, Then the dashboard should display dynamic graphs showing sales trends, inventory levels, and stock turnover rates for the selected period.

Category Manager uses the real-time data visualization feature to assess the inventory levels of established products prior to a seasonal sale.

Given that the Category Manager is viewing established products, When they apply the 'Established Products' filter and select the last month as the timeframe, Then the dashboard should update to show accurate and current inventory levels, as well as historical stock turnover rates.

Category Manager monitors the dashboard to identify underperforming products to address them in the next strategy meeting.

Given that the Category Manager is on the dashboard, When they analyze the metrics for products showing below the average sales trend, Then the system should highlight these products and provide a suggestion for potential actions.

Category Manager wants to track product performance over time to identify seasonal trends.

Given that a Category Manager wants to observe seasonal trends, When they select a multi-period filter covering the last three years, Then the dashboard should display cumulative sales data across the specified periods in a comparative graph format.

Category Manager customizes their dashboard view to focus on the performance metrics relevant to a specific product category.

Given that the Category Manager is customizing their dashboard, When they select a specific product category and save the settings, Then the dashboard should reflect the new customized views with real-time updates on the specified metrics for that category.

Category Manager tests the real-time data visualization feature during peak business hours to verify performance under load.

Given that the Category Manager is using the dashboard during peak hours, When they analyze multiple data points simultaneously, Then the dashboard should continuously update without lag or delay, ensuring all data reflects real-time conditions.

Predictive Lifecycle Analysis

User Story: As a Category Manager, I want predictive analytics on product lifecycle trends so that I can plan inventory and marketing strategies more effectively.
Description: The Predictive Lifecycle Analysis requirement involves integrating AI-driven models that analyze historical sales data to forecast future performance of products at various lifecycle stages. This predictive capability will provide insights into expected seasonal trends, potential stock shortages, and optimal timing for new product launches. By comparing predicted data with actual performance, Category Managers can refine their inventory strategies and customer engagement initiatives. This feature aims to enhance the strategic planning process, ensuring that retailers remain competitive and responsive to market demands.
Acceptance Criteria: Category Manager analyzes product performance during the back-to-school season using predictive lifecycle analysis.

Given historical sales data from the previous back-to-school season, when the Category Manager inputs the data into the Predictive Lifecycle Analysis tool, then the system should generate accurate forecasts of expected sales trends, potential stock shortages, and optimal launch timings for new school supplies.

Category Manager identifies underperforming products and adjusts inventory accordingly using insights from predictive analysis.

Given the predictive analysis output showing underperforming products, when the Category Manager reviews the recommendations for stock adjustments, then they should be able to see actionable insights that suggest specific quantities to reorder or discontinue based on forecasted demand.

Category Manager compares predicted data with actual sales performance post-launch of new products.

Given a newly launched product, when the Category Manager analyzes the predictive sales forecast against actual sales data after three months, then the system should display a variance report highlighting discrepancies and providing insights on potential causes for any deviations.

Category Manager prepares a seasonal report using lifecycle insights for quarterly strategy meetings.

Given the lifecycle performance insights generated by the predictive analysis tool, when the Category Manager creates a report that includes trends, expected sales, and stock levels, then the report should accurately reflect all relevant data in a clear and presentable format for stakeholders.

Category Manager monitors real-time alerts for stock shortages based on predictive analysis.

Given the predictive analysis of product lifecycles, when a stock level for a critical product falls below the predefined threshold, then the system should automatically notify the Category Manager in real-time to prompt immediate decision-making for reordering.

Category Manager reviews historical data to validate predictive lifecycle outcomes.

Given a collection of historical sales data for various products, when the Category Manager accesses the validation tool within the Predictive Lifecycle Analysis, then they should be able to compare historical performance with predictions to assess accuracy and refine future forecasts.

Seasonality Trend Dashboard

User Story: As a Category Manager, I want a dashboard that shows seasonal trends in sales so that I can align my product buying and marketing efforts with customer behavior.
Description: The Seasonality Trend Dashboard requirement entails creating a dedicated section within the platform that highlights seasonal trends across all product categories. This dashboard will aggregate data on sales patterns, customer preferences, and inventory turnover, visualizing this information to show clear seasonal spikes or declines. By enabling Category Managers to identify these trends, the dashboard will support timely adjustments in marketing campaigns and inventory allocations, optimizing overall product performance and ensuring alignment with consumer demand.
Acceptance Criteria: Category Managers need to access the Seasonality Trend Dashboard at the beginning of each season to adjust marketing and inventory strategies based on historical sales data and trends.

Given a Category Manager accesses the Seasonality Trend Dashboard, when the dashboard loads, then it should display sales patterns, customer preferences, and inventory turnover data for the past three seasons, with clear visualizations of seasonal spikes and declines.

A Category Manager wants to analyze the seasonal trends of a newly launched product within a specific category to make informed decisions about future inventory purchases.

Given the Seasonality Trend Dashboard is accessible, when the Category Manager selects a new product from the dropdown menu, then the dashboard should update to show the seasonality trends for that product, including comparative data against established products in the same category.

During an end-of-season review, a Category Manager uses the Seasonality Trend Dashboard to evaluate the performance of various products to inform next season’s inventory strategy.

Given the dashboard is loaded with data at the end of the season, when the Category Manager selects the 'End of Season' filter, then the dashboard should present summarized reports of product performance, including total sales, average inventory levels, and customer feedback ratings for each product.

A Category Manager needs real-time alerts for inventory levels that drop below a predetermined threshold based on seasonal trends to prevent stockouts.

Given the Seasonality Trend Dashboard is active, when inventory levels for any product fall below the specified threshold, then the system should trigger an alert notification to the Category Manager via email or in-app notification during the dashboard session.

The marketing team collaborates with Category Managers to refine promotional campaigns based on insights from the Seasonality Trend Dashboard.

Given the collaborative access to the Seasonality Trend Dashboard, when the marketing team reviews the dashboard, then they should see direct insights and recommendations based on current trends, including recommended promotional strategies tied to identified seasonal opportunities for each category.

Lifecycle Stage Mapping

User Story: As a Category Manager, I want to map products to their lifecycle stages so that I can prioritize my focus and resources on improving product performance effectively.
Description: The Lifecycle Stage Mapping requirement focuses on the development of a feature that categorizes products into distinct lifecycle stages (Introduction, Growth, Maturity, Decline) based on real-time performance indicators. This functionality will help Category Managers quickly assess at a glance where each product stands within its lifecycle, allowing for strategic planning and marketing adjustments. By mapping products effectively, managers can foster proactive decisions to boost performance for lagging products or prepare for phase-out strategies.
Acceptance Criteria: Lifecycle Stage Mapping for Product Assessment

Given a product in the system, When the product's performance metrics are analyzed, Then the product is accurately categorized into one of the lifecycle stages (Introduction, Growth, Maturity, Decline) based on predefined indicators such as sales volume, customer feedback, and inventory levels.

Real-time Lifecycle Stage Updates

Given a product's performance data changes, When the data is updated in the system, Then the lifecycle stage of the product is automatically recalculated and updated in the dashboard within 2 minutes.

Lifecycle Stage Visualization on Dashboard

Given a Category Manager accessing the dashboard, When they view the Lifecycle Performance Insights section, Then they can see all products color-coded according to their lifecycle stages for easy visual identification.

Historic Performance Comparison

Given multiple products within the same category, When a Category Manager requests a lifecycle performance report, Then the report should display historic performance trends and comparisons between new and established products for the last two fiscal years.

Alerts for Product Lifecycle Changes

Given a product that transitions from Growth to Maturity, When this transition occurs, Then the system sends an automated alert to the Category Manager with recommended actions for marketing adjustments and inventory management.

User Access for Lifecycle Stage Editing

Given a Category Manager, When they want to edit a product's lifecycle stage manually, Then they should have permissions to do so only if the product's lifecycle has not been automatically updated within the last 24 hours.

Performance Indicator Definitions

Given a product in the system, When the lifecycle stages are being defined, Then the definitions and metrics used for categorizing products into lifecycle stages must be clear and documented for all users within the platform.

Custom Alerts for Lifecycle Changes

User Story: As a Category Manager, I want custom alerts for significant lifecycle changes so that I can take timely action on my product portfolio.
Description: The Custom Alerts for Lifecycle Changes requirement includes the implementation of a notification system that alerts Category Managers when products transition between lifecycle stages or when performance metrics reach predefined thresholds. This feature will enhance responsiveness by ensuring that managers are immediately informed of significant changes, enabling them to react promptly and implement necessary strategies. The alerts can be customized in frequency and relevance, allowing managers to set priorities based on their current focus areas.
Acceptance Criteria: Category Manager receives alerts on product lifecycle transitions.

Given a product transitions from 'Introduction' to 'Growth' stage, When the transition occurs, Then the Category Manager receives a real-time alert via email and in-app notification.

Category Manager customizes alert preferences for product metrics.

Given a Category Manager accesses the alert settings, When they select specific metrics and frequency options, Then the system saves these preferences for future alerts accurately.

Category Manager receives alerts based on predefined performance thresholds.

Given a product's sales performance metric reaches its predefined threshold, When this event occurs, Then the Category Manager receives an immediate notification detailing the performance metrics and suggested actions.

Category Manager acknowledges receipt of alerts.

Given the Category Manager receives an alert, When they view and acknowledge the alert, Then the alert status updates to 'Acknowledged' and is logged in the system for future reference.

Category Manager filters alerts based on urgency and product categories.

Given the alert interface, When the Category Manager applies filters for urgency and specific product categories, Then the displayed alerts accurately reflect the applied filters and are sorted accordingly.

Category Manager accesses historical alert data.

Given a Category Manager wants to review past alerts, When they navigate to the historical alerts section, Then they can view the full details of previous alerts, including timestamps and product details.

Dynamic Price Adjuster

Dynamic Price Adjuster automatically modifies product prices based on real-time analysis of market trends and competitor pricing. By utilizing AI algorithms, this feature ensures retail prices remain competitive and aligned with customer demand, optimizing profit margins while preventing potential loss of sales due to pricing discrepancies.

Requirements

Real-Time Market Analysis

User Story: As a retail manager, I want to automatically adjust product prices based on real-time competitor pricing, so that I can stay competitive and maximize my profit margins.
Description: The Real-Time Market Analysis requirement enables the Dynamic Price Adjuster to continuously analyze competitor pricing and market trends. This involves integrating external market data streams and internal sales data to provide timely insights. The benefit of this capability is that it empowers retailers to adjust their pricing strategy dynamically based on live market conditions, ensuring they remain competitive. This integration supports the overall goal of maximizing profitability while aligning with customer expectations, clearly positioning BeaconLyte as an essential tool in the retailer's decision-making process.
Acceptance Criteria: Analyze and Adapt Pricing Automatically Based on Competitor Activity

Given that the Dynamic Price Adjuster is active, when competitor prices change in real time, then the system should automatically adjust product prices within 5 minutes to align with the new market conditions, without manual intervention.

Real-Time Data Integration for Market Analysis

Given that external market data streams and internal sales data are available, when the system processes these inputs, then it should provide a dynamic report highlighting at least three pricing strategies every hour based on the latest trends.

Impact Assessment of Price Adjustments on Sales Performance

Given that a price adjustment has been made by the Dynamic Price Adjuster, when a comparison is made between sales data before and after the adjustment, then there should be a measurable increase in sales volume of at least 10% within the first week post-adjustment.

User Notification for Significant Price Changes

Given that the Dynamic Price Adjuster adjusts prices by more than 15%, when the adjustment occurs, then the system should send an automated notification to the retail manager within 1 hour of the change.

Historical Price Change Tracking

Given ongoing price changes, when a retailer requests a history of price adjustments, then the system should provide a complete log of price changes for the last 30 days, including timestamps and reasons for adjustments.

Competitor Price Comparison for Similar Products

Given that there are competitor products with similar features, when a retailer queries for competitor prices, then the system should return a comparative analysis showing all similar products and their current pricing trends within 2 minutes.

User-Controlled Pricing Floors and Ceilings

Given that the retailer has set price floors and ceilings for products, when the Dynamic Price Adjuster attempts to modify prices, then it should not exceed the defined limits, thereby preserving the retailer's pricing strategy.

AI-Driven Pricing Algorithms

User Story: As a pricing analyst, I want AI algorithms to help determine pricing adjustments, so that I can make data-driven decisions and improve our revenue.
Description: The AI-Driven Pricing Algorithms requirement centers around developing intelligent algorithms that evaluate numerous factors such as sales velocity, customer behavior, and competitor pricing. These algorithms will determine optimal price adjustments in real time, based on predictive analytics. Incorporating this requirement enhances the functionality of the Dynamic Price Adjuster, ensuring that price changes are not arbitrary but are strategically informed by data, thus optimizing sales and customer engagement.
Acceptance Criteria: AI-Powered Price Adjustments Based on Market Conditions

Given that the algorithm has access to real-time market data and competitor pricing, when there is a significant change in competitor price within a 10% threshold, then the pricing algorithm should adjust the product price within 30 minutes, ensuring it remains competitive while optimizing for profit margins.

Customer Demand Fluctuation Response

Given that the AI algorithms monitor sales velocity and customer purchasing behaviors, when sales velocity decreases by 20% or more over a 7-day period, the algorithm should suggest a price decrease to stimulate demand within 24 hours, maintaining at least a 10% profit margin.

Rollback Mechanism for Price Changes

Given that prices can be adjusted dynamically, when a price adjustment is made, then the system should log the original price, enabling a rollback option that can be executed within 48 hours if sales do not meet the expected threshold post-adjustment.

Integration with Inventory Management System

Given that the pricing algorithms operate within the wider retail system, when inventory levels drop below a predetermined threshold, the algorithm should trigger a price increase by 5%, ensuring profit stability while notifying the inventory management team.

Predictive Analytics for Seasonal Pricing

Given historical sales data and patterns, when a seasonal high-demand period approaches, the algorithm should propose a 15% price increase for high-demand products at least 14 days in advance, allowing for market testing and consumer reaction monitoring.

Real-Time Alert System for Price Changes

Given that price changes occur, when a price adjustment is made through the AI algorithm, then a notification should be sent to the retail manager via email and dashboard alert immediately after the change has been completed, confirming the new price and rationale.

Competitor Pricing Comparison Dashboard

Given that competitor pricing is being analyzed, when the system detects a price change from a key competitor, then an update to the dashboard should occur within 15 minutes to reflect the new competitor price, allowing for rapid strategic decision-making.

User-Friendly Dashboard Integration

User Story: As a store owner, I want to see real-time price adjustments on my dashboard, so that I can quickly understand pricing changes and their impact on sales.
Description: The User-Friendly Dashboard Integration requirement aims to provide a seamless interface within the BeaconLyte platform where users can monitor dynamic pricing adjustments in real time. This feature involves the creation of a customizable dashboard that highlights price changes, market trends, and alerts for important fluctuations. This functionality is crucial as it presents critical data in an accessible format, allowing users to make informed decisions quickly and efficiently.
Acceptance Criteria: User accesses the customizable dashboard to view real-time price adjustments and market trends for their product inventory.

Given the user is logged into the BeaconLyte platform, when they navigate to the dashboard section, then the dashboard displays real-time data of dynamic pricing adjustments and corresponding market trends within 3 seconds.

A user sets up alerts for significant price changes within the dashboard settings.

Given the user is in the dashboard settings, when they configure alert thresholds for price changes, then the system saves these settings and confirms with a success message and alerts should be triggered upon reaching the defined thresholds.

Users request a historical comparison of pricing adjustments over the past month using the dashboard feature.

Given the user selects the historical data option within the dashboard, when they specify the date range for the past month, then the dashboard generates a comparative report displaying price adjustments with visual graphs and percentage changes.

A user interacts with the dashboard to filter product pricing based on different categories or market segments.

Given the user is viewing the dashboard, when they apply category filters, then the dashboard updates the displayed data to only show products and prices corresponding to the selected categories within 2 seconds.

Users can export the data displayed on the dashboard into a CSV format for further analysis.

Given the user is on the dashboard, when they click the export button, then the system generates a CSV file containing all displayed data related to dynamic pricing and market trends and initiates download within 5 seconds.

The dashboard loads correctly on different devices (desktop and mobile).

Given that the dashboard is accessed from both desktop and mobile devices, when the user logs in to the BeaconLyte platform, then the dashboard is displayed accurately on both devices without loss of functionality or data visibility.

Notifications for Price Changes

User Story: As a retail manager, I want to receive notifications about price changes, so that I can react promptly to maintain competitiveness.
Description: The Notifications for Price Changes requirement involves implementing a system whereby users receive alerts when pricing adjustments occur. This function should include options for customizable notification settings (e.g., real-time alerts, daily summaries) to accommodate different user preferences. By establishing this feature, users can stay informed about critical pricing changes that may need immediate attention, enhancing their ability to respond to market dynamics.
Acceptance Criteria: User receives a real-time alert for a price change during an active shopping session.

Given a user is logged into BeaconLyte and has enabled real-time notifications, when there is a price change for a product in their cart, then the user receives an immediate alert via their preferred communication channel (e.g., email, SMS, in-app notification).

User opts to receive daily summary alerts of price changes for tracked products.

Given a user has selected the daily summary notification option, when the notification period ends, then the user receives a comprehensive email summarizing all price changes for tracked products for that day, including product names, old prices, and new prices.

User customizes their notification settings in BeaconLyte.

Given a user accesses the notification settings section, when they change the frequency of alerts from real-time to daily or weekly and save the settings, then the system updates their preferences and confirms the changes with a notification message.

User receives notifications for competitor price changes related to their tracked products.

Given a user has set up tracking for specific products, when a competitor lowers their price for these products, then the user receives an alert indicating the competitor's price along with the new retail price for their tracked products.

User experiences a delay in receiving alerts for recent price changes.

Given there are price changes that occurred, when the user checks their notifications, then all relevant alerts for price changes should be delivered within a maximum of 5 minutes from the change occurrence.

User disables price change notifications and checks for confirmation.

Given a user has previously enabled notifications, when they disable all alerts and save the changes, then the system should confirm that all notifications are turned off and that the user will not receive further alerts until they re-enable them.

User checks the historical log of price change notifications sent.

Given a user is logged into their account, when they navigate to the notification history section, then they can view a complete log of price change notifications received, including timestamps, product names, and previous and current prices.

Competitor Price Comparison Tool

User Story: As a pricing strategist, I want to compare our prices with competitors directly in BeaconLyte, so that I can identify areas to adjust our pricing.
Description: The Competitor Price Comparison Tool requirement allows users to directly compare their product prices with competitors’ prices on a designated interface. This feature involves integrating competitor pricing data and presenting it through visual metrics. Having this capability enhances strategic pricing by providing context for price adjustments and allowing users to identify pricing gaps, ultimately leading to improved decision-making.
Acceptance Criteria: User accesses the Competitor Price Comparison Tool on the BeaconLyte platform during a weekly pricing strategy review meeting.

Given the user is logged into BeaconLyte, when they navigate to the Competitor Price Comparison Tool, then they should see an up-to-date comparison of their product prices against specified competitors' prices in an easily interpretable format.

User receives daily email alerts summarizing significant price changes from competitors to inform their pricing decisions.

Given the user has subscribed to competitor price alerts, when a competitor significantly alters their prices, then the user should receive an email notification within one hour detailing the changes and affected products.

User wants to visualize pricing gaps between their products and competitors within the dashboard.

Given the user is viewing the Competitor Price Comparison Tool, when they filter for specific product categories, then the system should display a graphical representation of pricing gaps, highlighting where their prices are uncompetitive.

User needs to export competitor pricing data for offline analysis after accessing the tool.

Given the user is on the Competitor Price Comparison Tool page, when they select the export option, then the system should generate a downloadable CSV file containing the current competitor pricing data.

User submits feedback on the usability of the Competitor Price Comparison Tool after their first use.

Given the user has completed their first comparison using the tool, when they submit their feedback via the provided form, then a confirmation message should be displayed indicating successful submission.

User analyzes historical competitor pricing trends using the tool for strategic planning.

Given the user accesses the historical data feature within the Competitor Price Comparison Tool, when they select a date range, then they should see a detailed line graph showing pricing trends for their products vs. competitors during that period.

User adjusts their product prices based on insights gained from the Competitor Price Comparison Tool.

Given the user identifies a pricing opportunity from the tool’s analysis, when they initiate a price change for their products, then the new prices should reflect accurately in their inventory system and be confirmed by a success notification.

Review and Feedback Mechanism

User Story: As a user, I want to provide feedback on pricing decisions, so that the system can improve and better align with market needs.
Description: The Review and Feedback Mechanism requirement establishes a function for users to provide feedback on price changes and their outcomes. This feature will collect data on user satisfaction with pricing strategies and the overall effectiveness of the Dynamic Price Adjuster. The feedback loop is integral for continuous improvement and helps refine algorithms and strategies based on user experiences and suggestions.
Acceptance Criteria: User submits feedback on pricing strategy effectiveness after observing changes in product prices due to the Dynamic Price Adjuster.

Given a user navigates to the feedback section, when they submit their feedback on a price change, then the system should record the feedback in the database and display a confirmation message to the user.

Admin views aggregated feedback on pricing changes to evaluate user sentiment and effectiveness of price adjustments.

Given an admin accesses the feedback dashboard, when they request a report on feedback submitted for pricing strategies, then the system should generate and display a report with average user satisfaction ratings and common feedback themes.

A user provides feedback that a price change negatively impacted their purchasing decision, and the feedback is reviewed for algorithm adjustment.

Given feedback indicating dissatisfaction with a price change is received, when analyzed by the system's feedback loop, then the algorithm should flag this feedback for review and suggest potential adjustments to pricing strategies based on the insights.

User updates their feedback on a recent pricing change they previously submitted.

Given a user has previously submitted feedback on pricing, when they navigate to the feedback section to update their comment, then the system should allow them to modify their feedback and save the changes successfully.

The price changes made by the Dynamic Price Adjuster are analyzed over a 30-day period to determine user feedback trends.

Given price adjustments made by the Dynamic Price Adjuster, when the system compiles feedback over a 30-day period, then it should provide analysis results on the correlation between price changes and user satisfaction ratings in a visual format.

User experiences a real-time notification about the outcome of their submitted feedback on pricing strategy.

Given a user submits feedback on pricing, when the feedback has been analyzed by the system, then the user should receive a real-time notification indicating whether their feedback led to any changes in pricing strategy or adjustments.

Competitor Price Tracker

Competitor Price Tracker continuously monitors competitor prices through automated data gathering. This feature informs Marketing Strategists of market changes, allowing timely adjustments to pricing strategies. By staying informed about competitor actions, users can enhance pricing decisions, fostering a more competitive edge in the marketplace.

Requirements

Automated Price Monitoring

User Story: As a Marketing Strategist, I want an automated system that monitors competitor prices so that I can quickly adjust my pricing strategies in response to market changes, ensuring I remain competitive.
Description: The Automated Price Monitoring requirement involves implementing a system that continuously tracks and collects pricing information from competitors' websites or databases using web scraping and API integration. This feature's functionality will ensure that the data is updated in real-time to reflect any changes in competitor pricing, ensuring users have access to the most current and relevant pricing information. The benefits of this functionality include improving the speed and accuracy of pricing strategy adjustments, allowing for timely responses to market changes. Integration within the BeaconLyte platform will allow users to visualize competitor pricing data alongside their analytics, creating a comprehensive market view that informs decision-making.
Acceptance Criteria: As a Marketing Strategist, I want to receive real-time notifications when competitor prices change, so I can promptly adjust our pricing strategies accordingly.

Given that competitor price data is being monitored, When a competitor's price changes, Then the system should send an immediate notification to the designated user via email.

As a Marketing Strategist, I want to view a dashboard displaying current competitor prices alongside our pricing data, enabling easy comparison and analysis.

Given that the competitor pricing data is updated real-time, When I access the dashboard, Then I should see a comprehensive view of both competitor prices and our prices for selected products.

As a Marketing Strategist, I want the system to generate weekly reports summarizing pricing trends and significant changes among competitors.

Given that competitor price tracking is in place, When the report generation schedule arrives, Then the system should compile and distribute a report that highlights major price changes and trends over the past week.

As a Marketing Strategist, I want the system to integrate competitor price changes into our pricing model calculation, so we can determine optimal pricing strategies.

Given that I input our desired pricing rules, When a competitor's price changes and triggers a recalculation, Then the system should update the recommended pricing suggestions based on the latest competitor data.

As a Marketing Strategist, I need to ensure the accuracy of competitor pricing information collected, to confirm the credibility of the data being used for decision-making.

Given that competitor prices are scraped from their websites, When the system retrieves pricing data, Then it should validate accuracy by comparing it against multiple sources or previous data points before finalizing the information.

As a Marketing Strategist, I want to customize the parameters for which products are monitored for competitor pricing, to focus on our key items.

Given that I access the product monitoring settings, When I select specific products for tracking, Then the system should apply those parameters and begin monitoring only the selected items.

As a Marketing Strategist, I want to ensure the historical pricing data of competitors is recorded, so I can analyze trends over time.

Given that the automated price monitoring has been running for at least one month, When I access the historical data section, Then I should see a complete record of competitor pricing changes logged chronologically.

Customizable Alerts

User Story: As a Marketing Strategist, I want to set up customizable alerts for competitor price changes so that I can be notified instantly and make timely adjustments to my pricing strategies without needing to monitor manually.
Description: The Customizable Alerts requirement focuses on developing a feature that allows users to set personalized notifications based on specific price changes or trends observed in competitor pricing data. Users can define parameters such as percentage changes or specific threshold prices that trigger alerts. This capability is essential for keeping stakeholders informed without needing to manually monitor prices continuously. It enhances the platform's value by ensuring users proactively respond to market fluctuations, rather than reactively, supporting timely and informed pricing decisions across their retail strategies.
Acceptance Criteria: As a marketing strategist, I want to set a customizable alert for when a competitor's price drops by 10%, so that I can adjust my pricing strategy accordingly.

Given I am on the Competitor Price Tracker dashboard, when I set an alert for a 10% price drop for a specific competitor, then I receive a notification immediately when that competitor's price falls by 10%.

As a retailer, I want to create multiple alerts for different competitors at the same time, so I can monitor pricing changes more effectively across the market.

Given I am logged into my account, when I create alerts for three different competitors with specific threshold prices, then all alerts should be saved and active without any errors.

As an analyst, I need to receive alerts during specific business hours only, ensuring I am only notified when I can take immediate action on price changes.

Given I am configuring my alert settings, when I select a specific time range for alerts, then I should only receive notifications within that selected time frame and no alerts outside of it.

As a user, I wish to modify an existing alert whenever needed, ensuring I can keep up-to-date with market changes.

Given I have previously set an alert for a competitor, when I modify the price threshold and save the changes, then my alert should reflect the updated parameters successfully without errors.

As a marketing strategist, I want to receive a summary of all alerts triggered within the last week, enabling me to review significant market changes easily.

Given the alerts have been triggered, when I request a weekly summary report, then I should receive a detailed report listing all triggered alerts with their corresponding price changes.

As a user, I want to ensure that I can turn off alerts temporarily without losing the settings, allowing flexibility in managing notifications.

Given I am on the alerts management screen, when I select an option to temporarily deactivate an alert, then that alert should be turned off and remain in place without losing the initial configurations.

Competitor Price Comparison Dashboard

User Story: As a Marketing Strategist, I want a dashboard that visually compares my prices to those of my competitors so that I can quickly understand my pricing position in the market and make informed decisions.
Description: The Competitor Price Comparison Dashboard requirement outlines the development of a dedicated dashboard component where users can visually compare their pricing against competitors’ pricing in real time. This dashboard will present data in clear, actionable formats such as graphs and charts, making it easier to analyze trends and pricing strategies visually. The implementation of this dashboard within the BeaconLyte platform will create a seamless experience, allowing users to derive insights quickly and make data-driven decisions. This feature is crucial for enabling users to identify pricing advantages or disadvantages in their market segment, facilitating strategic adjustments.
Acceptance Criteria: Display of Competitor Prices in Real-Time.

Given the user is on the Competitor Price Comparison Dashboard, when the dashboard is accessed, then the current prices of all competitors should be displayed accurately in real-time with no more than a 5-second refresh rate.

Visual Comparison of Prices Through Graphs.

Given the competitor prices are available, when a user selects a time frame, then a comparative graph should display their prices against competitors' prices for that selected period, allowing easy visualization of trends.

User Alerts for Price Changes.

Given that the Competitor Price Tracker is monitoring prices, when a competitor's price changes by more than 10% for any item, then the system should automatically send an alert to the user within 5 minutes of the change happening.

Customization Options for Dashboard Components.

Given the user wants to customize their dashboard, when they access the customization settings, then they should be able to add, remove, or rearrange components on the dashboard without any lag or errors.

Data Export Functionality.

Given the user needs to share data, when they click on the export option, then the dashboard data should be exportable in CSV and Excel formats without loss of information or formatting errors.

Historical Price Data Access.

Given the user is analyzing pricing strategies, when they select a competitor, then the dashboard should provide access to historic price data for the user to review and compare over the past 12 months.

User Interface Responsiveness on Mobile Devices.

Given the user accesses the dashboard via a mobile device, when they navigate the dashboard, then all elements should resize and reposition correctly to ensure full functionality and accessibility without any broken layouts.

Historical Price Trend Analysis

User Story: As a Marketing Strategist, I want to analyze historical pricing trends of my competitors so that I can identify patterns and make more informed pricing predictions and strategies.
Description: The Historical Price Trend Analysis requirement focuses on creating a feature that enables users to review and analyze historical pricing trends of competitors over selected periods. This feature will provide insights into how competitors' prices have changed over time, helping users to identify seasonal patterns and forecast future pricing movements. By integrating this analysis into the existing analytics framework of BeaconLyte, users will be able to enhance their predictive strategies and make informed pricing decisions based on historical data rather than just current snapshots, ultimately leading to more effective long-term strategies.
Acceptance Criteria: As a Marketing Strategist, I want to review historical pricing trends of competitors for the past six months so that I can identify seasonal pricing patterns and forecast future pricing movements.

Given I am on the Historical Price Trend Analysis page, when I select a competitor and a date range of six months, then I should see a graphical representation of the competitors' price changes over that period.

As a Marketing Strategist, I want to filter historical price trends by product category to identify relevant pricing strategies in my specific market segment.

Given I am on the Historical Price Trend Analysis page, when I apply a filter for a specific product category, then the displayed trends should update to reflect only the historical prices of competitors in that category.

As a Marketing Strategist, I want to receive alerts when a competitor's price drops or increases by more than 10% within a month, so that I can react quickly to market changes.

Given I have set alert thresholds for price changes, when a competitor's price changes by more than 10%, then I should receive a notification alerting me to the specific changes.

As a Marketing Strategist, I want to export the historical pricing data to a CSV file for further analysis and reporting.

Given I am viewing the historical pricing trends, when I select the option to export the data, then a CSV file containing the relevant pricing information should be generated and ready for download.

As a Marketing Strategist, I want to visualize pricing trends using different chart types (line, bar, etc.) to better understand data variations.

Given I am on the Historical Price Trend Analysis page, when I select a different chart type from the available options, then the historical pricing data should update to reflect the selected visualization format.

As a Marketing Strategist, I want to visualize at least three years of historical pricing trends to identify long-term pricing behaviors of competitors.

Given I select the three-year date range on the Historical Price Trend Analysis page, when the historical prices are loaded, then I should be able to view a continuous trend line or graph representing the pricing data over the selected duration.

As a Marketing Strategist, I want to access a summary report of trends, including average prices and the frequency of price changes over a specified period, to analyze competitor pricing performance.

Given I have selected a specific date range, when I generate the summary report, then it should include the average prices, the number of price changes, and any identified trends in a clear and concise manner.

Integration with Internal Pricing Strategies

User Story: As a Marketing Strategist, I want my competitor pricing data integrated with my internal pricing models so that I can easily adjust strategies based on real-time market insights, enhancing my competitiveness.
Description: The Integration with Internal Pricing Strategies requirement outlines the need to ensure that the competitor price tracking feature can effectively communicate and integrate with the retailer's existing pricing strategies. This involves creating an API or similar mechanism that allows users to apply the insights from the Competitor Price Tracker directly to their pricing models in BeaconLyte. The significance of this requirement lies in its ability to create a fluid workflow where market insights can directly inform pricing decisions, ensuring that pricing strategies are based on real-time data and improving the overall accuracy and effectiveness of price adjustments.
Acceptance Criteria: User Story: Marketing Strategist utilizes the Competitor Price Tracker to adjust their pricing strategy based on competitor actions.

Given the Competitor Price Tracker is integrated with internal pricing strategies, when the user accesses the dashboard, then they can view real-time competitor pricing data and receive alerts on significant price changes.

User Story: Retailer wants to apply insights from the Competitor Price Tracker to their internal pricing model.

Given that competitor pricing data is available, when the user selects a product in their pricing model, then they should be able to see recommended pricing adjustments based on competitor pricing insights.

User Story: Marketing Strategist needs to ensure the pricing updates are aligned with business goals.

Given that internal pricing strategies are set, when the user reviews the suggested adjustments from the Competitor Price Tracker, then they can manually approve or modify the recommendations before applying them to the pricing model.

User Story: Retailer expects real-time responsiveness from the pricing adjustments after integrating competitor data.

Given that the API is functioning correctly, when a competitor changes their price, then the pricing model in BeaconLyte should update within 30 minutes to reflect the most current market conditions.

User Story: Marketing Strategist needs to track the historical pricing adjustments made based on competitive insights.

Given that pricing adjustments have been made, when the user accesses the reporting feature, then they can view a historical log of adjustments alongside competitor price changes for analysis.

User Story: Retailer requires validation of data quality from the Competitor Price Tracker before implementing pricing changes.

Given that competitor data is collected, when the user reviews the data integrity report, then they should see metrics on data accuracy, completeness, and timeliness before executing any pricing changes.

Reporting and Analytics Tools

User Story: As a Marketing Strategist, I want to generate customizable reports on competitor pricing so that I can analyze and present data-driven insights to my team for better strategic planning.
Description: The Reporting and Analytics Tools requirement emphasizes the need for comprehensive reporting functionalities that allow users to generate detailed reports on competitor pricing and trends over specified timeframes. Users should be able to customize report parameters, including selected competitors, time periods, and types of reports (summary vs. detailed). This feature will provide stakeholders with actionable insights into pricing strategies and market positioning, enabling more informed decision-making. By incorporating this capability into the BeaconLyte platform, businesses can leverage data-driven strategies to refine their pricing decisions, guided by thorough analysis.
Acceptance Criteria: Generating Detailed Competitor Pricing Reports in the BeaconLyte Platform.

Given a user is logged into BeaconLyte, when they navigate to the Reporting and Analytics Tools section and select 'Generate Report', then the system should present customizable parameters for competitors, timeframes, and report types (summary vs. detailed). Additionally, when the user submits the parameters, the report should be generated within 30 seconds and allow the user to download it in .pdf and .csv formats.

Filtering Competitor Data by Date Range and Competitor Selection.

Given the Reporting and Analytics Tools interface, when a user selects specific competitors and a date range, then the system should display only the competitor pricing data that falls within the specified parameters without errors and with accurate data representation.

Real-time Updates on Competitor Pricing Changes.

Given a user subscribes to real-time alerts for selected competitors, when a competitor changes their price, then the system should notify the user within 5 minutes via email or platform notification showing the updated pricing and the previous price for comparison.

Comparative Analysis of Pricing Strategies among Selected Competitors.

Given a user has generated reports for multiple competitors, when viewing the report, then the system should provide a comparative analysis dashboard that highlights key pricing trends, percentage changes, and actionable insights based on the data over the selected timeframe.

User Customization of Reporting Dashboards.

Given a user is in the Reporting and Analytics Tools section, when they customize their dashboard to include specific metrics and KPIs related to competitor pricing, then the system should save these preferences and display the updated dashboard upon the user's next login.

User Access Control for Competing Report Data.

Given different user roles within the BeaconLyte platform, when a user with limited access tries to generate a competitor pricing report, then the system should restrict this action and display an appropriate error message stating insufficient permissions.

Evaluating the Performance Impact of Reporting Features on System Load Times.

Given a user generates a detailed competitor pricing report, when this action is completed, then the platform should maintain a system load time of under 2 seconds for any other user actions taken in the dashboard at the same time.

Profit Margin Optimizer

Profit Margin Optimizer evaluates the profitability of each product based on suggested pricing alterations. By balancing product demand with competitive rates, this feature enables Marketing Strategists to fine-tune pricing strategies that maximize profit margins while still attracting customers. It provides valuable insights into pricing elasticity, helping mitigate risks associated with price alterations.

Requirements

Dynamic Pricing Adjustment

User Story: As a Marketing Strategist, I want to receive real-time pricing suggestions based on market conditions so that I can adjust our pricing strategy promptly and effectively to optimize profit margins and remain competitive in the market.
Description: The Dynamic Pricing Adjustment requirement involves developing a system that allows for real-time evaluations of product pricing based on market demand, competition pricing, and inventory levels. This feature will enable the Profit Margin Optimizer to suggest optimal pricing adjustments that maintain competitive positioning while maximizing profit margins. By integrating machine learning algorithms, the system will analyze sales data and competitive pricing patterns to provide dynamic recommendations that can be automatically applied or reviewed by Marketing Strategists. This functionality enhances the retailer's ability to respond quickly to market changes and improves profitability with data-driven pricing strategies.
Acceptance Criteria: As a Marketing Strategist, I need to adjust product prices in real-time based on live market data and inventory levels to maximize profitability without driving away customers.

Given the system's access to real-time market data, when the inventory for a product falls below a predefined threshold and competitor pricing is higher, then the system suggests a price decrease that does not fall below the cost price.

As a Marketing Strategist, I want to visualize price changes suggested by the system to understand their impact on profit margins before implementation.

Given that the system generates pricing suggestions, when the Marketing Strategist reviews the suggested pricing changes, then the interface displays projected profit margins alongside historical sales data for each suggested price.

As a Marketing Strategist, I need to evaluate the effectiveness of the pricing adjustments made by the system over a specific period to ensure they contribute positively to sales and profitability.

Given a set of pricing adjustments applied over the last three months, when the Marketing Strategist accesses the performance report, then the report shows a clear comparison of sales data before and after the adjustments, indicating a positive impact on profit margins.

As a Marketing Strategist, I want the system to automatically adjust pricing based on pre-set parameters to react quickly to market changes without manual intervention.

Given that the inventory levels and competitor prices fluctuate, when these conditions trigger the predefined rules in the system, then the system automatically adjusts product prices within the specified limits and notifies the Marketing Strategist of the adjustments made.

As a retail manager, I want to receive real-time alerts about significant changes in market conditions or competitor pricing that may affect the recommended pricing for our products.

Given that the competitor pricing changes significantly or there is a high demand for a product, when such a change occurs, then the system sends an alert to the Marketing Strategist indicating the nature of the change and its potential impact on pricing strategies.

As a Marketing Strategist, I need to be able to override automated pricing adjustments suggested by the system to ensure alignment with overall marketing strategies and promotions.

Given that the system has proposed a price adjustment, when the Marketing Strategist opts to override the suggestion, then the system captures the manager's input, provides confirmation of the override, and logs the reason for future analysis.

Competitive Price Tracker

User Story: As a Marketing Strategist, I want to track competitors' pricing in real-time so that I can adjust our pricing strategy accordingly and ensure our products remain competitive and attractive to customers.
Description: The Competitive Price Tracker requirement is aimed at developing a module that continuously monitors and records pricing changes from key competitors. This feature will feed data into the Profit Margin Optimizer, allowing for comprehensive analysis and more informed pricing decisions. By maintaining an up-to-date database of competitors’ pricing strategies, retailers can better understand market positioning and consumer behavior. The Competitive Price Tracker will also provide alerts when competitor prices change significantly, allowing for proactive strategy adjustments. This functionality ensures that retailers remain competitive and can respond strategically to pricing dynamics.
Acceptance Criteria: As a Marketing Strategist, I need to receive real-time alerts when a competitor’s price changes significantly, so that I can adjust my pricing strategy accordingly and maintain a competitive edge in the market.

Given I am a Marketing Strategist, When a competitor’s price for a tracked product changes by more than 5%, Then I should receive an alert notification within 5 minutes of the price change.

As a Marketing Strategist, I want to access a dashboard that displays competitors’ pricing changes over the last 30 days, so that I can identify trends and make informed pricing decisions.

Given the Competitive Price Tracker is operational, When I access the pricing changes dashboard, Then I should see a graphical representation of each competitor's pricing changes over the last 30 days, along with the corresponding dates.

As a Retail Manager, I need the Competitive Price Tracker to update the product pricing database in real-time, ensuring that I have the most current data available to inform my pricing strategies.

Given the Competitive Price Tracker is active, When a competitor updates their price, Then the product pricing database should reflect this change within 2 minutes, ensuring that I have up-to-date information.

As a Marketing Strategist, I need to analyze the impact of competitors' price changes on our sales volume, to evaluate whether adjustments in our pricing are needed based on market conditions.

Given that the Competitive Price Tracker has retrieved competitor pricing data, When I run a profitability report, Then I should see a comparison of our sales volume against competitor price changes, including insights on pricing elasticity.

As a Business Analyst, I want to generate a weekly report summarizing competitors' pricing strategies and price fluctuation trends, so that I can provide management with actionable insights.

Given the Competitive Price Tracker is functioning, When I request a weekly report, Then I should receive a comprehensive report that includes summary data on price changes, average pricing, and recommendations for pricing strategy adjustments.

As a Retail Manager, I would like to configure my tracking preferences for specific products and competitors, so I can focus on the most relevant data for my strategic decisions.

Given I have access to the Competitive Price Tracker settings, When I select specific products and competitors to track, Then these preferences should be saved, and alerts should only be sent for those selected items.

As a Marketing Strategist, I want the Competitive Price Tracker to provide historical pricing data so I can analyze past trends and adjust future strategies accordingly.

Given the Competitive Price Tracker has stored previous pricing data, When I request historical pricing information, Then I should receive detailed records of price changes for the past year for all tracked products and competitors.

Pricing Elasticity Analyzer

User Story: As a Marketing Strategist, I want to analyze pricing elasticity for our products so that I can understand customer behavior better and make informed pricing decisions that optimize profitability without alienating our customers.
Description: The Pricing Elasticity Analyzer requirement focuses on creating a tool that assesses how changes in price impact the demand for each product. By utilizing historical sales data and market trends, this feature will calculate the elasticity of product pricing, determining how sensitive customers are to price changes. This tool will help Marketing Strategists understand the potential risks and opportunities associated with pricing adjustments, enabling smarter and more strategic decisions regarding pricing strategies. The outcomes will support predictive analytics efforts, contributing to a more well-rounded approach to inventory and pricing management.
Acceptance Criteria: Analyze Impact of Price Increase on Demand for Popular Products

Given historical sales data for a popular product, when a 10% increase in price is applied, then the pricing elasticity analyzer should output the projected change in demand based on the historical elasticity value.

Evaluate Price Reduction Effects on Steady Sellers

Given sales data for a steady-selling product, when a 15% price reduction is applied, then the pricing elasticity analyzer should predict an increase in demand of at least 20% if elasticity is calculated as being greater than -1.

Assess Competitor Pricing Impact on Product Demand

Given competitor pricing information and historical data, when the pricing elasticity analyzer is run, then it should provide insights on how demand shifts for our product when competitors lower their prices by 5%.

Run Sensitivity Analysis for Seasonal Products

Given a seasonal product’s sales data, when seasonal price changes are simulated, then the pricing elasticity analyzer should deliver projections on expected demand shifts for each proposed pricing scenario.

Simulate Multi-Product Price Adjustment Impact

Given a selection of products poised for price changes, when the pricing elasticity analyzer processes simultaneous price adjustments, then it should calculate the overall expected demand change across all products combined in one report.

Benchmark Against Historical Pricing Strategies

Given past pricing strategies and associated sales data, when the pricing elasticity analyzer evaluates current price points, then it should provide recommendations comparing potential profitability against historical performance.

Generate Reports on Pricing Strategy Effectiveness

Given a specified period of sales and pricing data, when the pricing elasticity analyzer is initiated, then it should produce a comprehensive report detailing pricing strategy effectiveness, including metrics like ROI and demand forecasts.

Profit Margin Dashboard Widget

User Story: As a Marketing Strategist, I want a dashboard widget that displays real-time profit margin data so that I can quickly identify trends and make timely decisions to maintain or improve profitability on our products.
Description: This requirement involves creating a dashboard widget that visually displays profit margin analytics in real-time. The widget will provide key insights into profit margins per product, helping Marketing Strategists quickly assess the impact of pricing decisions. Integration with the existing BeaconLyte dashboard will streamline access to critical information, ensuring that users can monitor trends and performance indicators systematically. The Profit Margin Dashboard Widget will also enable customization of notifications for margins falling below acceptable thresholds, prompting immediate action and strategic adjustments that safeguard profitability.
Acceptance Criteria: Marketing Strategists access the Profit Margin Dashboard Widget on the BeaconLyte dashboard to evaluate the profit margins for each product in response to fluctuating demand and competitive pricing changes.

Given that the user is authenticated and is on the BeaconLyte dashboard, when they click on the Profit Margin Dashboard Widget, then the widget displays real-time profit margin data for all products, updated within 5 seconds.

A Marketing Strategist wants to set up custom notifications for products whose profit margins drop below a defined threshold.

Given the user has accessed the Profit Margin Dashboard Widget, when they configure the threshold settings for profit margins, then the system should allow thresholds to be set at any value from 0% to 100% and save these settings correctly.

The Profit Margin Dashboard Widget should provide a visual representation of profit margins that helps Marketing Strategists quickly identify trends over time.

Given that the user is viewing the Profit Margin Dashboard Widget, when they select a time frame (daily, weekly, monthly), then the widget displays a line chart or bar graph showing profit margin trends for each selected product over that period.

A Marketing Strategist receives a notification about a product’s profit margin falling below the acceptable threshold.

Given that the threshold is set for a specific product and the profit margin for that product drops below the set limit, when the condition is met, then the system sends an immediate alert via email and in-app notification to the user.

The Profit Margin Dashboard Widget integrates seamlessly with the existing dashboard layout without causing functional or visual disruptions.

Given that the user views the complete dashboard, when the Profit Margin Dashboard Widget is rendered, then the layout must not overlap or distort any other widgets and should maintain overall dashboard aesthetics and usability.

Marketing Strategists wish to compare profit margins of similar products to identify pricing strategy effectiveness.

Given that the user is using the Profit Margin Dashboard Widget, when they select multiple similar products, then the widget should allow comparing profit margins side by side in a clear and concise format, such as a comparative table or bar chart.

User Training and Documentation

User Story: As a Marketing Strategist, I want to access training resources and documentation so that I can become proficient in using the Profit Margin Optimizer and make data-driven decisions to enhance our pricing strategies.
Description: The User Training and Documentation requirement is crucial for ensuring that Marketing Strategists and other users can leverage the Profit Margin Optimizer effectively. This will involve creating comprehensive training materials, including user manuals, tutorial videos, and interactive workshops. The documentation will cover all functionalities of the Profit Margin Optimizer, with a focus on its features a user may be utilizing, such as the Dynamic Pricing Adjustment and Pricing Elasticity Analyzer. This requirement aims to empower users with knowledge, promoting competent and confident use of the tool, ultimately leading to better decision-making and maximized profit margins.
Acceptance Criteria: User Access to Training Materials

Given that a Marketing Strategist has logged into the BeaconLyte platform, when they navigate to the training section, then they should be able to access all user manuals and tutorial videos relevant to the Profit Margin Optimizer feature.

Interactive Workshop Engagement

Given that a Marketing Strategist has registered for an interactive workshop, when they attend the workshop, then at least 80% of participants should successfully complete a feedback survey confirming they understand how to utilize the Profit Margin Optimizer features discussed.

Comprehensive User Manual Availability

Given that the user manual for the Profit Margin Optimizer has been created, when a new user searches for this manual within the training documentation portal, then the user should be able to locate and download the manual without any errors.

Video Tutorial Completion Tracking

Given that the video tutorials on the Profit Margin Optimizer are available, when a user completes watching a tutorial, then the system should record their completion status and provide a certificate of completion for each tutorial.

Understanding of Dynamic Pricing Adjustment Feature

Given that a user completes the training materials on the Dynamic Pricing Adjustment feature, when they take a knowledge check quiz afterward, then they must achieve a minimum score of 75% to demonstrate their understanding.

User Feedback Collection on Training Effectiveness

Given that users have participated in training for the Profit Margin Optimizer, when surveyed about the training effectiveness, at least 90% of users should report feeling confident in using the features they've learned about.

Customer Behavior Insights

Customer Behavior Insights analyzes purchasing patterns and preferences from historical sales data. This feature empowers users to predict how changes in pricing might affect customer buying behavior, enabling strategic decision-making that aligns with customer expectations and maximizes sales opportunities.

Requirements

Predictive Pricing Analysis

User Story: As a retail manager, I want to predict how different pricing strategies will affect customer purchasing decisions so that I can optimize my pricing to increase sales and profitability.
Description: This requirement encompasses the development of a predictive analytics engine that analyzes historical sales data and customer purchasing behavior to forecast the impact of various pricing strategies on future sales. The functionality will allow users to input potential pricing changes and visualize predicted customer responses, thus aiding in strategic pricing decisions. This capability is crucial in enhancing the overall pricing strategy by aligning it with customer expectations and maximizing sales opportunities. Integration will be seamless within the existing Customer Behavior Insights framework, allowing users to draw on their historical data effortlessly.
Acceptance Criteria: User inputs a new pricing strategy within the Predictive Pricing Analysis tool to assess its impact on customer behavior.

Given the user has access to the Predictive Pricing Analysis tool, When they enter a new pricing strategy based on historical data, Then the system should display a predicted change in customer purchasing behavior within 10 seconds.

The user wants to analyze the impact of a price increase on customer purchases over the next quarter.

Given the user selects a predefined price increase scenario, When they execute the predictive analysis, Then the results should show potential sales volume and revenue changes for the upcoming quarter accurately reflecting historical trends.

A retailer wishes to visualize how different pricing strategies affect customer segments.

Given the user selects multiple customer segments for analysis, When they apply various pricing strategies, Then the analytics engine should generate a comparative report showing projected customer responses for each segment within 5 minutes.

The user needs to evaluate the predictive outcomes against actual sales data after implementing a pricing change.

Given that a pricing change has been made and sales data has been collected, When the user reviews the predictive analysis results alongside actual sales results, Then the system should indicate the accuracy of the predictions compared to actual outcomes.

The user is integrating their existing historical sales data into the Predictive Pricing Analysis feature.

Given historical sales data exists in the system, When the user initiates the integration process, Then the system should successfully import the data without errors and confirm integration within 3 minutes.

A user wants to compare the effectiveness of two different pricing strategies using the predictions generated.

Given the user has conducted two separate predictive analyses for different pricing strategies, When they select the compare function, Then the system should display a side-by-side comparison of predicted customer responses and their projected sales impact.

Customers request real-time alerts if predicted responses to pricing changes fall below a certain threshold.

Given that the user has set up alert thresholds for pricing strategy predictions, When the predicted customer response falls below the threshold, Then the system should send a real-time alert to the user via email or SMS within 1 minute.

Customer Segmentation Dashboard

User Story: As a marketer, I want to segment customers based on their buying patterns so that I can create targeted marketing campaigns aimed at different customer groups.
Description: This requirement focuses on creating an interactive dashboard that segments customers based on their purchasing behavior and preferences. It will visually display key metrics, such as frequency of purchase, average purchase value, and category preferences. By enabling users to identify and analyze different customer segments, this feature empowers businesses to tailor their marketing strategies and personalized offers to specific groups, ultimately driving customer engagement and loyalty. The dashboard will integrate with existing analytics tools to leverage historical sales data for effective segmentation.
Acceptance Criteria: Customer Segmentation Dashboard displays distinct customer segments based on historical purchasing data.

Given historical sales data is available, When the dashboard is accessed, Then it should display at least three distinct customer segments based on purchasing behavior.

The dashboard shows key metrics for each customer segment such as frequency of purchase and average purchase value.

Given distinct customer segments are displayed, When a user selects a segment, Then the dashboard should present metrics including frequency of purchase and average purchase value for that segment.

Users can filter customer segments by different criteria to gain deeper insights.

Given the dashboard is loaded, When a user applies filters for criteria such as purchase frequency or product categories, Then the displayed segments should update accordingly to reflect the applied filters.

Integration with existing analytics tools enables seamless data retrieval.

Given the dashboard is set up, When a user interacts with the dashboard, Then it should successfully pull and display data from at least one existing analytics tool.

The dashboard allows users to export customer segment data for further analysis.

Given customer segments are displayed, When a user clicks the export button, Then the dashboard should provide an option to download segment data in CSV format.

The dashboard is responsive and displays well on different devices and screen sizes.

Given the dashboard is accessed on various devices, When viewed on a desktop, tablet, or mobile, Then the layout should adjust appropriately without loss of functionality or data visibility.

The dashboard includes real-time updates of customer behavior data to ensure accuracy.

Given that new sales data is generated, When the dashboard is refreshed, Then it should automatically update the customer segments and metrics to reflect the latest data available.

Real-time Behavior Alerts

User Story: As a store owner, I want to receive real-time alerts about changes in customer purchasing behavior so that I can quickly adapt my inventory and marketing strategies.
Description: This requirement involves the implementation of a real-time alert system that notifies users of significant changes in customer purchasing behavior, such as unexpected spikes or drops in sales for certain products or categories. These alerts will enable users to act quickly to address potential issues or capitalize on emerging trends. The alerts will be customizable, allowing users to set their own thresholds for notifications. This feature enhances responsiveness in the dynamic retail environment, ensuring that strategies can be adjusted in real-time to align with customer behavior.
Acceptance Criteria: User decides to set up real-time alerts for specific product categories after noticing fluctuating sales trends during a promotional period.

Given the user has access to the real-time behavior alerts feature, when the user sets a threshold for alerting of a 20% increase or decrease in sales of a selected product category, then the system should send a notification immediately when this threshold is crossed.

A user receives a real-time alert indicating that a particular product is experiencing a significant spike in sales.

Given the user is subscribed to alerts for the designated product, when the system detects a spike of over 30% in sales in a 24-hour period, then the user should receive a notification via their preferred communication channel (email or SMS).

After setting the alert thresholds for different product categories, the user wishes to review and adjust these settings based on past notifications received.

Given the user previously received alerts, when the user accesses the alert settings menu, then the user should be able to view and modify the current thresholds for each product category along with a summary of the alert history.

A user wants to receive alerts for a newly launched product that they expect to sell well based on customer behavior insights.

Given a new product is launched, when the user sets a notification threshold of increased sales for that product, then the system should monitor sales data and alert the user within 5 minutes of the threshold being breached.

The user experiences a consistent drop in sales for a specific product category and sets an alert to stay informed.

Given the user has defined low sales thresholds for the product category, when the sales drop below the specified threshold for 3 consecutive days, then the alert notification should be triggered to indicate this trend.

A manager conducts a weekly review of alert effectiveness to assess the utility of real-time notifications.

Given the manager has scheduled the review, when they access the alert summary dashboard, then they should see statistics on alerts triggered within the last week, including the number of alerts sent, categories affected, and user responses.

A user wants to disable alerts temporarily during a planned inventory audit.

Given the user accesses the alert management system, when they select the option to disable all alerts for a defined period, then all notifications should be paused and automatically resume after the specified audit duration.

Seasonal Price Adjustments

Seasonal Price Adjustments harness seasonal sales trends to recommend strategic price changes. This feature helps Marketing Strategists capitalize on peak buying seasons by adjusting prices accordingly, ensuring competitive offers throughout the year while optimizing sales volume and inventory turnover.

Requirements

Dynamic Pricing Engine

User Story: As a marketing strategist, I want the system to dynamically adjust prices based on seasonal trends so that I can maximize sales and ensure we stay competitive throughout the year.
Description: The Dynamic Pricing Engine requirement focuses on developing an AI-driven engine that analyzes historical sales data, market trends, and seasonal patterns to recommend optimal pricing strategies. It enhances the Seasonal Price Adjustments feature by ensuring that prices are dynamically adjusted based on real-time data analytics. This requirement is critical for enabling retailers to respond swiftly to changes in demand and competition while maximizing sales and inventory efficiency. It integrates seamlessly with the existing analytics platform in BeaconLyte, providing actionable insights directly to marketing strategists. The expected outcome is a robust pricing strategy that aligns with seasonal variations, boosting both revenue and customer satisfaction.
Acceptance Criteria: As a Marketing Strategist, I want to view recommended price adjustments based on the Dynamic Pricing Engine, so that I can make informed decisions during peak buying seasons.

Given the Dynamic Pricing Engine is operational, when the Marketing Strategist accesses the Seasonal Price Adjustments dashboard, then they should see price recommendations that reflect real-time market trends for all active products.

As a retailer, I need the Dynamic Pricing Engine to automatically adjust prices based on historical sales data, allowing me to maximize sales during seasonality changes.

Given that historical sales data and market trends are available, when the system analyzes data during a seasonal transition, then it must dynamically adjust product prices by at least 5% based on established thresholds.

As a Marketing Strategist, I want to receive real-time alert notifications about necessary price changes, so that I can quickly respond to market conditions.

Given the Dynamic Pricing Engine processes data, when a significant market shift occurs (e.g., a 10% increase in competitor pricing), then I should receive an alert notification within 5 minutes to take action.

As a retailer, I want to ensure that the system's price adjustments do not negatively impact inventory turnover rates during peak seasons.

Given that seasonal price adjustments are implemented, when monitoring sales metrics, then the inventory turnover rate should not decrease by more than 2% during the peak buying season.

As a Marketing Strategist, I need to validate that the recommendations from the Dynamic Pricing Engine lead to an increase in sales volume during promotional periods.

Given that price recommendations are generated, when the Marketing Strategist implements the recommended prices during a promotional campaign, then sales volume should increase by at least 15% compared to the previous comparable promotional period.

User-Friendly Interface for Price Adjustments

User Story: As a marketing strategist, I want a simple and intuitive interface to adjust product prices seasonally so that I can quickly respond to market changes without technical assistance.
Description: The User-Friendly Interface requirement aims to create an intuitive dashboard that allows marketing strategists to easily visualize and implement price adjustments. This interface will provide drag-and-drop functionality, allowing users to adjust prices for multiple products quickly. The goal is to streamline the process for strategists to make strategic price changes without needing technical support. The feature will incorporate visual indicators for sales trends and suggested price changes based on the Dynamic Pricing Engine, ensuring that strategists can make informed decisions at a glance. The expected outcome is improved operational efficiency and a reduction in the time required to implement pricing strategies.
Acceptance Criteria: As a Marketing Strategist, I want to access the User-Friendly Interface for price adjustments during the peak buying season to implement strategic price changes based on sales trends and suggestions from the Dynamic Pricing Engine.

Given the Marketing Strategist is logged into the system, When they navigate to the User-Friendly Interface, Then they should see a dashboard displaying current prices, product information, sales trends, and recommended price adjustments in a clear and intuitive layout.

During a quarterly review, the Marketing Strategist needs to adjust prices for multiple products simultaneously using the drag-and-drop functionality in the User-Friendly Interface.

Given the Marketing Strategist is on the price adjustment dashboard, When they select multiple products and drag them to the recommended price range, Then the new prices should update in real-time and confirm with a visual success indicator for each product affected.

As a Marketing Strategist, I want to review suggested price changes based on real-time sales data to make informed decisions before implementing any adjustments.

Given the sales data is being analyzed by the Dynamic Pricing Engine, When the Marketing Strategist clicks on a product, Then they should see detailed visual indicators and trend graphs showing past performance, competitor pricing, and suggested adjustments for that product.

After making price adjustments, the Marketing Strategist wants to ensure that the changes are saved and reflected across the retail platform in real-time.

Given the Marketing Strategist has confirmed the price adjustments, When they click the 'Save Changes' button, Then the system should display a confirmation message, and all adjustments should be updated across the retail platform within two minutes.

The Marketing Strategist is using the User-Friendly Interface and needs to understand the impact of their pricing decisions with historical sales data.

Given the user is on the price adjustment interface, When they select a product, Then they should be able to view historical sales data and trends for that product alongside the suggested price adjustments in a comparative format.

The User-Friendly Interface should provide help and tooltips for new Marketing Strategists who may not be familiar with the pricing adjustment process.

Given the user is new to the platform, When they hover over any feature in the User-Friendly Interface, Then a tooltip with a brief description and guidance should be displayed to assist them.

Real-Time Sales Alert System

User Story: As a marketing strategist, I want to receive instant alerts about significant sales changes so that I can swiftly make price adjustments and optimize our inventory turnover.
Description: The Real-Time Sales Alert System requirement focuses on implementing an alerts mechanism that notifies marketing strategists when sales for specific products exceed or fall below predefined thresholds. This requirement is essential for proactive price management, enabling users to make timely adjustments based on sales performance. Alerts can be customized to reflect specific products, categories, or overall inventory levels. By integrating with the Seasonal Price Adjustments feature, this system will allow users to monitor the effectiveness of pricing strategies and adjust accordingly. The outcome is an enhanced ability to react quickly to sales trends, improving overall sales performance and inventory management.
Acceptance Criteria: Alert Notification for Sales Spike

Given a product has a configured sales threshold, when sales exceed that threshold, then an alert notification is sent to the marketing strategists' dashboard and via email within 5 minutes.

Alert Notification for Sales Drop

Given a product has a configured sales threshold, when sales fall below that threshold, then an alert notification is sent to the marketing strategists' dashboard and via email within 5 minutes.

Customize Alerts for Product Categories

Given a marketing strategist defines alerts for specific product categories, when sales data changes, then the system displays alerts specifically for those categories.

Integration with Seasonal Price Adjustments

Given the Real-Time Sales Alert System is active, when an alert is triggered, then the system provides suggestions for price adjustments based on the Seasonal Price Adjustments feature.

Real-Time Dashboard Updates

Given an active Real-Time Sales Alert System, when sales data changes, then the dashboard is updated to reflect the latest sales figures and alerts in real-time.

User Management for Alerts

Given the need for different user permissions, when a user accesses the alert system, then they should only see alerts relevant to their roles and products they manage.

Audit Trail of Alerts Sent

Given that alerts are generated, when an alert is sent, then that event is logged to provide an audit trail of actions taken and alerts dispatched by the system.

Market Competitor Tracking

User Story: As a marketing strategist, I want to see real-time competitor pricing information so that I can adjust our prices strategically to maintain a competitive edge.
Description: The Market Competitor Tracking requirement involves integrating a feature that monitors competitor pricing and promotional activities in real-time. This capability will provide valuable insights for adjusting our prices competitively during key seasonal periods. The feature will include a dashboard that displays competitor prices, promotions, and other relevant market data alongside BeaconLyte’s own sales analytics. This integration is significant for ensuring that the pricing strategies informed by the Seasonal Price Adjustments feature also consider the competitive landscape, allowing for strategic price positioning. The expected outcome is a comprehensive view of the market that supports smarter pricing decisions.
Acceptance Criteria: Real-time Competitor Pricing Integration

Given the Market Competitor Tracking feature is implemented, when a user accesses the dashboard, then they should see real-time competitor pricing data displayed alongside BeaconLyte’s sales analytics.

Promotional Activity Monitoring

Given the Market Competitor Tracking is active, when competitors launch a promotion, then the system must alert users within one hour and display the promotion details on the dashboard.

Customizable Dashboard for Competitive Insights

Given the Market Competitor Tracking is integrated, when a user configures their dashboard settings, then they should be able to choose which competitor data is shown and how it is visualized.

Historical Competitor Price Data Analysis

Given the Market Competitor Tracking feature is fully operational, when a user selects a date range, then the dashboard must display historical prices and promotions from competitors for that period.

Impact on Pricing Strategy Adjustment

Given real-time competitor tracking is in place, when a seasonal price adjustment is suggested, then the suggested prices must reflect changes in competitor pricing within the same timeframe.

User Feedback on Competitor Data Relevance

Given the Market Competitor Tracking feature has been in use for at least one month, when users submit feedback, then at least 80% of users should find the competitor data relevant and actionable.

Comprehensive Reporting Tool

User Story: As a marketing strategist, I want to generate customizable reports on pricing strategies' performance so that I can analyze our decisions and improve future marketing initiatives.
Description: The Comprehensive Reporting Tool requirement is designed to provide detailed analytics and insights into the effectiveness of seasonal pricing strategies. This feature will generate reports on sales performance before and after price adjustments, taking into account inventory levels and competitor pricing. Users will be able to customize reports to focus on specific products, time frames, or seasonal events, enabling a thorough analysis of marketing initiatives. This requirement is vital to ensure that marketers can assess the impact of their pricing strategies on sales outcomes and inventory management. The intended result is a data-driven approach to refining pricing strategies over time.
Acceptance Criteria: Generate a customizable report focused on the sales performance of specific products before and after price adjustments during the holiday season.

Given that the user selects a specific product and a time frame for holiday sales, When the user requests the report, Then the report should display sales data for the selected product before and after the price adjustment, along with inventory levels and competitor pricing for the same period.

Produce an inventory analysis report to assess the stock levels and turnover rates in relation to seasonal pricing adjustments.

Given that the user selects a specific seasonal event, When the user generates the report, Then the report should include detailed inventory levels and turnover rates before and after the seasonal price changes, highlighting any discrepancies in stock availability.

Provide a comparative report that outlines how the pricing strategy performed against key competitors during the same seasonal period.

Given that the user selects a specific seasonal period for comparison, When the user runs the report, Then the report should show a side-by-side analysis of sales and pricing strategies against the selected competitors, indicating market positioning and competitive advantage.

Allow users to customize report formats based on their specific analytics needs, focusing on visual clarity and data interpretation.

Given that the user selects the customization options for report layout, When the user generates the report, Then the report should reflect the chosen formats, including graphs, tables, and highlight key metrics without any loss of data integrity.

Enable users to export the generated reports in multiple formats for easy sharing and integration with other analytical tools.

Given that the user has generated a report, When the user chooses to export the report, Then the system should allow downloading the report in at least three different formats (e.g., PDF, Excel, CSV) without any data loss.

Implement a system alert for users if inventory levels fall below a predefined threshold after price adjustments.

Given that a price adjustment is made, When inventory levels drop below the defined threshold, Then the system should trigger an alert to the designated user(s) for immediate action.

Facilitate a summary report that presents overall performance metrics of seasonal pricing strategies across various product categories.

Given that the user wants a holistic view of seasonal pricing strategies, When the user generates the summary report, Then the report should include aggregated metrics highlighting overall sales performance, customer engagement, and inventory impact across all product categories affected by seasonal price changes.

Price Sensitivity Analyzer

Price Sensitivity Analyzer evaluates customer responses to price changes across different market segments. This benefit-centric feature gives Marketing Strategists valuable feedback on how various demographics react to price modifications, allowing for more targeted pricing strategies that align with customer willingness to pay.

Requirements

Market Segment Identification

User Story: As a marketing strategist, I want to identify different market segments within my customer base so that I can tailor pricing strategies that effectively cater to each group’s preferences.
Description: The Market Segment Identification requirement enables the Price Sensitivity Analyzer to accurately classify and segment customers based on demographic data, purchase history, and behavioral patterns. This functionality is crucial for understanding which customer groups are most impacted by price changes, ultimately guiding targeted marketing strategies. By offering clear segmentation, marketing strategists can design specific pricing strategies that resonate with distinct market segments, enhancing the efficacy of promotional campaigns and improving customer satisfaction. The implementation of this requirement includes integrating existing customer data sources, establishing criteria for segmentation, and creating a user-friendly interface for strategists to access insights.
Acceptance Criteria: Market segment identification for targeted pricing strategies based on demographic data.

Given that a marketing strategist has input customer demographic data into the Price Sensitivity Analyzer, when the system processes the data, then it must accurately categorize customers into defined market segments based on the established criteria.

Integration of existing customer data sources to enable effective segmentation.

Given that the user has provided access to existing customer data sources, when the integration is performed, then the system should successfully import customer data without any errors and ensure data accuracy of at least 95%.

User interface evaluation for strategists to access insights on market segments.

Given that the user is logged into the system, when they navigate to the Market Segment Insights page, then they should see a user-friendly interface displaying segmented customer groups with relevant analytics clearly presented and easy to interpret.

Real-time update of market segment information based on new customer interactions.

Given that new customer interaction data is entered into the system, when the update process is triggered, then the market segments should reflect new data within 30 seconds to ensure the information remains current and actionable.

Validation of pricing strategies based on identified market segments.

Given that a pricing strategy has been developed for a specific market segment, when the Price Sensitivity Analyzer is used to simulate this strategy, then the predicted customer response must meet a minimum acceptance threshold of 75% positive feedback from the targeted segment.

Analysis of customer purchase history to further enrich market segment profiles.

Given that the customer purchase history is accessible to the analyzer, when the data is processed, then the system must enhance each market segment profile with at least three relevant behavioral insights derived from the historical data.

Price Change Impact Analysis

User Story: As a marketing strategist, I want to analyze the impact of price changes on customer behavior so that I can make informed decisions that maximize revenue and customer retention.
Description: This requirement outlines the need for a robust analytical tool within the Price Sensitivity Analyzer that evaluates the effects of price changes on customer purchasing behavior. By utilizing historical sales data, feedback from customer surveys, and advanced predictive algorithms, this tool will enable marketing strategists to forecast how different segments react to various price adjustments. The insights gleaned from this analysis will help in setting optimal price points and minimizing revenue loss due to adverse customer reactions. The implementation will involve calculations of projected changes in sales volume, revenue estimations, and visual representations of data that aid in strategic decision-making.
Acceptance Criteria: Price Change Scenario Analysis for Electronics Segment

Given that a price change of 10% is applied to a popular electronics product, when the analysis is conducted, then the tool should accurately forecast a minimum of 15% decline in sales volume based on historical data and present this visually on the dashboard.

Multi-Demographic Price Sensitivity Visualization

Given that multiple demographic data sets are imported, when the Price Sensitivity Analyzer processes this data, then it must generate a comparative chart showing at least three distinct demographic responses to a 5% price increase, with results displayed within two minutes.

Integration with Customer Feedback Surveys

Given that customer feedback surveys are collected post-price change, when the analysis tool aggregates this feedback, then it should correlate at least 80% of the responses to the respective sales volume changes presented on the dashboard.

Predictive Analytics on Seasonal Price Changes

Given seasonal sales trends data is fed into the system, when a price analysis is run for the upcoming holiday season, then it should generate an estimated revenue impact report that suggests at least three pricing strategies to maximize revenue based on predicted customer reactions.

Real-Time Alert Generation for Significant Price Changes

Given a significant price change exceeding 15% is implemented, when this occurs, then the tool should trigger a real-time alert to marketing strategists within 5 minutes, highlighting potential risks based on previous customer behavior patterns.

Sales Forecast Accuracy Validation

Given historical sales data and proposed price changes are inputted, when the user requests a forecast, then the projected sales estimate must have a variance of no more than 10% from the actual sales data within the first month post-implementation.

User Dashboards for Strategic Decision-Making

Given that the user selects different price scenarios, when the dashboard is accessed, then it must allow the user to view customizable charts reflecting price impact on sales and revenue across different product categories, updating in real-time.

Real-Time Pricing Feedback

User Story: As a marketing strategist, I want to receive real-time feedback on customer reactions to price changes so that I can quickly adapt my pricing strategies to improve sales performance.
Description: The Real-Time Pricing Feedback requirement focuses on providing instant notifications and analytics regarding customer reactions to ongoing price changes. This feature will integrate with point-of-sale systems to instantly collect data on customer purchases and feedback, allowing strategists to monitor the effectiveness of pricing strategies as they are implemented. By capturing data in real time, the Price Sensitivity Analyzer can provide actionable insights, enabling timely adjustments to pricing strategies that align with customer expectations. The implementation will include API integrations with POS systems, a dashboard for monitoring real-time data, and a reporting mechanism for analyzing trends over time.
Acceptance Criteria: Customer purchases a product at a reduced price and provides feedback via an in-store survey about their purchase experience immediately after the transaction.

Given that a customer completes a purchase at a percentage discount, when they submit feedback through the in-store survey, then the system should capture and log this feedback in real-time within the analytics dashboard.

A marketer adjusts pricing for a specific product based on real-time data insights and needs to evaluate immediate customer response trends.

Given that a marketer updates the price of an item in the system, when the change is saved and active at the point of sale, then the Price Sensitivity Analyzer should show real-time updates in customer purchase behavior for that item within the analytics dashboard.

During a price change campaign, the strategy team needs to assess the volume of feedback received from different customer demographics within the first 24 hours of implementation.

Given that the price change has been active for 24 hours, when the team accesses the reporting mechanism, then they should see a comprehensive report detailing customer feedback segmented by demographic data (age, gender, income level).

The system integrates with Point-of-Sale (POS) to collect purchasing data in real-time during peak shopping hours when price changes are most effective.

Given that a POS transaction occurs, when a customer buys a product at the new price point, then the transaction data along with the corresponding feedback must be transmitted to the Price Sensitivity Analyzer without delays, ensuring data integrity and accuracy.

A weekly review meeting requires insights into the effectiveness of recent price adjustments across multiple product categories.

Given that the review meeting is scheduled, when the marketing team pulls up the analytics dashboard, then it should display a summary of price sensitivity results and trends over the last week, comparing these to previous pricing strategies implemented.

An administrator needs to check whether the real-time pricing feedback feature is functioning correctly during system maintenance.

Given that maintenance is being conducted on the system, when the administrator runs the health check on the real-time feedback API integrations with the POS, then the response time for data capture and processing must be within acceptable limits and show no errors in data logging.

A business analyst generates a monthly report on the correlation between pricing strategies and sales volume across demographics.

Given that the analysis is to be conducted, when the analyst specifies the parameters for the report, then the Price Sensitivity Analyzer should generate a detailed report showing correlations with clear visual representations of data trends, including any anomalies or significant findings.

Scenario Simulation Tool

User Story: As a marketing strategist, I want to simulate pricing scenarios to explore how changes could affect customer behavior and sales, so that I can make more informed and strategic pricing decisions.
Description: The Scenario Simulation Tool requirement empowers users to create hypothetical pricing scenarios and assess potential outcomes on customer behavior and sales performance. This feature will allow marketing strategists to test different pricing strategies, taking into account various market conditions, competitor prices, and customer demographics. By simulating multiple scenarios, strategists can utilize predictive analytics to forecast the effects of different pricing decisions before implementation. This will facilitate more informed decision-making and risk assessment. The successful integration of this feature will involve building a user interface for scenario creation, implementing backend analytical models, and establishing output reporting formats.
Acceptance Criteria: User creates a hypothetical pricing scenario adjusting the price of a product by 15% and selecting the target demographic as 'young adults'.

Given the user is on the Scenario Simulation Tool page, when they input a 15% price adjustment and select 'young adults' as the target demographic, then the tool should create a simulation for this scenario and display the expected customer response metrics on the dashboard.

Marketing strategists review the output report generated from a simulated scenario to assess sales performance impact.

Given the user successfully simulates a scenario, when they request the output report, then the system should generate a report that includes key performance indicators such as projected sales increase, customer drop-off rate, and demographic engagement scores.

User tests multiple pricing scenarios simultaneously to compare their potential outcomes.

Given the user has created at least three different pricing scenarios, when they choose to run a comparative analysis, then the system should allow the user to view a side-by-side comparison of the scenario outcomes on a single dashboard interface.

User modifies a pricing scenario after reviewing initial results and wants to re-simulate it.

Given that a user has previously simulated a scenario, when they change the parameters (e.g., price adjustment or demographic), then the system should allow the user to save or discard changes and re-run the simulation, updating the output metrics accordingly.

The system notifies users about significant changes in market conditions that may affect their pricing decisions.

Given real-time market data integration, when a significant competitor price drop or consumer trend change occurs, then the system should trigger a notification to users currently utilizing the Scenario Simulation Tool, suggesting a review of their current scenarios.

User receives assistance in understanding how to use the Scenario Simulation Tool effectively.

Given that the user is on the Scenario Simulation Tool page, when they click on the help icon, then the system should display a guided tutorial or FAQ section that walks them through the simulation process, including examples of effective pricing strategies.

The system should effectively handle errors when invalid inputs are entered by the user in the Scenario Simulation Tool.

Given the user enters an invalid value (e.g., a negative price adjustment), when they attempt to create a simulation, then the system should display an error message indicating the input error and prompt the user to correct it before proceeding.

Integrated Reporting Dashboard

User Story: As a marketing strategist, I want a comprehensive reporting dashboard to view all relevant analytics in one place so that I can quickly access insights and report findings to my team.
Description: This requirement entails creating an Integrated Reporting Dashboard that consolidates insights from the Price Sensitivity Analyzer, allowing marketing strategists to view the latest analytics, segmented customer responses, and pricing strategies in a single interface. The dashboard will enhance decision-making by providing a clear overview of key performance metrics, trends, and actionable insights in an easy-to-understand format. The implementation involves designing the dashboard layout, defining metrics to be displayed, and ensuring connectivity with various data sources for real-time updates. This will facilitate efficient communication of insights among stakeholders and enable quick access to essential data.
Acceptance Criteria: Integrated Reporting Dashboard - Marketing Strategist Data Visualization

Given that the Integrated Reporting Dashboard has been fully developed, when a marketing strategist accesses the dashboard, then they should be able to view real-time analytics segmented by customer demographics and pricing strategies without delay.

Integrated Reporting Dashboard - Key Performance Metrics Display

Given that the Integrated Reporting Dashboard is designed, when a user opens the dashboard, then it should display the key performance metrics related to price sensitivity including average purchase behavior, promotional response rates, and demographic preferences, all updated in real-time.

Integrated Reporting Dashboard - Data Source Connectivity

Given that the Integrated Reporting Dashboard requires multiple data sources, when data sources are connected, then the dashboard should refresh its data automatically every 10 minutes to ensure that the information displayed is current and accurate.

Integrated Reporting Dashboard - User Interface Usability Testing

Given that the Integrated Reporting Dashboard is implemented, when usability testing is conducted, then at least 90% of users should be able to navigate the dashboard and find the required insights within 2 minutes without assistance.

Integrated Reporting Dashboard - Alert System Functionality

Given that the Integrated Reporting Dashboard features an alert system, when a significant change in price sensitivity metrics is detected, then the system should send real-time alerts to marketing strategists via email and dashboard notifications within 5 minutes.

Integrated Reporting Dashboard - Performance on Various Devices

Given that the Integrated Reporting Dashboard is a cloud-based application, when accessed from a mobile device, tablet or desktop, then it should maintain consistent performance and visibility of insights across all devices.

Real-Time Profit Impact Dashboard

Real-Time Profit Impact Dashboard provides instant visualizations that connect pricing changes with profit outcomes. This feature allows Marketing Strategists to rapidly assess the financial viability of pricing strategies, facilitating quicker and more informed decisions that drive profitability and market responsiveness.

Requirements

Dynamic Pricing Visualization

User Story: As a Marketing Strategist, I want to visualize the impact of different pricing strategies on profits in real time so that I can make informed decisions quickly and adjust pricing to maximize profitability.
Description: The Dynamic Pricing Visualization requirement is focused on developing an interactive visualization tool that illustrates how different pricing strategies impact profit margins in real time. This feature will enable marketing strategists to input various pricing scenarios and immediately see projected financial outcomes, integrating seamlessly with the existing data analytics tools within BeaconLyte. By emphasizing clarity and usability, this tool is designed to empower users to make quick and informed pricing decisions, ultimately contributing to strategic pricing adjustments that enhance profitability and market responsiveness. The visualization will support multiple graphical formats to cater to diverse analytical needs, making complex data accessible and actionable.
Acceptance Criteria: Dynamic Pricing Visualization is used by Marketing Strategists to input different pricing scenarios and analyze their impact on profit margins during a Weekly Marketing Strategy Meeting.

Given a user in the Marketing Strategists role, When they input a pricing scenario and select 'Analyze', Then the dashboard should display projected profit margins for each pricing option in under 2 seconds.

During a live demonstration of Dynamic Pricing Visualization to potential clients, a Marketing Strategist showcases how real-time data impacts pricing decisions.

Given a pricing change is input into the system, When the user requests an immediate profit impact report, Then the system should generate a visual report that accurately reflects the changes within the current financial data set.

Post-implementation user feedback session held with Marketing Strategists exploring usability and clarity of the Dynamic Pricing Visualization tool.

Given the tool has been implemented, When a group of marketing strategists tests the tool, Then at least 80% of users should indicate that the visualization is intuitive and informative during the user feedback survey.

After integrating the Dynamic Pricing Visualization with the existing analytics tools in BeaconLyte, the effectiveness of the integration is evaluated.

Given that necessary API connections have been established, When users access the Dynamic Pricing Visualization feature, Then the feature should successfully pull data from at least three other BeaconLyte analytics tools without any delays or errors.

Dynamic Pricing Visualization is utilized across different devices (desktop, tablet, and mobile) to ensure consistent performance for Marketing Strategists.

Given a user accesses the dynamic pricing tool on any device, When they input pricing scenarios and view visualizations, Then the functionality and visuals should be consistent with less than a 5% variation across all devices.

Training session conducted for Marketing Strategists to ensure they can effectively use the Dynamic Pricing Visualization tool.

Given a training session on the Dynamic Pricing Visualization tool is completed, When assessed through a follow-up quiz, Then at least 90% of participants should score 80% or above, demonstrating understanding and capability to use the tool.

Profit Margin Alert System

User Story: As a Marketing Strategist, I want to receive real-time alerts when profit margins change significantly due to pricing adjustments so that I can take immediate corrective actions to maintain profitability.
Description: The Profit Margin Alert System is a critical requirement that establishes a real-time alert mechanism to notify marketing strategists of significant changes in profit margins as a result of pricing adjustments. This feature will automatically analyze pricing data and profit margin fluctuations, sending alerts via email or in-app notifications when predefined thresholds are crossed. The system will integrate with the existing analytics framework of BeaconLyte, leveraging AI-driven insights to ensure timely and accurate alerts. By enabling proactive management, this requirement aims to facilitate immediate corrective actions to maintain profitability and ensure market competitiveness.
Acceptance Criteria: Notification System during High Volume Pricing Changes

Given a significant increase or decrease in profit margins due to pricing changes, when the pricing adjustment occurs, then the system should send an email and an in-app notification to the marketing strategist within 5 minutes of detecting the margin change.

Threshold Configuration for Alerts

Given the marketing strategist accesses the Profit Margin Alert System settings, when they set a predefined threshold for profit margin changes, then the system should allow for thresholds to be set in increments of 1% and save these settings without errors.

Accuracy of Alerting Mechanism

Given that pricing data changes impact profit margins, when the Profit Margin Alert System analyzes the data, then it must accurately reflect at least 95% of real-time profit margin status against actual profit margins calculated at the end of the day.

Integration with Existing Analytics Framework

Given that the Profit Margin Alert System needs to function within the BeaconLyte platform, when the system is implemented, then it must integrate seamlessly with existing dashboards and provide alerts without affecting performance or requiring additional user actions.

Historical Data Reference for Alert Validation

Given the Profit Margin Alert System has been in place for a full month, when reviewing the alerts generated, then 90% of the alerts should correspond to actual profit margin shifts observed in historical data during that period.

User-Friendly Alert Management Interface

Given that a marketing strategist needs to manage alerts, when accessing the alert management interface, then it must provide options to view, filter, and categorize alerts easily without requiring more than three clicks.

Predictive Profit Analytics

User Story: As a Marketing Strategist, I want to use predictive analytics to forecast the profitability of future pricing strategies based on historical data so that I can create better-informed pricing decisions.
Description: The Predictive Profit Analytics requirement is aimed at enhancing the existing capabilities of BeaconLyte by incorporating predictive modeling techniques that forecast the profitability of future pricing strategies based on historical data. This feature will allow users to simulate various pricing scenarios and visualize potential profit outcomes, incorporating key variables such as market trends, seasonality, and customer behavior. By providing deep insights into future profit potentials, this predictive capability will assist marketing strategists in developing data-driven pricing strategies that are more likely to succeed, thus driving overall business growth.
Acceptance Criteria: Marketing Strategist inputs various pricing scenarios into the Predictive Profit Analytics feature to assess potential profitability based on historical data, market trends, and customer behavior.

Given the marketing strategist has inputted a pricing scenario, when they request a profitability forecast, then the system should generate a report that includes predicted profit outcomes for each pricing option based on historical data and relevant variables.

A marketing strategist uses the Predictive Profit Analytics feature during a team meeting to present potential pricing strategies to stakeholders.

Given the marketing strategist is presenting the predictive analytics results, when they display the profitability forecast dashboard, then all displayed figures should correctly reflect the input scenarios and incorporate real-time market data analysis.

The marketing team conducts a quarterly review of pricing strategies using the Predictive Profit Analytics feature to adjust their future pricing decisions based on past performance and predictive insights.

Given the marketing team is reviewing the quarterly profit reports, when they access the Predictive Profit Analytics feature, then they should be able to generate a comparative analysis of past pricing strategies and their actual profitability versus predicted profitability.

A marketing strategist utilizes the Predictive Profit Analytics feature to run simulations for different customer segments and their response to pricing changes.

Given the user has selected specific customer segments and proposed pricing changes, when they run the simulation, then the system should output segmented profit forecasts and customer behavior insights for each scenario.

The system needs to alert the marketing strategist on significant deviations between predicted profits and actual results after a pricing change is implemented.

Given that a pricing strategy has been executed, when the predicted profit deviates from actual profit by a defined threshold, then the system should trigger an alert notification to the marketing strategist.

Customizable Dashboard Widgets

User Story: As a Marketing Strategist, I want to customize my dashboard with specific widgets that display relevant profit data so that I can easily access the information I need for decision-making.
Description: The Customizable Dashboard Widgets requirement involves creating modular dashboard components that allow marketing strategists to select, arrange, and personalize data visualizations related to profit impacts from pricing strategies. These widgets will be designed to display key performance indicators such as profit margins, sales volume, and customer engagement metrics in a visually appealing and user-friendly manner. By enabling users to tailor their dashboards to their specific analytical needs, this feature enhances user experience and allows for quicker access to relevant data, thereby improving decision-making processes.
Acceptance Criteria: User selects specific widgets to display on their dashboard for analyzing profit impacts.

Given a logged-in marketing strategist, when they access the Customizable Dashboard Widgets feature, then they should be able to select from a list of available widgets and add them to their dashboard layout successfully.

User rearranges existing widgets on their dashboard to prioritize the most relevant data.

Given a dashboard with multiple widgets, when the user clicks and drags a widget to a new position, then the widget should update its position accordingly without refreshing the webpage.

User personalizes the data shown in a specific widget to align with their business goals.

Given a specific widget on the dashboard, when the user selects from data source options, then the widget should update to reflect the chosen data sources in real-time.

User removes an unneeded widget from their dashboard to reduce clutter.

Given a widget on the dashboard, when the user clicks the remove button, then the widget should be removed from the dashboard without affecting any other widgets or data displayed.

User saves their customized dashboard layout for future use.

Given a personalized dashboard, when the user clicks on the save button, then their dashboard configuration and widgets should be saved and retrievable upon next login.

User views real-time updates in dashboard widgets as pricing strategies are modified.

Given an active dashboard, when changes are made to pricing strategies in the system, then the relevant widgets should reflect updated metrics within a 5-second interval.

User accesses the dashboard from different devices and sees a consistent layout.

Given a saved dashboard layout on one device, when the user logs into the same account on another device, then the dashboard should display the same arrangement and selected widgets as previously saved.

Collaborative Strategy Planning Tool

User Story: As a Marketing Strategist, I want to collaborate with my team in real-time on pricing strategies and profit outcomes so that we can develop more effective pricing decisions together.
Description: The Collaborative Strategy Planning Tool requirement focuses on building a feature that facilitates collaboration among marketing strategists by enabling them to share insights, pricing strategies, and profitable outcomes with team members in real time. This tool will include functionalities such as shared dashboards, comments, and annotations, which will allow for dynamic discussions around potential pricing changes. By fostering teamwork and transparency, this requirement aims to enhance collaborative decision-making within the organization, ultimately driving better strategies aligned with profit optimization goals.
Acceptance Criteria: Marketing strategists are collaborating in a virtual meeting to discuss upcoming pricing strategies for a new product launch. They intend to use the Collaborative Strategy Planning Tool to view shared dashboards and make annotations in real time.

Given that a marketing strategist has shared a dashboard with team members, when a team member adds a comment or annotation, then all other team members should receive an immediate notification of this addition, ensuring real-time collaboration.

During a live strategy session, a marketing strategist wants to present data on the profitability of several pricing strategies using the Collaborative Strategy Planning Tool to facilitate group discussion.

Given that a marketing strategist is presenting a shared dashboard, when they switch between different pricing strategies on the dashboard, then the system should update the visualizations in less than 2 seconds to reflect the selected strategy, ensuring a smooth presentation.

After finalizing a new pricing strategy, the marketing team will review the comments and insights recorded in the Collaborative Strategy Planning Tool to refine their approach before presentation to upper management.

Given that pricing strategies have been discussed and annotated, when the marketing strategists generate a summary report from the tool, then the report should include all comments and insights from the previous discussions, formatted clearly for presentation to upper management.

A marketing strategist is using the Collaborative Strategy Planning Tool to assess the impact of recent pricing changes on profitability in collaboration with team members during a strategy meeting.

Given that the team is reviewing the dashboard analytics, when the pricing change is highlighted, then all relevant profitability metrics and analytics should update instantly, allowing for real-time decision-making.

Team members need to track historical changes made to pricing strategies within the Collaborative Strategy Planning Tool to evaluate effectiveness and make informed adjustments.

Given that a pricing strategy has been adjusted, when team members access the change history feature, then they should be able to view a complete log of all changes made, including dates, author names, and previous values, within 3 seconds.

In order to enhance collaboration, the marketing team wants to ensure that all functionalities of the Collaborative Strategy Planning Tool are accessible on both desktop and mobile devices.

Given that a marketing strategist is using the Collaborative Strategy Planning Tool, when they access the tool from a mobile device, then all features, including shared dashboards and commenting functionalities, should work seamlessly with no loss in performance or usability.

KPI Alert System

The KPI Alert System proactively notifies Store Operations Managers of significant deviations from established benchmarks, allowing for immediate intervention. This feature enhances responsiveness by delivering real-time alerts via push notifications or emails, ensuring teams can address issues swiftly and maintain operational performance.

Requirements

Real-Time KPI Tracking

User Story: As a Store Operations Manager, I want to see real-time updates of key performance indicators so that I can make informed decisions quickly and maintain optimal operations.
Description: The Real-Time KPI Tracking requirement enables the KPI Alert System to continuously monitor key performance indicators (KPIs) in real-time. This feature ensures that the data feeds are consistently updated, allowing Store Operations Managers to have the most current insights regarding performance metrics. The benefit of implementing this requirement is to minimize lag time in data reporting and to empower the team with up-to-date information for timely decision-making. It plays a crucial role in operational efficiency by providing immediate visibility into performance metrics, which helps in identifying trends and addressing issues proactively.
Acceptance Criteria: Real-time monitoring of sales KPIs during peak hours to adjust inventory levels accordingly.

Given the system is operational, when sales data is recorded, then sales KPI updates should be delivered in real-time without delays.

Generating alerts when customer satisfaction scores drop below a predefined threshold.

Given the satisfaction score is being monitored, when the score drops below the threshold, then an immediate alert should be sent to Store Operations Managers via push notification and email.

Tracking inventory turnover rates and notifying when inventory levels are critically low.

Given the inventory levels are being tracked, when the turnover rate indicates low stock, then a notification should be triggered informing the Store Operations Manager.

Continuous tracking of merchandise return rates and flagging unusual spikes.

Given the returns data is active, when return rates exceed the established threshold, then

Create products with ease

Product Details

Personas

Data-Driven Debbie

Insightful Ivan

Supply Chain Samantha

Customer-Centric Carl

Product Ideas

Product Features

Stock Health Indicator

Requirements

Real-Time Inventory Monitoring

Predictive Stock Analytics

Slow-Moving Item Alerts

Customizable Dashboard Widgets

Integration with Supply Chain Management Systems

Historical Performance Reports

Restock Recommendations

Requirements

AI-Powered Sales Analysis

Customizable Reorder Alerts

Integration with Current Systems

Dynamic Stock Level Recommendations

User-Friendly Dashboard for Restocking Insights

Seasonal Demand Alerts

Requirements

Historical Data Analysis

Real-Time Notification System

Customizable Alert Settings

Integration with Supply Chain Systems

Comprehensive Reporting Dashboard

Supplier Performance Tracker

Requirements

Real-Time Supplier Analytics

Automated Reorder Alerts

Supplier Performance Dashboard

Supplier Scorecard Generation

Historical Supplier Data Analysis

Integration with ERP Systems

Multi-Location Inventory Monitor

Requirements

Real-Time Stock Level Updates

Customizable Alerts for Low Stock

Centralized Dashboard for Overview

Stock Redistribution Recommendations

Historical Data Analysis Tool

Custom Alert Thresholds

Requirements

Dynamic Threshold Configuration

User-Friendly Alert Management Interface

Historical Alert Performance Analytics

Multi-Channel Alert Notifications

Customizable Alert Categories

Inventory Turnover Visualizer

Requirements

Real-time Inventory Analytics

Customizable Dashboards

Predictive Turnover Forecasting

Alerts for Low Turnover Rates

Data Integration with Existing ERP Systems

Touchpoint Tracker

Requirements

Real-time Interaction Analytics

Touchpoint Performance Dashboard

Automated Touchpoint Alerts

Historical Touchpoint Data Analysis

Multichannel Integration Capability

User Feedback Loop for Touchpoint Improvement

Path Analyzer

Requirements

Customer Journey Mapping

Path Effectiveness Metrics

Interactive Path Exploration

Integration with Existing Systems

Automated Reporting

Engagement Scoring

Requirements

Dynamic Engagement Weighting

Real-time Engagement Dashboard