Customer Experience Software

PulsePanel

Spot Risks. Save Customers. Instantly.

PulsePanel equips SaaS customer success managers with instant, unified client feedback by centralizing frontline reports and auto-tagging issues as they arise. Teams pinpoint and resolve 37% more recurring problems before escalation, reducing churn risk by 21%—transforming scattered tickets into immediate, actionable insights that keep customers satisfied and loyal.

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PulsePanel

Product Details

Explore this AI-generated product idea in detail. Each aspect has been thoughtfully created to inspire your next venture.

Vision & Mission

Vision
To empower every SaaS success team to predict and resolve customer pain points before they threaten satisfaction or retention.
Long Term Goal
By 2028, empower 5,000 SaaS success teams to proactively cut customer churn by 20%, transforming real-time client insights into industry-leading retention and customer loyalty.
Impact
Enables SaaS customer success teams to detect and resolve 37% more recurring client issues before escalation, reducing customer churn risk by 21% within six months and improving retention rates, directly addressing delayed issue detection caused by scattered feedback and slow survey-based processes.

Problem & Solution

Problem Statement
Customer success managers at mid-sized SaaS firms miss early signs of recurring client issues because crucial frontline feedback is buried in scattered emails and tickets; slow surveys and outdated dashboards fail to provide timely, actionable insights to prevent churn.
Solution Overview
PulsePanel centralizes frontline client feedback with one-click logging and auto-tagging, giving SaaS customer success managers instant trend analytics as issues emerge. This immediate visibility allows teams to spot and resolve recurring problems before they escalate into churn risks, eliminating delays from surveys or scattered ticket systems.

Details & Audience

Description
PulsePanel gives SaaS customer success teams real-time visibility into emerging client issues. Designed for managers at firms with 20-500 employees, it centralizes frontline feedback with one-click logging and auto-tagging. Teams spot 37% more recurring problems before escalation, reduce churn risk by 21%, and take targeted action instantly—unlike lagging surveys or scattered tickets, PulsePanel delivers instant trend analytics as issues unfold.
Target Audience
Customer success managers (30-50) at SaaS firms urgently surfacing client issues to reduce churn.
Inspiration
Late one night, an urgent email landed: a major client was leaving after months of an unresolved bug. Digging into the notes, I saw their pain points buried in scattered tickets and forgotten surveys—signals missed until it was too late. That loss made me realize: true customer rescue demands instant, unified feedback. PulsePanel was sparked to prevent those silent departures.

User Personas

Detailed profiles of the target users who would benefit most from this product.

I

Integration Innovator Ian

- Age 34, solutions architect at mid-size SaaS - Master’s in Computer Science, technical background - Manages $200K+ integration projects annually - Based in Austin, hybrid work schedule

Background

Ian began as a software developer before pivoting to system integrations, where he led API deployments for global clients. His hands-on work with disparate data sources taught him the value of reliable automated feedback pipelines.

Needs & Pain Points

Needs

1. Automated API connectivity without manual code adjustments 2. Unified error logging across multiple data endpoints 3. Clear documentation for feedback integration processes

Pain Points

1. Frequent API schema changes breaking data pipelines 2. Disconnected feedback causing reporting blind spots 3. Manual tagging errors delaying issue resolution

Psychographics

- Obsessive about eliminating data silos - Energized by streamlining complex workflows - Values precision in automated processes - Craves immediate system feedback loops

Channels

1. GitHub issues – integration track 2. Slack integrations – real-time alerts 3. Postman workspace – API testing 4. Dev.to forums – technical discussions 5. LinkedIn groups – architecture insights

R

Renewal Ranger Ryan

- Age 29, renewals manager at SaaS company - Bachelor’s in Business Administration - Manages $5M annual renewal portfolio - Located in New York, fully remote

Background

Ryan started in customer support, then moved to renewals after mastering customer pain patterns. He leverages data-driven insights to preempt churn, collaborating closely with sales and finance teams.

Needs & Pain Points

Needs

1. Consolidated churn risk metrics for timely outreach 2. Pre-packaged retention narratives with feedback examples 3. Automated reminders for upcoming renewals

Pain Points

1. Last-minute churn alerts compromising outreach success 2. Difficulty correlating feedback with renewal timelines 3. Incomplete data hindering ROI presentations

Psychographics

- Driven by customer retention targets - Thrives on persuasive, data-backed pitches - Values transparent ROI reporting - Motivated by exceeding renewal quotas

Channels

1. Salesforce – renewal tracking 2. Zoom – video calls 3. Microsoft Teams – collaboration chats 4. Gmail – email communications 5. LinkedIn Sales Navigator – prospect insights

S

Support Sleuth Sam

- Age 32, Tier-2 support engineer at SaaS - 4 years of technical support experience - Computer Science degree, troubleshooting expert - Based in Dublin, flexible schedule

Background

Sam began in help desk roles, tackling diverse product issues before specializing in deep-dive analysis. His knack for pattern recognition led him to champion proactive fixes and share insights across teams.

Needs & Pain Points

Needs

1. Advanced filters to isolate recurring ticket patterns 2. Context-rich logs with complete incident histories 3. Quick access to historical resolution details

Pain Points

1. Scattered ticket fragmentation obscuring root causes 2. Missing context forcing repeated customer inquiries 3. Slow search hampering urgent analysis tasks

Psychographics

- Analytical thinker solving complex puzzles - Passionate about proactive issue prevention - Values comprehensive, accurate incident data

Channels

1. Zendesk – ticket analysis 2. Confluence – knowledge base reference 3. Slack – support channel queries 4. Jira – bug tracking 5. Email – detailed incident updates

E

Executive Echo Emma

- Age 45, VP of Customer Success at enterprise - 15+ years in customer experience leadership - MBA in strategic planning - Headquarters in London, frequent global travel

Background

Emma rose through CS ranks at a Fortune 500, shaping customer programs for international markets. She integrates multi-source feedback into strategic plans, steering cross-functional teams toward retention goals.

Needs & Pain Points

Needs

1. Executive dashboards with top-line feedback trends 2. Customizable reports for board presentations 3. Real-time alerts for emerging strategic risks

Pain Points

1. Overly detailed reports obscuring key messages 2. Delayed data slowing strategic decision-making 3. Inconsistent metrics across departments

Psychographics

- Visionary leader prioritizing big-picture insights - Demands data-driven strategic direction - Values succinct, impactful presentation visuals

Channels

1. Tableau – executive dashboards 2. PowerPoint – board presentations 3. Slack – leadership channels 4. Email – executive summaries 5. Zoom – virtual boardrooms

L

Learning Liaison Laura

- Age 28, training specialist at SaaS startup - BA in Instructional Design - Oversees 300+ trainees yearly - Remotely based in Manila

Background

Laura started as a support rep, then transitioned into training after designing peer-led workshops. Her hands-on experience drives her to tailor learning paths around actual customer pain points.

Needs & Pain Points

Needs

1. Access to frequent feedback trend snapshots 2. Edit-friendly export of tagged issue examples 3. Seamless integration with LMS platform

Pain Points

1. Manual extraction of examples slows content creation 2. Outdated trends leading to irrelevant training 3. Lack of LMS integration increasing workload

Psychographics

- Enthusiastic educator valuing hands-on learning - Seeks continuous feedback for curriculum improvements - Believes in experiential, data-driven training

Channels

1. LMS (Moodle) – training modules 2. Google Docs – collaborative content 3. Slack – training feedback 4. Zoom – virtual workshops 5. PulsePanel notifications – feedback alerts

Product Features

Key capabilities that make this product valuable to its target users.

Threshold Tuner

Dynamically adjusts alert thresholds based on historical feedback patterns and seasonality. This ensures alerts remain relevant and reduces noise by adapting to normal fluctuations, helping teams focus on genuine spikes.

Requirements

Dynamic Threshold Calculation
"As a customer success manager, I want the system to automatically calculate alert thresholds based on historical feedback patterns so that I can focus on genuine issues without manually tuning settings."
Description

Analyzes historical client feedback data to calculate adaptive alert thresholds in real time, adjusting dynamically to emerging patterns and trends, thereby reducing false positives and ensuring that alerts stay relevant under varying conditions.

Acceptance Criteria
Post-Release Feedback Surge Adjustment
Given historical feedback data shows a ≥50% increase in daily feedback volume over the past 7 days compared to the prior 7-day average, when the dynamic threshold calculation runs, then the alert threshold is increased by at least 30% above baseline but not exceeding 70%, and false positive rate remains below 5% during this period.
Holiday Season Baseline Calibration
Given historical feedback patterns indicate a consistent 25% volume drop during holiday periods over the past two years, when computing the dynamic threshold, then the system lowers the threshold by approximately 20–30% relative to the annual baseline to avoid missing genuine spikes.
Weekly Pattern Threshold Stabilization
Given a recurring weekly pattern with ±10% fluctuation in daily feedback over the last 4 weeks, when threshold recalculation occurs at the start of each week, then the threshold varies within a ±15% band of the 4-week moving average to minimize noise.
Campaign-Induced Spike Detection
Given an observed 40% spike in feedback volume linked to a marketing campaign, when the dynamic threshold is recalculated, then the threshold temporarily elevates by 25–35% to filter expected spikes, and reverts to standard levels within 48 hours post-campaign.
Noise Suppression in Low-Volume Periods
Given periods with feedback volumes below the 20th percentile of historical data, when dynamic thresholds are applied, then the minimum alert threshold floor is enforced at the 20th percentile value to prevent alerts on trivial fluctuations.
Seasonality Detection Module
"As a customer success manager, I want the system to account for seasonal variations in feedback so that alerts are accurate during both high-activity and slow periods."
Description

Implements advanced seasonality detection algorithms to identify periodic trends and recurring fluctuations in client feedback, enabling the system to adjust alert thresholds in advance of expected peaks and troughs.

Acceptance Criteria
Historical Data Seasonal Pattern Identification
Given at least 12 months of client feedback data, when the Seasonality Detection Module is initialized, then it accurately identifies recurring monthly peaks and troughs with a confidence level of at least 90%.
Peak Season Threshold Adjustment
Given identified seasonal peaks, when the upcoming peak period approaches, then the system automatically increases alert thresholds by the calculated seasonal index and logs the adjustment for review.
Off-Season Anomaly Alerting
Given normalized seasonal thresholds, when feedback volume exceeds the adjusted threshold outside of predicted peak periods, then an alert is triggered within 5 minutes for customer success review.
Monthly Model Retraining
Given a rolling window of the last 12 months of feedback data, when the end of each calendar month occurs, then the module retrains the seasonality model and completes processing within 30 minutes without performance degradation over 5%.
Threshold Adjustment Notification
Given any automatic threshold adjustment, when the system applies the new threshold, then a notification is sent to the assigned CSM within 10 minutes including the adjustment details and rationale.
Noise Reduction Engine
"As a customer success manager, I want the system to filter out trivial feedback fluctuations so that I’m alerted only about significant issues."
Description

Builds a noise reduction engine that filters out minor or insignificant spikes by comparing current feedback metrics against dynamically established baselines, thereby minimizing alert fatigue and highlighting only critical anomalies.

Acceptance Criteria
Baseline Establishment for New Feedback Streams
Given the system has received historical feedback data for a new client, when the noise reduction engine processes the data, then it establishes a dynamic baseline within a 5% variance range of historical averages and stores it for threshold comparisons.
Filtering of Minor Feedback Spikes
Given a feedback volume spike within the dynamic baseline variance range, when the noise reduction engine evaluates incoming metrics, then the spike is not flagged as an alert and no notification is generated.
Detection and Alerting of Critical Anomalies
Given a feedback metric spike exceeding 150% of the dynamic baseline, when the noise reduction engine processes the metric, then the system flags the event as a critical anomaly and triggers an alert notification to the customer success manager within 30 seconds.
Adaptive Threshold Adjustment Based on Seasonality
Given recurring seasonal fluctuations identified over the past three seasons, when the noise reduction engine detects these patterns, then it adjusts the dynamic baseline thresholds to accommodate a 20% increase in normal variation during the identified period.
Historical Pattern Re-evaluation Post Baseline Update
Given one month has passed since the last baseline update, when the noise reduction engine re-evaluates historical feedback data, then it recalculates and updates the dynamic baseline within a 3% accuracy margin compared to actual data trends.
User Override Controls
"As a customer success manager, I want to manually override automatically tuned thresholds so that I can respond immediately to unique or critical situations."
Description

Provides a user interface allowing CSMs to manually adjust, lock, or reset alert thresholds, offering control over sensitivity settings, with audit logging to track changes and revert to automated defaults as needed.

Acceptance Criteria
Manual Threshold Adjustment
Given a CSM is viewing the Threshold Tuner interface, When the CSM inputs a new minimum or maximum alert threshold value and clicks “Save,” Then the system updates the threshold values immediately and displays a confirmation message.
Threshold Locking
Given a CSM has set a custom alert threshold, When the CSM toggles the “Lock Threshold” option on and confirms, Then the system prevents automated processes from modifying that threshold until it is unlocked.
Threshold Reset to Automated Defaults
Given a CSM has overridden a threshold and locked it, When the CSM clicks “Reset to Default” and confirms, Then the system reverts the threshold to the dynamically calculated value and unlocks it.
Audit Log Entry Creation
Given any manual adjust, lock, or reset action is performed, When the action is saved, Then the system creates an audit log entry recording the user ID, timestamp, previous value, new value, and action type.
Unauthorized Access Prevention
Given a user without override permissions attempts to adjust thresholds, When they try to save changes, Then the system displays an “Insufficient Permissions” error and does not apply the change.
Real-time Visualization Dashboard
"As a customer success manager, I want a real-time visualization of feedback patterns and threshold adjustments so that I can assess the effectiveness of the tuning feature at a glance."
Description

Develops a dashboard that visualizes real-time feedback trends, current threshold levels, and alert triggers, enabling teams to monitor the performance of dynamic threshold tuning and quickly interpret the impact of tuning adjustments.

Acceptance Criteria
Real-time Feedback Trend Loading
Given the dashboard is opened, When live feedback data streams in, Then the trend graph updates to reflect new feedback counts within 60 seconds of data arrival.
Dynamic Threshold Level Display
Given historical feedback patterns are analyzed, When the threshold tuner adjusts levels, Then the dashboard displays the updated threshold values alongside current feedback trends.
Alert Trigger Visualization
Given feedback counts exceed dynamic thresholds, When an alert is triggered, Then the dashboard highlights the affected metric and logs the timestamp of the alert.
Historical Data Overlay
Given a user selects a historical date range, When the range is applied, Then the dashboard overlays past feedback trends with current data for comparison.
Dashboard Performance Under Load
Given high-volume feedback inflow, When the system processes thousands of events per minute, Then the dashboard maintains refresh rates under 60 seconds without errors.

MultiChannel Dispatch

Sends alerts simultaneously via mobile push, SMS, email, or chat platforms like Slack. By meeting users on their preferred channels, it guarantees critical notifications are seen and acted upon immediately.

Requirements

Channel Integration Setup
"As an administrator, I want to easily connect and authenticate our communication channels in PulsePanel so that alerts can be dispatched seamlessly without manual intervention."
Description

Enable administrators to configure and connect PulsePanel to multiple communication channels (mobile push, SMS, email, Slack, Teams) through a unified interface. This requirement includes secure authentication, API key management, and test connectivity for each channel. It ensures that alerts can be sent reliably across all configured platforms, reducing manual setup overhead and accelerating time to notify stakeholders.

Acceptance Criteria
Configure Email Channel
Given the administrator navigates to the Channel Integration Setup page and selects Email option; When they enter a valid SMTP server, port, username, and password; Then the system saves the configuration and displays a confirmation message "Email channel connected successfully".
Validate SMS API Key
Given the administrator selects SMS channel and inputs an API key; When they click Test Connection; Then the system performs a live API call and displays a success or detailed error message within 5 seconds.
Authenticate Slack Workspace
Given the administrator clicks Connect Slack; When they complete the OAuth consent in the Slack pop-up; Then the system stores the access token securely and shows "Slack integration successful" notification.
Authenticate Microsoft Teams Channel
Given the administrator selects Teams and initiates OAuth; When the OAuth flow completes without error; Then the system records the token securely and updates the integration status to "Connected".
Send Test Push Notification
Given the administrator has configured the Mobile Push channel and provided valid API credentials; When they click Send Test Notification; Then the system sends a push notification to the administrator's registered device within 10 seconds and logs it as "Delivered" in the dashboard.
Alert Template Customization
"As a customer success manager, I want to customize alert templates for different channels so that messages are concise and formatted correctly for each medium."
Description

Provide a templating engine that allows users to design and customize alert messages per channel, including variables for client name, issue details, and urgency level. Templates should support rich formatting, channel-specific constraints (e.g., SMS character limits), and preview functionality. This enables consistent branding and clear messaging across all notification mediums.

Acceptance Criteria
Channel-Specific SMS Template Character Limit
Given a user designs an SMS template with variables {{client_name}} and {{issue_detail}}, when they attempt to save the template longer than 160 characters after placeholder expansion, then the system prevents saving and displays a warning about exceeding the SMS character limit.
Rich Formatting in Email Templates
Given a user applies bold, italics, hyperlinks, and images in the email template editor, when they send a test email to a designated address, then the received message preserves all specified rich formatting without distortion.
Variable Placeholder Rendering in Push Notifications
Given a push notification template containing {{client_name}}, {{urgency_level}}, and {{ticket_id}}, when a notification is triggered for Acme Corp with urgency High and ticket 1234, then the delivered notification displays “Acme Corp,” “High,” and “1234” correctly in their respective positions.
Template Preview Functionality
Given a user edits any channel template, when they click the Preview button, then the system renders a real-time preview using sample data for each variable and flags any formatting or constraint violations.
Branding Consistency Across Chat Platforms
Given a user configures templates for Slack and Microsoft Teams including company logo and brand colors, when a test message is dispatched to both platforms, then each message displays the logo and colors in compliance with each platform’s formatting rules.
Delivery Retry Mechanism
"As a system user, I want failed notifications to retry automatically so that I can be confident important alerts will be delivered."
Description

Implement an automatic retry strategy for failed notifications, with configurable retry intervals and maximum attempts. The system should log failures, escalate after threshold breaches, and provide visibility into retry status. This ensures critical alerts reach recipients even in case of transient network issues or service outages.

Acceptance Criteria
Transient Network Failure Retry
Given a transient network failure occurs during notification delivery When the system initiates a retry After each failure at configured intervals Then the system successfully re-delivers the notification within the maximum retry attempts
Maximum Retry Attempts Escalation
Given a notification has failed delivery for the maximum allowed retry attempts When the retry threshold is reached Then the system logs the failure, escalates the alert to the defined escalation channel, and ceases further retry attempts
Configurable Retry Interval Application
Given retry intervals are configured in the system settings When a notification delivery fails Then the system waits for the specified interval before each retry, adhering precisely to the configured timing
Retry Attempt Logging
Given a retry attempt occurs When the system processes a retry Then it logs the timestamp, attempt number, target channel, and error details for each retry attempt
Retry Status Visibility in Dashboard
Given a user views the notification status dashboard When a retry is in progress or completed Then the dashboard displays real-time retry status including number of retries attempted, next retry schedule, and final delivery status
Real-time Channel Status Monitoring
"As a support engineer, I want to monitor channel health in real time so that I can address outages or delays before they affect end users."
Description

Create a dashboard displaying the live status of each dispatch channel, including success rates, current outages, and latency metrics. Alerts should trigger when a channel falls below defined performance thresholds, enabling rapid remediation. This feature maintains reliability by proactively identifying issues before they impact notification delivery.

Acceptance Criteria
Channel Status Dashboard Visibility
Given a logged-in user accesses the Real-time Channel Status Monitoring dashboard, When the dashboard loads successfully, Then the current operational status (active, degraded, or down) for each dispatch channel is displayed.
Threshold-based Alert Generation
Given performance metrics for a channel fall below predefined thresholds (success rate < 95% or latency > 2 seconds for five consecutive checks), When monitoring detects this condition, Then an alert appears on the dashboard and a notification is sent to the designated Slack channel within one minute.
Real-time Latency Metric Tracking
Given the dashboard is active, When new latency data arrives at one-minute intervals, Then the dashboard updates the displayed latency metrics for each channel in real time without requiring a manual refresh.
Success Rate Historical Overview
Given a user selects a time-range filter (e.g., last 24 hours), When the filter is applied, Then the dashboard displays the success rate trends for each channel over the selected period using graphical charts.
Channel Outage Notification
Given a channel status changes to 'down' and remains down for at least two minutes, When this condition is met, Then the system automatically sends SMS and email notifications to the on-call team and highlights the channel in red on the dashboard.
User Channel Preference Management
"As an end user, I want to choose which channels I receive alerts on so that notifications come through in my preferred way."
Description

Allow end users to manage their preferred notification channels and opt in or out of specific alert types from their profile settings. Preferences should be stored securely and respected by the dispatch system. This empowers users to receive alerts via their most convenient channels, improving engagement and reducing notification fatigue.

Acceptance Criteria
Profile Settings Page Access
Given a logged-in user accesses the Profile Settings page When they navigate to Notification Preferences Then they see a list of all available channels (Mobile Push, SMS, Email, Slack) and alert types with current opt-in/out status
Channel Preference Update
Given the user is on the Notification Preferences page When they select or deselect one or more channels and click Save Then the system confirms the update and displays the new preference state
Notification Dispatch Respecting Preferences
Given a new critical alert is triggered for the user When the dispatch system runs Then notifications are sent only via channels the user has opted-in and none via channels opted-out
Opt-Out of Specific Alert Types
Given the user opts out of a specific alert type When that type of alert is generated Then the user does not receive any notifications for that alert type on any channel
Preference Security and Persistence
Given the user updates notification preferences When they refresh the page or log out and log back in Then the updated preferences persist correctly and are stored securely
Notification Analytics and Reporting
"As a product manager, I want detailed analytics on notification performance so that I can measure effectiveness and improve our alerting workflows."
Description

Develop analytics reporting that tracks dispatch metrics such as delivery rates, open/click rates for emails, read receipts for push notifications, and response times. Provide configurable reports and export capabilities. Insights derived will help optimize communication strategies and demonstrate ROI of the dispatch feature.

Acceptance Criteria
Email Dispatch Open and Click Rate Reporting
Given a user schedules an email dispatch campaign When the campaign completes Then the system displays the total emails sent, delivery rate ≥ 98%, open rate percentage, and click-through rate, and flags campaigns below KPI thresholds
Push Notification Read Receipt Tracking
Given push notifications are sent to mobile devices When users receive and read notifications Then the system logs read receipts within 5 minutes of read action and displays read rate per notification batch
SMS Delivery Success and Failure Rates Analysis
Given an SMS dispatch batch When messages are processed by carriers Then the system records delivery successes and failures, calculates delivery rate and failure reasons, and generates a summary report
Slack Alert Response Time Metrics
Given alerts are sent to Slack channels When users respond via Slack message Then the system captures response timestamps, calculates average response time per alert, and highlights delays exceeding SLA of 10 minutes
Configurable Report Export Functionality
Given users configure report parameters When users export analytics report Then the system generates a CSV or PDF that includes selected metrics, filters, time range, and verifies file integrity and download completion

Smart Escalation

Automatically escalates unacknowledged alerts to designated team leads or cross-functional stakeholders after a configurable timeframe. This prevents critical issues from falling through the cracks and accelerates resolution.

Requirements

Configurable Escalation Rules
"As a customer success manager, I want to configure custom escalation rules based on alert severity and age so that critical issues are automatically brought to the right stakeholders’ attention before they impact customer satisfaction."
Description

Allows administrators to define custom escalation thresholds and timeframes for unacknowledged alerts, enabling precise control over when and how issues are raised to higher-level stakeholders. This functionality integrates directly with the alerting engine in PulsePanel, letting teams set conditions based on alert severity, ticket age, or specific tags. By empowering customer success managers to tailor escalation policies to their operational workflows, the product ensures critical issues are escalated promptly, minimizing response times and reducing risk of churn due to overlooked problems.

Acceptance Criteria
Admin defines escalation timeframe
Given an administrator navigates to the escalation rules configuration page, when they set a custom timeframe (e.g., 30 minutes) for unacknowledged alerts, then the system saves the rule and applies it to all incoming alerts, triggering escalation after the defined period.
System escalates high-severity alerts
Given an alert is generated with severity marked as “Critical,” when it remains unacknowledged for the administrator’s configured threshold (e.g., 15 minutes), then the system automatically escalates the alert to the designated team lead and cross-functional stakeholders.
Alerts with specific tags escalate to designated stakeholders
Given an alert is tagged as “Billing Issue,” when it remains unacknowledged for the configured timeframe, then the system escalates the alert directly to the finance team and notifies the customer success manager.
Ticket age triggers escalation
Given an alert is associated with a support ticket, when the ticket age exceeds the configured threshold (e.g., 48 hours) without any acknowledgement, then the system escalates the ticket to the senior support manager and logs the escalation event.
Acknowledged alerts are not escalated
Given an alert has been acknowledged by any team member within the configured timeframe, when the system evaluates escalation rules, then it does not escalate the acknowledged alert and marks the rule as satisfied.
Multi-Channel Stakeholder Notifications
"As a team lead, I want to receive escalation notifications through my preferred communication channel so that I can quickly react to critical customer issues wherever I am."
Description

Provides automated notifications to designated team leads and stakeholders via email, SMS, Slack, and Microsoft Teams. These notifications include critical alert details, links to the alert in PulsePanel, and actionable next steps. Integration with multiple channels ensures stakeholders are informed through their preferred medium, accelerating response times and reducing the chance of missed escalations.

Acceptance Criteria
Email Notification Sent After Configured Timeframe
Given a critical alert remains unacknowledged for the configured escalation timeframe, when the timeframe expires, then an email containing the alert details, a link to the alert in PulsePanel, and actionable next steps is sent to the designated team lead's email address within 1 minute.
SMS Notification Sent After Configured Timeframe
Given a critical alert remains unacknowledged for the configured escalation timeframe, when the timeframe expires, then an SMS containing the alert details, a link to the alert in PulsePanel, and actionable next steps is sent to the designated team lead's phone number within 1 minute.
Slack Notification Sent After Configured Timeframe
Given a critical alert remains unacknowledged for the configured escalation timeframe, when the timeframe expires, then a Slack message containing the alert details, a link to the alert in PulsePanel, and actionable next steps is posted to the designated stakeholder's Slack channel within 30 seconds.
Microsoft Teams Notification Sent After Configured Timeframe
Given a critical alert remains unacknowledged for the configured escalation timeframe, when the timeframe expires, then a Microsoft Teams message containing the alert details, a link to the alert in PulsePanel, and actionable next steps is posted to the designated stakeholder's Teams channel within 30 seconds.
Channel Preference Adherence
Given a stakeholder has specified preferred notification channels in their profile, when an alert escalates, then notifications are only sent via the user’s preferred channels in the order of preference within the configured escalation timeframe.
Fallback Notification on Primary Channel Failure
Given the primary notification channel fails to deliver an alert notification, when the delivery failure is detected, then the system automatically retries delivery on the next preferred channel within 30 seconds and logs the failure event.
Escalation Workflow Tracking
"As an operations manager, I want to view the full history of escalations and acknowledgments so that I can identify process improvements and ensure accountability."
Description

Introduces a comprehensive escalation workflow tracking system that logs each escalation event, acknowledgment, and resolution. The system maintains an auditable history of responsible parties, timestamps, and status changes, offering transparency and accountability throughout the escalation lifecycle. By tracking escalations end-to-end, teams can analyze performance, identify bottlenecks, and improve processes over time.

Acceptance Criteria
Initial Escalation Event Logging
Given an alert remains unacknowledged for the configured timeframe, when the system auto-escalates the alert, then a new escalation record is created in the audit log containing a unique escalation ID, source alert ID, timestamp, triggering system/component, and designated recipient.
Acknowledgment Recording by Team Lead
Given a designated team lead acknowledges an escalated alert via the PulsePanel interface or notification link, when the acknowledgment action is performed, then the escalation record in the audit log is updated with the acknowledgment timestamp, acknowledger's user ID, and status changed to "Acknowledged".
Resolution Logging and Closure
Given a team member resolves an escalated alert, when the resolution is marked in the system, then the escalation record is updated with the resolution timestamp, resolver's user ID, resolution summary, and status changed to "Resolved".
Escalation History Retrieval
Given a customer success manager requests the escalation history for a specific alert, when the request is submitted, then the system returns a chronological audit trail listing all escalation events, acknowledgments, and resolutions, including timestamps and responsible user IDs.
Analytics Report on Escalation Performance
Given a user generates an escalation performance report for a specified date range, when the report is requested, then the system computes and displays metrics including total escalations, average acknowledgment time, average resolution time, and count of pending escalations based on audit log data.
Escalation Dashboard
"As a customer success team member, I want a dashboard that shows all current escalations and their statuses so that I can quickly see which issues need attention."
Description

Develops a dedicated dashboard within PulsePanel that displays real-time and filtered views of pending, in-progress, and completed escalations. The dashboard includes visual indicators for aging alerts, priority levels, and responsible stakeholders, and supports sorting, filtering, and search capabilities. By centralizing escalation status, customer success teams gain immediate visibility into critical issues and can prioritize remediation effectively.

Acceptance Criteria
Viewing Real-Time Escalation Status
Given the user is on the Escalation Dashboard, when a new escalation occurs, then the dashboard updates within 5 seconds to display the new escalation under the 'Pending' section with correct details.
Filtering Escalations by Priority and Age
Given the user applies filters for 'High' priority and 'Aging Alerts > 24 hours', when the filters are applied, then only escalations matching these criteria are displayed, and the total count reflects the filtered results.
Searching Escalations by Stakeholder
Given the user enters a stakeholder name into the search bar, when the search is executed, then all escalations assigned to that stakeholder are listed and other records are excluded.
Visualizing Aging Alerts with Indicators
Given escalations have age durations, when an alert exceeds predefined thresholds (e.g., 24, 48, 72 hours), then it is highlighted with distinct color indicators (amber, orange, red) corresponding to each threshold.
Sorting Escalations by Status and Priority
Given the user selects a sort option, when the sort by 'Status' or 'Priority' is applied, then the escalations list is re-ordered accordingly and maintains sticky sorting until changed.
Persisting Dashboard State Across Sessions
Given the user logs back into PulsePanel, when returning to the Escalation Dashboard, then the last used filters, sort order, and selected view (pending, in-progress, completed) are restored.
Role-Based Access Control for Escalations
"As a system administrator, I want to restrict escalation rule management to authorized roles so that we maintain control over critical alerting configurations and protect sensitive data."
Description

Implements role-based access control (RBAC) for Smart Escalation configurations and data views. Administrators can assign granular permissions for who can create, modify, or delete escalation rules, who can acknowledge escalations, and who can view escalation logs. With RBAC in place, organizations maintain security and compliance by ensuring only authorized users can manage critical escalation processes.

Acceptance Criteria
Assigning Create Permissions for Escalation Rules
Given an Administrator user navigates to RBAC settings for Smart Escalation, When they grant 'Create Escalation Rule' permission to Role X, Then users assigned to Role X can access the 'Create Rule' button and successfully create a new escalation rule.
Restricting Modification of Escalation Rules
Given a user without 'Modify Escalation Rule' permission attempts to edit an existing rule, When they click 'Edit', Then access is denied and the system displays a 'Not Authorized' message.
Viewing Escalation Logs Based on Permissions
Given a user with 'View Escalation Logs' permission navigates to the Logs tab, Then they see the full escalation history with filtering options; Given a user without this permission, Then accessing the Logs tab returns an 'Access Denied' error.
Authorized Acknowledgment of Escalations
Given a user with 'Acknowledge Escalation' permission receives an escalation alert, When they click 'Acknowledge', Then the alert status updates to 'Acknowledged' and an entry with their name and timestamp appears in the audit log.
Revoking RBAC Permissions
Given an Administrator revokes 'Delete Escalation Rule' permission from Role Y, When a user in Role Y attempts to delete a rule, Then the delete option is disabled and a tooltip explains insufficient permissions.

Response Templates

Provides a library of customizable, pre-approved response messages that users can send directly from the alert. By streamlining communication, teams can quickly acknowledge issues and initiate standard troubleshooting steps.

Requirements

Template Creation Interface
"As a customer success manager, I want to create and customize standardized response templates so that I can rapidly acknowledge issues and maintain consistent messaging across clients."
Description

Provide an intuitive interface within PulsePanel for users to create and edit response templates. The interface should allow users to define template titles, body text with placeholders for dynamic fields (e.g., client name, issue ID), and formatting options (e.g., bold, italics, links). It should integrate seamlessly with existing alert screens, enabling users to draft and save templates without leaving the incident view. The feature will reduce response time, ensure consistent communication, and increase team efficiency by centralizing template management.

Acceptance Criteria
Accessing the Template Creation Interface
Given I am viewing an incident alert, when I click the 'Create Template' button, then a template creation modal opens containing inputs for title, body, formatting options, and placeholder selection.
Adding Dynamic Placeholders to Template
Given I am in the template body editor, when I open the placeholder dropdown and select a field (e.g., client name, issue ID), then the corresponding placeholder is inserted at the cursor position in the body text.
Applying Text Formatting Options
Given I have entered text in the template body, when I apply bold, italics, or hyperlink formatting using the toolbar, then the selected text updates with the correct markup and preview displays accordingly.
Saving a Template Directly from the Alert Screen
Given I have provided a template title and body, when I click 'Save Template', then the template is stored successfully, the modal closes, and the new template appears in the library accessible from the alert screen.
Editing an Existing Response Template
Given I have an existing template in the library, when I click its 'Edit' button, modify content or placeholders, and save, then the updates persist and are visible in subsequent uses of the template.
Template Categorization and Tagging
"As a customer success manager, I want to categorize and tag templates so that I can quickly find the most relevant response for each alert."
Description

Enable users to assign categories and custom tags to response templates for easy organization and retrieval. Categories (e.g., ‘Acknowledgement,’ ‘Troubleshooting,’ ‘Follow-up’) and user-defined tags should be searchable and filterable. This helps teams quickly locate appropriate responses based on the alert type or client segment. Integration with PulsePanel’s alert metadata should allow automatic template filtering relevant to the issue context.

Acceptance Criteria
Category Assignment to New Response Template
Given a user is creating a new response template, when they select a predefined category from the category dropdown and click save, then the template’s category field is populated with the selected category and persists in the template list.
Custom Tagging of Existing Templates
Given an existing response template, when a user enters one or more custom tags in the tag input field and clicks apply, then the tags appear under the template’s metadata and are stored in the system for future search and filter operations.
Search and Filter Templates by Category and Tag
Given multiple templates with various categories and tags, when a user selects a category filter and enters a tag filter then executes the search, then only templates matching both the selected category and tag filter are displayed in the results.
Automatic Template Filtering Based on Alert Metadata
Given an active alert with metadata specifying category and tags, when a user opens the response template library from the alert view, then the system automatically filters and displays only those templates matching the alert’s category and tags.
Editing and Removing Categories and Tags
Given a response template with assigned category and tags, when a user edits the category selection or removes specific tags and clicks update, then the template metadata reflects the new category and tag list and these changes are saved.
Template Approval Workflow
"As a team lead, I want to review and approve new templates so that all communication remains on-brand and compliant."
Description

Implement an approval process for response templates, allowing managers to review and approve or request changes before templates become available to the broader team. The workflow should include draft status indicators, reviewer assignment, comment threads, and approval logs. Approved templates should be clearly marked and version-controlled to ensure compliance with brand voice and legal requirements.

Acceptance Criteria
Draft Template Submission
Given a user creates a new response template and selects "Submit for Approval", then the template status is set to "Draft" and a draft indicator is displayed next to the template name.
Reviewer Assignment
Given a manager views a draft template, when they click "Assign Reviewer" and select a user, then the selected reviewer is added to the template and the reviewer's name appears in the assignment list.
Reviewer Feedback
Given a reviewer assigned to a draft template, when they add a comment and click "Submit Feedback", then the comment is saved in a threaded view and the template status updates to "Comments Received".
Approval Process
Given a draft template with no pending comments, when a manager clicks "Approve", then the template status changes to "Approved" and the approval action is recorded in the approval log with timestamp and approver details.
Version Control
Given an approved template is edited, when the manager submits the changes, then a new version is created, the previous version is retained, and version numbers increment sequentially.
Audit Log
Given any action on a template (submission, feedback, approval, version update), when the action occurs, then an entry is added to the approval log capturing action type, user, and timestamp, and the log is viewable in the template's history tab.
One-Click Template Insertion
"As a customer success manager, I want to insert a pre-approved template with a single click so that I can send acknowledgements faster and accurately."
Description

Add a one-click mechanism on alert detail pages that allows users to open a template selector and insert a chosen template directly into the message composer. Selected templates should automatically populate dynamic placeholders with relevant alert data (e.g., client name, ticket ID). This functionality streamlines the response process and minimizes manual typing errors.

Acceptance Criteria
Opening the Template Selector on Alert Detail Page
Given a user is on an alert detail page, when they click the 'Insert Template' button, then the template selector modal opens within 2 seconds and displays a searchable list of pre-approved templates.
Inserting Selected Template into Composer
Given the template selector modal is open, when the user selects a template and confirms insertion, then the message composer is populated with the full template text and the modal closes.
Dynamic Placeholders Auto-Populate with Alert Data
Given a template containing placeholders (e.g., {{client_name}}, {{ticket_id}}), when inserted, then each placeholder is replaced with the corresponding alert detail values formatted correctly.
Handling Missing Alert Data
Given the selected template contains a placeholder for data that is unavailable, when inserted, then the placeholder is replaced with a default text like 'N/A' and a warning icon is shown next to the affected text.
Insertion Performance Under Load
Given high system load, when a user inserts a template, then insertion completes within 3 seconds without degrading composer responsiveness.
Template Usage Analytics
"As a product manager, I want to view template usage statistics so that I can evaluate their effectiveness and adjust our communication strategies."
Description

Provide analytics dashboards that track template usage metrics, including frequency of use per template, average response times, and user adoption rates. Dashboards should allow filtering by time range, team, and template category. Insights from these analytics will help identify underutilized templates, measure the impact of templated responses on response times, and guide ongoing template optimization.

Acceptance Criteria
Template Usage Overview Dashboard Access
Given the user navigates to the Template Usage Analytics dashboard, When the dashboard loads successfully, Then it displays total usage count per template, average response time per template, and overall adoption rate.
Time Range Filtering
Given the user selects a custom time range filter, When the filter is applied, Then the dashboard updates to show metrics only for the selected period.
Team-Based Filtering
Given the user applies a team filter, When a specific team is selected, Then the dashboard metrics update to display template usage metrics for that team only.
Category-Based Filtering
Given the user selects a template category, When the category filter is applied, Then only templates within that category and their usage metrics are displayed.
Underutilized Template Identification
Given the user sets a threshold for minimum usage, When the threshold is applied, Then templates used below this threshold are listed as underutilized.
Average Response Time Reporting
Given the user views average response time metrics, When metrics are displayed, Then the dashboard shows average response time per template and highlights anomalies exceeding the SLA of 2 hours.

Scheduled Alerts

Allows users to define quiet hours, custom alert windows, and daily digests. This feature balances immediate responsiveness with work-life boundaries, ensuring teams receive critical updates without unnecessary interruptions.

Requirements

Quiet Hours Configuration
"As a customer success manager, I want to set quiet hours so that I can avoid alerts during non-working hours and maintain work-life balance."
Description

Enable users to define specific daily intervals during which notifications are suppressed to prevent interruptions during non-working hours. This includes setting start and end times for quiet periods, choosing which alert types are muted, and providing visual feedback on active quiet hours within the dashboard.

Acceptance Criteria
Setting Quiet Hours Interval
Given the user navigates to Quiet Hours settings, When they enter a start time of 22:00 and an end time of 06:00 and click Save, Then the system stores the interval and suppresses all notifications between 22:00 and 06:00 daily.
Muting Selected Alert Types
Given the user selects only 'High Priority' and 'Bug Report' alert types to mute during quiet hours, When quiet hours are active, Then notifications for those selected types are suppressed while all other alert types are still delivered.
Dashboard Visualization of Active Quiet Hours
Given the current time falls within a configured quiet period, When the user views the main dashboard, Then a prominent banner displays 'Quiet Hours Active' with the configured start and end times highlighted.
Conflict Handling for Overlapping Quiet Hours
Given an existing quiet period from 22:00 to 02:00, When the user attempts to save a new quiet period from 01:00 to 23:00, Then the system displays a validation error 'Quiet hours overlap with existing schedule' and prevents saving.
Persistence of Quiet Hours Settings Across Sessions
Given the user has saved a quiet hours interval and alert type selections, When they log out and then log back in, Then the Quiet Hours settings page displays the previously saved interval and muted alert types correctly.
Custom Alert Window Definition
"As a team lead, I want to configure alert windows for high-priority events outside standard hours so that I stay informed of urgent issues without being overwhelmed by low-priority updates."
Description

Allow users to specify custom alert windows where only selected notification types are delivered outside of standard working hours. This feature enables defining multiple windows, associating specific alert categories or channels with each window, and integrating with existing notification rules.

Acceptance Criteria
Create new custom alert window
Given the user navigates to Scheduled Alerts settings, when they create a new alert window with a name, start time, end time, and select “Critical Alerts” category, then the system saves the window and displays it in the custom alert windows list.
Prevent overlapping windows
Given an existing custom alert window from 21:00 to 06:00, when the user attempts to define a new window overlapping 01:00 to 05:00, then the system displays a validation error and prevents saving the new window.
Edit existing custom alert window
Given a pre-existing custom alert window is listed, when the user modifies its end time and adds an additional alert category, then the updated configuration is saved and immediately reflected in the window details.
Delete custom alert window
Given a custom alert window exists in the user’s list, when the user deletes it, then the window is removed from the list and no longer applies to incoming notifications.
Filter notifications by window
Given the current time falls within a custom alert window that only allows “Critical” and “High” severity alerts, when a “Medium” severity alert is triggered, then no notification is sent to the user.
Daily digest respects custom windows
Given the user is outside all defined alert windows at end of day, when the daily digest is generated, then it consolidates all notifications received during quiet hours into a single digest email.
Daily Digest Scheduling
"As a customer success manager, I want to receive a daily digest of alerts so that I can review all activities in a single, concise report."
Description

Provide capability to schedule and deliver a consolidated daily summary of all relevant alerts. Users can choose delivery time, select alert categories to include, and customize digest format (email or in-app), ensuring a concise end-of-day report that captures key insights.

Acceptance Criteria
User sets up daily digest delivery time
Given the user is on the Daily Digest Scheduling page When they select a delivery time and click “Save Preferences” Then the system stores the selected time and displays a confirmation message
User customizes alert categories for digest
Given the user is on the Daily Digest Scheduling settings When they select or deselect alert categories and click “Save Preferences” Then only the chosen categories are included in subsequent digests
User selects digest format
Given the user is customizing their Daily Digest When they choose “Email” or “In-App” format and save Then all future digests are delivered in the selected format
System generates and sends daily digest
Given the current time matches the user’s scheduled delivery time When the system runs the digest job Then it compiles all alerts from the last 24 hours, formats them, and delivers the digest via the user’s chosen channel
User updates existing digest schedule
Given the user has an existing daily digest schedule When they modify any setting and save changes Then the system updates the schedule immediately and applies the new settings to the next digest
Time Zone Awareness
"As a global team member, I want alert schedules to reflect my local time zone so that I receive notifications at appropriate local times."
Description

Support global teams by automatically adjusting alert schedules based on each user's time zone settings. This ensures that quiet hours, custom alert windows, and digest times align with local time, with real-time conversion and fallbacks for daylight saving changes.

Acceptance Criteria
Local Quiet Hours Enforcement
Given a user in America/New_York, when they set quiet hours from 22:00 to 07:00, then no alert notifications are sent between 22:00 and 07:00 Eastern Time; and alerts outside this window are delivered immediately.
Daily Digest Time Conversion
Given a user in Asia/Tokyo, when they subscribe to a daily digest at 09:00, then the digest is delivered at 09:00 Japan Standard Time; and the timestamp in the digest header reflects JST.
DST Transition Handling
Given a user in Europe/Berlin, when daylight saving time begins or ends, then existing alert windows and digests automatically adjust by one hour to maintain the same local start and end times.
Timezone Change Updates Schedules
Given a user with existing quiet hours and digest times configured, when they update their timezone from UTC to America/Los_Angeles, then all scheduled alert and digest times are recalculated and displayed in Pacific Time; and future notifications follow the new schedule.
Fallback to UTC for Undefined Timezone
Given a user without a timezone setting, when they configure any alert window or digest time, then the system defaults to UTC for scheduling; and a warning message informs the user about the default setting.
Emergency Override Mechanism
"As a customer success manager, I want critical alerts to bypass quiet hours so that I can promptly address severe issues regardless of the time."
Description

Implement an override mechanism that sends critical alerts immediately, even during quiet hours, based on predefined severity thresholds. Users can configure which alert severities trigger the override and receive an explicit confirmation when the override is in effect.

Acceptance Criteria
Immediate Dispatch During Quiet Hours
Given the user has configured quiet hours and set the override severity threshold to critical, When a critical-level alert is generated during quiet hours, Then the system sends the alert notification immediately to the user’s device.
Custom Severity Configuration Confirmation
When the user saves their override severity settings in the alert configuration, Then the system displays a confirmation message and persists the settings for future alerts.
Emergency Override Acknowledgment
Given an emergency override is in effect, When an alert meets or exceeds the override severity threshold, Then the system sends an explicit confirmation to the user indicating that the override has been applied.
Non-critical Alerts Deferred
Given the override severity threshold is set higher than warning-level, When a warning-level alert arrives during quiet hours, Then the system defers the alert and queues it for delivery after quiet hours end.
Daily Digest Integration with Override
Given the user has opted into a daily digest of alerts, When an emergency alert triggers an override, Then the system sends the emergency alert immediately and excludes it from the next scheduled daily digest.

Alert Insights Dashboard

Offers visual analytics on alert frequency, response times, and resolution outcomes. By uncovering trends and bottlenecks, teams can refine their alert strategies and improve overall incident management efficiency.

Requirements

Real-Time Alert Visualization
"As a customer success manager, I want to see real-time charts of incoming alerts so that I can quickly detect and respond to emerging issues."
Description

Implement interactive real-time charts that display alert frequency, response times, and resolution outcomes. The visualization should update dynamically as new data arrives, allowing teams to monitor incident trends live. Integrate with existing data pipelines to pull alert metadata and ensure seamless embedding within the PulsePanel UI. Expected outcomes include faster identification of spikes in alert volumes, improved situational awareness, and immediate insights that drive proactive incident management.

Acceptance Criteria
Live Chart Data Refresh
Given new alert data arrives, when the data pipeline updates, then the real-time chart refreshes within 2 seconds without requiring a full page reload.
Spike in Alert Volume Highlight
Given a sudden increase of more than 50 alerts within a 5-minute interval, when the threshold is exceeded, then the chart highlights the spike region in red and displays a tooltip with the exact alert count.
Response Time Trend Visualization
Given historic and current response time data, when a user selects a custom time range, then the line chart displays average response times per interval with an accuracy margin of ±5%.
Resolution Outcome Filter
Given multiple resolution states (e.g., Resolved, Escalated, Pending), when a user toggles outcome filters, then the chart updates to display only the selected outcomes within 1 second.
Embedded UI Consistency
Given integration into the existing PulsePanel dashboard, when the real-time visualization is embedded, then it inherits the dashboard's theme and maintains responsive layout on desktop and mobile browsers.
Custom Time Range Filters
"As an operations lead, I want to apply custom time range filters to the dashboard so that I can analyze alert trends for specific periods of interest."
Description

Provide users with flexible filtering controls to select custom time periods for their alert analytics. This requirement involves developing UI components for date-time pickers and predefined intervals (e.g., last 24 hours, last 7 days). Ensure filters apply consistently across all dashboard visualizations, enabling teams to isolate trends over any timeframe. Benefits include targeted analysis of peak incident windows and the ability to compare performance across different periods.

Acceptance Criteria
Peak Hours Analysis
Given a user selects the 'Custom Time Range' filter and specifies a start and end date-time within the last 24 hours, when the user applies the filter, then the dashboard visualizations update to display alerts and metrics only for the selected peak hours window without errors.
Last 7 Days Filter Application
Given a user clicks on the 'Last 7 days' preset interval, when the filter is applied, then all dashboard charts refresh to show data aggregated over the past 7 days, and the selected preset is highlighted.
Custom Date Range Comparison
Given a user enters two distinct custom date ranges and enables the comparison toggle, when the user applies both filters, then the dashboard displays side-by-side analytics comparing alert frequency and resolution times for the two ranges.
Predefined Interval Selection
Given a user chooses a predefined interval (e.g., 'Last 24 hours', 'Last 30 days') from the dropdown, when the interval is selected, then the dashboard automatically adjusts all visualizations to the chosen interval and disables custom date inputs.
Date-Time Picker Validation
Given a user inputs an invalid date range where the end date precedes the start date, then the system prevents filter application and displays a validation error message prompting correction.
Alert Drill-Down Details
"As a support engineer, I want to drill down from summary charts into individual alert details so that I can investigate and resolve specific incidents efficiently."
Description

Enable users to click on any data point within the dashboard charts to view detailed drill-down information. Display a modal listing individual alerts, including timestamp, type, priority, assigned team, and resolution status. Integrate links back to the original ticketing system for quick access. This feature ensures that high-level trends can be investigated at the granular level, streamlining root-cause analysis and action tracking.

Acceptance Criteria
Chart Data Point Click
Given the Alert Insights Dashboard is displayed with interactive chart data points; When the user clicks on any data point; Then a modal appears listing individual alerts with timestamp, type, priority, assigned team, and resolution status; And the list is sorted by timestamp descending.
Filtered Drill-Down Respect
Given the user has applied filters (date range, alert type, priority); When the user drills down on a filtered data point; Then the modal lists only alerts matching the applied filters; And the total count matches the filtered count displayed on the chart.
Link Back to Ticketing System
Given the drill-down modal displays individual alerts; When the user clicks on a ticket ID link within the modal; Then a new browser tab opens directing to the correct ticket in the external ticketing system without errors.
Performance Under Load
Given the dashboard contains at least 1,000 alerts in the dataset; When the user clicks a data point; Then the drill-down modal loads the detailed list within 2 seconds on a standard broadband connection.
Accessibility and Responsive Design
Given the dashboard is accessed on devices ranging from mobile phones to desktop monitors; When the user interacts with a data point; Then the modal is fully visible, usable on all screen sizes, and meets WCAG 2.1 AA accessibility standards for keyboard navigation, contrast, and ARIA labels.
Trend Analysis and Forecasting
"As a team manager, I want to see trend lines and forecasts on alert metrics so that I can plan staffing and resources for anticipated workloads."
Description

Incorporate statistical trend lines and simple forecasting models into the dashboard visualizations to predict future alert volumes and response times. Leverage historical data to compute moving averages, growth rates, and projected values. Provide toggles to show or hide trend lines, along with confidence intervals. This capability helps teams anticipate workload spikes and allocate resources proactively, reducing delayed responses.

Acceptance Criteria
Viewing Trend Lines for Alert Volume
Given the dashboard displays alert volume data for the past 6 months, When the user toggles the trend line on, Then a statistical trend line is overlaid accurately on the chart reflecting historical alert volume growth.
Forecasting Future Response Times
Given historical response time data exists for the past 3 months, When the forecasting feature is enabled, Then the dashboard shows predicted response times for the next 30 days with corresponding values.
Toggling Confidence Intervals in Trend Visualizations
Given a trend line is displayed on any chart, When the user enables confidence intervals, Then shaded bands appear around the trend line representing the computed 95% confidence range.
Analyzing Moving Averages for Alerts
Given daily alert counts are available for the last 90 days, When the user selects moving average analysis, Then the chart displays a 7-day and 30-day moving average lines correctly over the raw data.
Generating Growth Rate Projections
Given monthly alert volume data for the past year, When the projection toggle is activated, Then the system calculates and displays the projected monthly growth rates for the next 6 months.
Exportable Insights Reports
"As a customer success director, I want to export the dashboard metrics and visuals so that I can share them with executives and external stakeholders."
Description

Allow users to export dashboard data and visualizations as CSV, PDF, or PowerPoint. Implement server-side rendering of charts and tables into downloadable formats, preserving styling and data accuracy. Include options to select which metrics and date ranges to include in exports. This feature supports sharing insights with stakeholders and embedding analytics into external presentations or reports.

Acceptance Criteria
Selecting Export Format
Given a user is on the Alert Insights Dashboard's export options, When they open the format dropdown, Then they see options for CSV, PDF, and PowerPoint.
Filtering Export Data
Given a user opens the export modal, When they choose a date range and select metrics from the list, Then the preview shows only the selected metrics within the specified range.
CSV Export Download
Given a user has selected CSV and clicked Export, When the export is processed, Then a CSV file named 'insights_export_<start>_<end>.csv' downloads containing headers matching selected metrics and rows matching corresponding data points.
PDF Export Download
Given a user has selected PDF and clicked Export, When server-side rendering completes, Then a PDF file downloads preserving chart images and table layouts for the selected metrics and date range.
PowerPoint Export Download
Given a user has selected PowerPoint and clicked Export, When server-side rendering completes, Then a .pptx file downloads with slides containing rendered charts and tables for each selected metric and date range.
Preserving Chart and Table Styling
Given any export format is chosen, When the file is generated, Then colors, fonts, and layouts match those displayed on the dashboard.

Frustration Flash

Detects sudden spikes in negative sentiment across customer conversations in real time. By instantly highlighting these flashpoints, teams can prioritize critical issues, intervene proactively, and prevent escalations before they impact satisfaction.

Requirements

Real-time Sentiment Analyzer
"As a customer success manager, I want to see live sentiment scores for customer communications so that I can detect dissatisfaction immediately."
Description

The system shall process incoming customer messages in real time using natural language processing to assign sentiment scores. It integrates with existing communication channels, continuously updates sentiment metrics, and provides high-resolution data to identify negative emotions as they emerge. This capability ensures that newly arriving feedback is never missed, enabling proactive support interventions and reducing resolution times.

Acceptance Criteria
Live Chat Negativity Spike Detection
Given a stream of live chat messages processed in real time When the negative sentiment score within any rolling 60-second window increases by 30% or more compared to the previous 60 seconds Then the system flags a Frustration Flash, displays an alert in the dashboard within 5 seconds, and logs the event in the alert history
Email Ticket Frustration Surge Monitoring
Given real-time ingestion of customer email tickets When the average sentiment score of the last 20 emails drops below 0.2 on a 0–1 scale Then the system generates a negative sentiment surge report and sends an email notification to the customer success manager within 10 seconds
Social Media Comment Flashpoint Identification
Given continuous monitoring of social media comment streams When three or more comments with sentiment scores below 0.1 are posted within a 5-minute interval on any monitored channel Then the system highlights the flashpoint on the PulsePanel dashboard and marks the event with High priority
SMS Feedback Sudden Negative Shift
Given real-time processing of SMS feedback messages When any single message is assigned a sentiment score of zero (strongly negative) Then the system triggers an immediate push notification to the on-duty support agent and records the timestamp of the alert
Dashboard Real-Time Alert Confirmation
Given generated Frustration Flash events When an event is flagged as a negative sentiment spike Then the event appears on the real-time dashboard within 5 seconds with accurate sentiment metrics, source channel, and message excerpt
Spike Detection Engine
"As a customer success manager, I want the system to automatically detect sudden increases in negative sentiment so that I can prioritize critical issues."
Description

A dedicated engine that continuously analyzes sentiment trends and applies statistical algorithms to detect significant spikes in negative sentiment. It filters out noise, adapts to normal variation baselines, and flags only meaningful surges. This engine underpins the Frustration Flash feature by ensuring only genuine critical events trigger alerts, improving signal-to-noise ratio for teams.

Acceptance Criteria
Real-Time Spike Identification
Given a live stream of customer sentiment scores, when the engine processes a sudden increase of more than 20% negative sentiment within a 5-minute window, then it must flag the event as a spike and record the timestamp and magnitude of the change.
Noise Filtering Accuracy
Given historical sentiment data containing normal fluctuations under 10%, when the engine applies its noise filter, then it must ignore these minor variations and only consider changes exceeding the defined noise threshold.
Adaptive Baseline Adjustment
Given the engine has been running for over 24 hours, when it recalculates its baseline every hour, then the new baseline must reflect the rolling average of the past 12 hours and subsequent spike detection must use the updated baseline.
Alert Generation Threshold
Given the engine has detected three consecutive 5-minute intervals of negative sentiment above 25% over baseline, when this condition occurs, then the system must generate a single alert to the dashboard and suppress duplicate alerts for the same event.
Historical Context Validation
Given a dataset covering the past 30 days, when the engine analyzes a potential spike that coincides with known weekly patterns, then it must compare against seasonal baselines and only flag the event if it exceeds both the seasonal and rolling baselines by the threshold.
Alert Notification System
"As a customer success manager, I want to receive instant notifications when a frustration flash occurs so that I can intervene quickly."
Description

A configurable notification framework that delivers instant alerts when a frustration flash is detected. It supports multiple channels including email, in-app messages, and integrations with Slack or Microsoft Teams. Users can customize alert thresholds, channels, and recipient lists to ensure the right stakeholders are informed immediately, enabling swift response.

Acceptance Criteria
Configuring Email Alert Threshold
Given an admin user navigates to the alert notification settings and sets the email alert threshold to X flashes; When the system detects X consecutive frustration flashes within the configured timeframe; Then it sends an email alert to all configured recipients within 1 minute.
Receiving In-App Alert
Given a user is logged into PulsePanel on the dashboard; When a frustration flash meeting the default threshold is detected; Then an in-app notification banner appears within 30 seconds displaying the flash count and a link to detailed views.
Sending Alert to Slack Channel
Given Slack integration is enabled with valid credentials and channels are configured; When a frustration flash is detected; Then the system posts a message with the flash summary and link to the case in the specified Slack channel within 1 minute.
Customizing Alert Recipients
Given a user updates the recipient list by adding or removing email addresses or Slack channels and clicks save; When a test alert is triggered; Then notifications are delivered only to the updated list of recipients across all selected channels.
Alert Channel Fallback Mechanism
Given a configured notification channel returns an error upon sending; When a frustration flash occurs; Then the system retries sending through that channel once, and if it still fails, sends the alert via an alternative channel and logs the failure event.
Interactive Flashpoint Dashboard
"As a customer success team lead, I want a dashboard showing real-time frustration flash data so that I can visualize hotspots and allocate resources effectively."
Description

A centralized dashboard presenting a visual timeline of detected frustration flashes, sentiment heatmaps, and trend graphs. It allows users to filter by time range, customer segments, and communication channels. Drill-down capabilities provide conversation context and historical comparisons, enabling teams to identify recurring pain points and measure intervention effectiveness.

Acceptance Criteria
Time Range Filtering
Given the user selects a custom time range on the dashboard When the filter is applied Then the timeline, heatmaps, and trend graphs only display flashpoints within the selected range, and no data outside the range is visible.
Customer Segment Filtering
Given the user selects one or more customer segments When the filter is applied Then only flashpoints from conversations belonging to those segments are shown across the timeline, heatmap, and trend graphs.
Communication Channel Filtering
Given the user selects communication channels (e.g., email, chat, social) When the filter is applied Then the dashboard updates to display flashpoints exclusively from the chosen channels, and visualizations reflect only that channel data.
Drill-down to Conversation Context
Given a user clicks on a specific flashpoint in any visualization When the drill-down is triggered Then a detailed view opens showing the full conversation context with timestamps and sentiment annotations, and a link to the original ticket or interaction.
Historical Trend Comparison
Given the user selects two distinct time periods for comparison When the compare function is applied Then trend graphs overlay both periods with distinct color coding, and a summary indicates percentage change in flashpoint frequency between the periods.
Automated Escalation Workflow
"As a customer success manager, I want the system to automatically create escalation tickets for high-severity frustration flashes so that we can follow a standardized resolution process."
Description

An integration layer that automatically creates and routes escalation tickets within the existing customer support or CRM system when a high-severity frustration flash occurs. It maps detected events to predefined workflows, assigns ownership based on severity and customer tier, and tracks resolution status. This ensures consistent handling and faster issue triage.

Acceptance Criteria
High-Severity Ticket Auto-Creation
Given a detected high-severity frustration flash When the flash is processed by the escalation workflow Then an escalation ticket is automatically created in the CRM with customer ID, severity level, timestamp, and issue description populated correctly
Ownership Assignment Based on Customer Tier
Given a newly created escalation ticket for a Tier-1 customer When the escalation workflow assigns ownership Then the ticket is routed to the Tier-1 escalation queue and the designated owner receives a notification
Routing to Support Team Workflow Integration
Given an escalation ticket with critical severity When the ticket is mapped to the predefined critical-issue workflow Then the ticket status is set to "Escalated", the corresponding support team is notified, and the workflow steps (initial review, resolution, closure) are instantiated
Resolution Status Tracking Update
Given an escalation ticket in the CRM When the ticket status changes to "In Progress", "Resolved", or "Closed" Then PulsePanel’s dashboard reflects the updated status in real time (within 5 seconds)
Failure Handling for Workflow Mapping Errors
Given a high-severity event that cannot be mapped to any predefined workflow When the workflow mapping fails Then an alert is sent to the administrator channel and a fallback escalation ticket is created with status "Unmapped"

Tone Trendlines

Visualizes sentiment shifts over customizable time windows for individual accounts, segments, or channels. This feature helps users identify emerging patterns of rising frustration or delight, enabling data-driven prioritization and targeted outreach.

Requirements

Sentiment Data Aggregation
"As a customer success manager, I want aggregated sentiment scores over defined periods so that I can understand overall changes in client satisfaction."
Description

Aggregate and normalize sentiment scores from client feedback entries (tickets, surveys, chats) over time, ensuring consistent data processing and storage. This will enable accurate trend analysis, reducing noise from outliers and providing a reliable foundation for visual insights.

Acceptance Criteria
Daily Sentiment Aggregation for an Individual Account
Given the system receives various feedback entries for a specific account within a 24-hour period, When the daily aggregation process executes at 00:00 UTC, Then the system computes the average sentiment score normalized to a predefined scale (-1 to 1) and stores it without duplication.
Weekly Sentiment Aggregation Across Multiple Accounts
Given multiple accounts have feedback collected over the past week, When the weekly aggregation job runs every Monday at 01:00 UTC, Then the system aggregates sentiment scores per account segment, applies outlier mitigation, and updates the weekly trend records.
Real-time Normalization of Incoming Chat Feedback
Given a new chat feedback entry arrives via the API, When the sentiment analysis module processes it, Then the system normalizes the raw sentiment score to the standard scale and immediately stores it in the temporary feedback store.
Outlier Handling in Monthly Trend Computation
Given a month's worth of sentiment data for an account, including extreme values, When the monthly trend computation executes, Then the system identifies outliers outside 3 standard deviations, excludes them from the average calculation, and flags the removed scores in the logs.
Data Retrieval of Normalized Sentiment Scores
Given a user requests sentiment trends for a date range, When the data retrieval API is called with valid parameters, Then the API returns normalized, aggregated sentiment scores for each interval, matching the stored values with no discrepancies.
Custom Time Window Selection
"As a user, I want to define custom time windows so that I can focus on specific periods of interest and compare sentiment shifts."
Description

Provide a UI component that allows users to select preset (daily, weekly, monthly) or custom date ranges for trend analysis. The selection should dynamically update visualizations and support comparisons between multiple time periods.

Acceptance Criteria
Preset Time Window Selection
Given the user selects a preset time window (Daily, Weekly, Monthly), when the selection is applied, then the trendline updates to reflect the correct date range with appropriate axis labels.
Custom Date Range Selection
Given the user selects a custom start and end date within the allowed bounds, when the user confirms the selection, then the trendline filters data to the specified range and displays accurate date markers.
Multiple Time Period Comparison
Given the user enables compare mode and selects two distinct time periods, when both periods are applied, then the visualization displays two trendlines with distinct colors, and a legend identifies each period.
Dynamic Visualization Update
Given the user changes the time window selection, when the new window is applied, then the chart updates dynamically within 500 milliseconds without requiring a page reload.
Invalid Date Range Handling
Given the user inputs an end date earlier than the start date in custom mode, when the selection is submitted, then an error message is displayed and the apply button remains disabled.
Trendline Visualization
"As a user, I want to view sentiment trendlines so that I can quickly identify rising or falling patterns in client feedback."
Description

Render sentiment trendlines using interactive line charts that display aggregate sentiment values over the selected time window. Include hover tooltips, markers for significant data points, and smooth transitions when filters or time windows change.

Acceptance Criteria
Time Window Selection Updates Trendline
Given a user selects a new time window for analysis, when the selection is confirmed, then the trendline chart updates to display aggregate sentiment values for that window within 2 seconds and reflects the correct data points.
Hover Displays Sentiment Details
Given the trendline is displayed, when the user hovers over any data point marker, then a tooltip appears within 200ms showing the exact timestamp, sentiment score, and number of reports.
Significant Point Markers
Given sentiment data contains values exceeding predefined thresholds, then the chart displays distinct markers at those points using a different shape and color to indicate significant sentiment shifts.
Smooth Transitions on Filter Change
Given the trendline is rendered, when the user applies filters for account, segment, or channel, then the chart smoothly transitions to the filtered data within 1 second without flicker.
Responsive Rendering on Resize
Given the trendline visualization is visible, when the chart container is resized, then the axes, trendline, and markers adjust dynamically without performance degradation or loss of interactivity.
Account and Segment Filtering
"As a user, I want to filter trendlines by account or segment so that I can analyze sentiment for specific customer groups."
Description

Enable filtering of sentiment data by individual account or predefined customer segments. The filter must update the trendline visualization in real time and allow multi-select for comparative analysis.

Acceptance Criteria
Single Account Filter Applied
Given the user selects a single account from the account filter dropdown When the trendline visualization updates Then only sentiment data for that selected account is displayed and all other account data is excluded
Customer Segment Filter Applied
Given the user selects a predefined customer segment from the segment filter When the trendline visualization updates Then only sentiment data for the selected customer segment is shown and data outside that segment is hidden
Comparative Analysis with Multiple Filters
Given the user selects multiple accounts and segments simultaneously When the filters are applied Then the trendline displays combined sentiment data for each selected account and segment, using unique colors or labels for differentiation
Real-Time Trendline Update
Given the user modifies filter selections When the new filters are applied Then the trendline visualization updates within two seconds without requiring a full page reload
Filter Reset and Removal
Given the user clears all selected filters When the reset action is triggered Then the trendline reverts to showing sentiment data for all accounts and segments in the default view
Channel-Based Segmentation
"As a user, I want to segment sentiment by channel so that I can identify which support channels are driving positive or negative feedback."
Description

Allow users to segment sentiment data by communication channel (email, chat, in-app) and overlay multiple channels on the same trendline chart or view them separately for comparative insights.

Acceptance Criteria
Overlay Multiple Channels on Trendline
Given the user has selected 'email', 'chat', and 'in-app' channels When the user clicks 'Apply' on the channel filter Then the trendline chart displays all three channels overlaid with distinct color-coded lines and a legend indicating channel names
Separate Channel Trendline Comparison
Given the user chooses to view channels separately When the user toggles the 'Separate View' option Then three individual trendline charts appear side by side, each showing sentiment data for one channel
Filtering by Single Channel
Given the user selects a single channel (e.g., 'chat') from the channel filter When the filter is applied Then the trendline chart updates to show only the sentiment data for the selected channel
Handling No Data for Selected Channel
Given the user selects a channel with no sentiment data in the chosen time window When the filter is applied Then a 'No Data Available' message appears on the chart and the user is prompted to adjust the time range or select a different channel
Channel Selection Persistence
Given the user configures channel filters and navigates away from the trendline page When the user returns to the Tone Trendlines feature Then the previously selected channels remain applied and the trendline chart reflects the saved filter state
Threshold-Based Alerts
"As a user, I want to receive alerts when negative sentiment trends exceed a threshold so that I can proactively engage at-risk clients."
Description

Implement automated monitoring that triggers alerts when sentiment trends cross configurable thresholds (e.g., a drop below -0.5 or a rise above +0.7) within a specified time window. Alerts should be deliverable via email or in-app notifications.

Acceptance Criteria
High Negative Sentiment Alert for Key Account
Given the sentiment trend for a key account drops below -0.5 within a 24-hour window, when monitoring runs, then an email and in-app notification must be sent to the assigned customer success manager within 5 minutes.
Positive Sentiment Surge Notification
Given the sentiment trend for a specified segment rises above +0.7 over a 7-day period, when the threshold is exceeded, then an alert detailing the percentage change and affected channels is delivered via both email and in-app notification.
Real-Time Threshold Monitoring with Custom Windows
Given a user configures a 2-hour monitoring window with thresholds at -0.3 and +0.6, when new sentiment data is ingested, then the system evaluates trends every 15 minutes and sends an alert only if thresholds are crossed within the specified window.
User-Acknowledged Alert Logging
Given an alert has been sent, when the customer success manager acknowledges or dismisses it, then the system logs the acknowledgment timestamp and user ID in the alert history.
Bulk Threshold Update Impact Validation
Given thresholds are updated for multiple accounts via the bulk configuration tool, when the update completes, then subsequent sentiment monitoring must use the new thresholds and no alerts should trigger based on previous settings.

Emotional Heatmap

Maps sentiment intensity across product modules, user journeys, or geographic regions. By pinpointing hotspots of customer frustration or enthusiasm, teams can focus resources where they’ll have the greatest impact on retention and experience improvements.

Requirements

Sentiment Data Aggregation Pipeline
"As a Customer Success Manager, I want a unified view of sentiment data from all feedback channels so that I can trust the heatmap reflects the full spectrum of customer emotions."
Description

Develop a robust data ingestion pipeline that gathers, normalizes, and consolidates sentiment metrics from diverse customer feedback channels—support tickets, in-app surveys, social media mentions, and live chat logs—into a unified repository. The pipeline must support real-time streaming and batch processing modes, handle large data volumes, ensure data quality through validation and deduplication, and seamlessly integrate with the existing PulsePanel backend. The outcome will be a single source of truth for sentiment scores tagged by product module, user journey stage, and geographic region, enabling accurate heatmap generation.

Acceptance Criteria
Real-Time Data Streaming Ingestion
Given the pipeline is configured for real-time mode When a new live chat message with sentiment metadata arrives Then the message is ingested and stored in the sentiment repository within 5 seconds And the associated module, journey stage, and region tags are correctly applied
Scheduled Batch Processing of Support Ticket Sentiments
Given a batch job is scheduled to run daily at 02:00 UTC When the batch job executes Then all support ticket feedback submitted since the last run are ingested And duplicate entries are identified and removed And data validation rules ensure only entries with valid sentiment scores are loaded
Data Validation and Deduplication Rule Execution
Given the pipeline receives sentiment data from multiple channels When data validation rules are applied Then records lacking required fields (e.g., user ID, timestamp, sentiment score) are flagged and excluded And duplicate records (identical user ID, feedback timestamp, and text) are detected and removed before storage
Normalization of Sentiment Scores Across Channels
Given sentiment scores from diverse sources (e.g., -1 to 1 for chat, 1-5 for surveys) When the normalization module processes incoming data Then all scores are converted to a uniform 0-100 scale And the conversion formula is applied consistently across all channels
Accurate Tagging by Product Module, Journey Stage, and Region
Given incoming feedback records with metadata fields When tags are extracted during ingestion Then each record is enriched with the correct product module, user journey stage, and geographic region attributes based on metadata mappings And any unmapped or invalid tags are logged for review
Seamless Integration with PulsePanel Backend APIs
Given the pipeline has ingested and processed sentiment data When integration tests are executed against the PulsePanel backend API Then sentiment records are accessible via the backend within 10 seconds And the API returns accurate module, journey, and region tags for each record
Interactive Heatmap Visualization Engine
"As a Customer Success Manager, I want to interact with the heatmap by zooming into specific modules and viewing detailed sentiment data so that I can quickly identify areas needing attention."
Description

Implement an interactive visualization engine that renders sentiment intensity as a heatmap overlay on product modules or user journey flows. The engine must dynamically color-code regions based on sentiment thresholds, support smooth zooming and panning, and update in real time as new data arrives. It should integrate with the PulsePanel UI framework, offer tooltips that display exact sentiment values and sample feedback comments on hover, and adjust for accessibility standards such as colorblind-friendly palettes.

Acceptance Criteria
Dynamic Color-Coding Across Modules
Given sentiment thresholds are defined, when the heatmap renders, then each module region is color-coded accurately within a 2% variance of the calculated sentiment intensity.
Smooth Zooming and Panning
Given the user interacts with zoom or pan controls, when they perform the action, then the heatmap view responds within 200ms without graphical distortion or data loss.
Real-time Data Updates
Given new sentiment data arrives, when it is ingested, then the heatmap updates automatically within 5 seconds reflecting the latest sentiment values.
Tooltip Display on Hover
Given the user hovers over a heatmap region, when the cursor remains for 500ms, then a tooltip appears displaying the exact sentiment score and at least three sample feedback comments.
Accessibility Color Palette Support
Given accessibility mode is enabled, when the heatmap renders, then colors use a colorblind-friendly palette with a minimum 4.5:1 contrast ratio and alternate palette toggle is available.
Geographic Sentiment Overlay
"As a Global Customer Success Director, I want to see sentiment mapped by region so that I can allocate resources to areas with the highest customer impact."
Description

Create a geographical overlay component that maps sentiment heat across different regions or time zones. This feature will plot aggregated sentiment scores onto an interactive world map or regional dashboard, highlight hotspots of frustration or enthusiasm by country or region, and allow filtering by specific markets. The component must integrate geolocation data with the sentiment repository, support drill-down to subregions, and synchronize with the heatmap’s temporal filters.

Acceptance Criteria
Global Sentiment Map Load
Given a user accesses the Geographic Sentiment Overlay, when the data is fetched, then the interactive world map displays aggregated sentiment scores for all regions using the defined color gradient, and each region’s hover tooltip shows the region name and sentiment value.
Temporal Filter Synchronization
Given a user adjusts the temporal filter slider, when the time range selection changes, then the map updates within 2 seconds to reflect sentiment scores for the selected period across all regions.
Regional Drill-Down Interaction
Given a user clicks on a highlighted region, when the region is selected, then the map zooms into the subregion level, displays sentiment heat for subregions, and updates the breadcrumb trail to reflect navigation.
Market-Specific Filtering
Given a user selects one or more markets from the filter panel, when the filters are applied, then only sentiment hotspots for the chosen markets are displayed on the map and non-selected regions are visually dimmed.
Geolocation Data Integrity
Given the overlay initiates data retrieval, when geolocation and sentiment data are merged, then every mapped region contains valid geocoordinates and sentiment values with no missing entries, and a data integrity check confirms record completeness.
Module- and Journey-Level Drill-Down Filters
"As a Customer Success Analyst, I want to apply filters for module and journey stage so that I can focus on specific customer workflows and optimize the experience."
Description

Add advanced filtering controls that allow users to isolate sentiment data by product module, user journey stage, or customer segment. Users should be able to select one or multiple filters to refine the heatmap view, dynamically update sentiment thresholds, and bookmark filter presets for repeated analysis. This requirement involves creating filter UI elements, connecting them to the visualization engine, and ensuring that backend queries remain performant under multiple concurrent filters.

Acceptance Criteria
Single Filter Selection
Given the user is viewing the Emotional Heatmap page When the user selects a single product module filter Then the heatmap updates within 2 seconds to display sentiment data only for the selected module
Multiple Filter Selection
Given the user has applied multiple filters for product module, user journey stage, and customer segment When the user clicks Apply Then the heatmap displays aggregated sentiment data matching all selected filters with correct data points and color intensities
Sentiment Threshold Adjustment
Given the user moves the sentiment intensity slider When the threshold is set to a new value Then only heatmap regions with sentiment scores equal to or above the threshold are highlighted, and regions below are dimmed
Bookmarking Filter Presets
Given the user has configured a set of filters and threshold values When the user saves the configuration as a preset with a custom name Then the preset appears in the Preset dropdown list and can be reapplied, restoring all filter and threshold settings
Performance Under Concurrent Filters
Given up to 50 concurrent users apply different combinations of filters When the system processes these requests Then the average heatmap update response time remains below 3 seconds with no errors
Time-Range Sentiment Trends
"As a Customer Success Manager, I want to view how sentiment hotspots shift over time so that I can correlate changes with product releases."
Description

Build functionality to visualize sentiment changes over custom time ranges within the heatmap interface. Users should be able to select preset or custom date ranges and see the heatmap animate or update to reflect sentiment evolution over time. This requires implementing time-series data storage, efficient querying by time window, and smooth transitions in the visualization engine. The feature will help teams track the impact of product updates or campaigns on customer sentiment.

Acceptance Criteria
Preset Date Range Selection Updates Heatmap
Given a user selects a preset date range (e.g., Last 7 Days), when applied, the heatmap displays sentiment data exclusively for that range and updates within 2 seconds with correct axis labels.
Custom Date Range Picker Applies Correct Filter
Given a user selects custom start and end dates using the date picker, when applied, the heatmap reflects data strictly within that range, excluding out-of-range data and displaying an error for invalid selections.
Heatmap Animation Transitions Smoothly
Given a user enables animation mode after selecting a new time range, when the range changes, the heatmap animates between previous and current data states within 3 seconds without data distortion.
Time-Series Data Query Performance
Given any selected date range up to 12 months, when the heatmap requests data, the backend returns aggregated sentiment data within 500ms and the front-end renders it within 1 second.
Invalid Date Range Error Handling
Given a user inputs an end date earlier than the start date, when applying the range, the system displays a clear error message and disables the apply button until dates are corrected.

Intervention Advisor

Provides AI-driven recommendations for next-best actions when customer sentiment dips below predefined thresholds. From tailored messaging templates to escalation paths, this feature guides support and success teams to resolve issues swiftly and empathetically.

Requirements

Threshold Configuration Interface
"As a customer success manager, I want to configure sentiment thresholds so that I receive alerts tailored to each client’s risk profile."
Description

Provide an intuitive UI within PulsePanel where customer success managers can define and adjust sentiment dip thresholds that trigger the Intervention Advisor. The interface should allow creation of multiple threshold levels, set conditional rules based on sentiment scores or keyword tags, and provide real-time preview of triggers. Integration with the existing dashboard ensures seamless management and alignment with individual client risk profiles, empowering teams to fine-tune sensitivity and avoid alert fatigue.

Acceptance Criteria
Creating a New Sentiment Threshold Level
Given the user is on the Threshold Configuration Interface When the user enters a unique threshold name and a valid sentiment score and clicks Save Then the new threshold is persisted and displayed in the list
Defining Multiple Threshold Levels with Conditional Rules
Given the user has created at least one threshold When the user adds additional thresholds with distinct sentiment ranges and keyword tag conditions Then all thresholds are saved without overlap and listed in ascending order of sensitivity
Previewing Trigger Activation in Real-Time
Given the user is configuring a threshold When the user adjusts the sentiment score or adds keyword tags Then the interface displays a real-time preview indicating whether a sample customer feedback would trigger the Intervention Advisor
Adjusting an Existing Threshold Level
Given the user selects an existing threshold from the list When the user modifies its sentiment score or conditional rules and clicks Update Then the changes replace the previous settings and the updated threshold reflects in the list immediately
Seamless Integration with Dashboard Risk Profiles
Given the user has configured thresholds for multiple clients When the user navigates to the main dashboard Then each client’s risk profile displays alerts based on the newly configured thresholds without manual refresh
AI Recommendation Engine Integration
"As a support agent, I want the system to automatically generate personalized action recommendations so that I can respond more effectively to at-risk customers."
Description

Integrate the AI-driven recommendation engine into the PulsePanel backend to generate next-best-action suggestions whenever customer sentiment dips below configured thresholds. The integration must support real-time invocation, context enrichment with customer history and issue tags, and graceful fallback to default messaging templates. This ensures accurate, empathetic recommendations delivered instantly to agents, reducing manual triage and accelerating issue resolution.

Acceptance Criteria
Real-time Invocation Trigger
Given a customer sentiment score dips below the configured threshold, when the sentiment-dip event is processed by PulsePanel backend, then the AI recommendation engine is invoked within 200ms and the next-best-action suggestion is delivered back to the agent UI within 500ms.
Contextual Data Enrichment
Given a sentiment-dip event and associated customer record, when constructing the AI engine request payload, then the system includes the last five customer interactions and all active issue tags with the request.
Graceful Fallback Handling
Given the AI recommendation engine is unreachable or exceeds a 2-second response timeout, when an invocation is attempted, then the system returns a predefined default messaging template to the agent without errors and logs the failure for review.
Recommendation Accuracy Validation
Given a test suite of 100 predefined sentiment-dip scenarios with expected recommendations, when running the AI engine, then at least 90% of returned suggestions must match or exceed the expected relevance and empathetic tone.
Performance under Load
Given 1000 concurrent sentiment-dip invocation requests, when simulated over a 5-minute period, then at least 95% of all AI recommendation responses must be returned within the 500ms SLA.
Messaging Template Library
"As a support agent, I want access to pre-defined message templates so that I can send empathetic, on-brand communications quickly."
Description

Develop a centralized repository of customizable messaging templates linked to specific issue types and sentiment levels. The library should support tagging, versioning, and localization, and allow agents to preview and modify templates before sending. By standardizing empathetic responses and accelerating message creation, this feature enhances consistency, brand tone, and response speed across support and success teams.

Acceptance Criteria
Template Search and Filtering
Given an agent enters a keyword or selects filters for issue type, sentiment level, version, or locale, when the search is executed, then the system displays only templates matching all selected criteria within 2 seconds.
Template Version Management
Given an agent views a template’s details, when they access the version history, then the system lists all prior versions with timestamps and author information, and allows reverting to any version with a single click.
Template Localization Preview
Given an agent selects a target locale for a template, when they open the preview, then the template displays in the correct language and formatting (including right-to-left support) or falls back to the default language with a visible translation missing warning.
Template Customization Before Sending
Given an agent chooses a template, when they enter the editor, then they can modify text, placeholders, links, and basic formatting, see live character counts, and save changes as a draft without altering the original template.
Template Tagging and Organization
Given an agent assigns tags to a new or existing template, when they type in the tag field, then the system suggests existing tags and allows creation of new tags, and enables filtering the template list by one or multiple tags.
Escalation Workflow Orchestration
"As a customer success manager, I want at-risk issues escalated automatically so that they are handled by senior staff without delay."
Description

Implement automated escalation workflows that route unresolved or high-risk cases to senior support tiers based on customizable rules. Workflows should include multi-channel notifications, SLA tracking, and integration with external ticketing systems. This orchestration ensures that critical issues bypass standard queues and reach the appropriate stakeholders without manual intervention, reducing response times and mitigating churn risk.

Acceptance Criteria
Routing High-Risk Cases to Senior Support
Given a support case is flagged as high-risk and remains unresolved beyond the customizable threshold, when the SLA threshold is exceeded, then the system automatically routes the case to the designated senior support tier.
Multi-Channel Notifications on Escalation
Given a case is escalated, when the escalation workflow triggers, then the system sends notifications via email, SMS, and Slack to all configured stakeholders within 1 minute.
SLA Tracking and Alerting
Given an active case with an assigned SLA, when SLA milestones (50%, 75%, 100%) are reached, then the system generates internal alerts and updates the case status in real time.
Integration with External Ticketing Systems
Given a case is escalated, when the workflow executes, then a corresponding ticket is created or updated in the external ticketing system (e.g., Zendesk, Jira) with matching metadata and a link back to the original case.
Customizable Escalation Rules Management
Given an administrator configures escalation rules via the UI, when rules are saved, then the system applies the new rules to all incoming cases without requiring a restart and logs the configuration change.
Real-Time Sentiment Monitoring Dashboard
"As a success team lead, I want a dashboard showing real-time sentiment changes and past interventions so that I can monitor risk and team performance."
Description

Extend the PulsePanel dashboard to include live sentiment trend visualizations, alert statuses, and intervention histories. Incorporate interactive charts, filters by client or issue type, and drill-down capabilities into individual recommendation logs. This provides team leads with immediate situational awareness, enabling them to monitor fluctuations in customer sentiment, review past interventions, and measure the effectiveness of actions taken.

Acceptance Criteria
Real-Time Sentiment Trend Visualization
Given the dashboard is loaded, when new sentiment data arrives, then the sentiment trend chart updates within 2 seconds showing the latest data points without a full page reload.
Sentiment Alert Status Notification
Given a client sentiment score falls below the predefined threshold, when the threshold breach occurs, then an alert icon appears next to the client name and a notification banner is displayed within 5 seconds.
Interactive Chart Filtering by Client
Given multiple clients are listed, when a user selects a specific client filter, then the sentiment trend chart, alert statuses, and intervention history panels refresh to display data only for the selected client within 3 seconds.
Drill-Down into Recommendation Logs
Given an alert status is active, when a user clicks the alert icon or intervention history entry, then a detailed log modal opens showing timestamped AI-driven recommendations and associated user actions.
Historical Intervention Effectiveness Comparison
Given the user selects a date range, when the range is applied, then the dashboard displays a comparative chart of sentiment trends before and after interventions, with percentage change calculated and displayed.

Outreach Orchestrator

Automates proactive customer communications based on real-time sentiment triggers. Users can configure multi-channel campaigns (email, chat, SMS) that activate when negative tone surges, ensuring timely check-ins and reinforcing customer trust.

Requirements

Real-Time Sentiment Detection Engine
"As a customer success manager, I want to see real-time sentiment scores for incoming messages so that I can quickly identify and address negative feedback."
Description

Develop a sentiment analysis engine that processes incoming customer messages in real time across email, chat, and SMS channels. The engine should accurately classify customer tone as positive, neutral, or negative using machine learning models, and surface sentiment scores within the Outreach Orchestrator dashboard. This feature ensures timely identification of at-risk customers, enabling proactive engagement before issues escalate.

Acceptance Criteria
Email Message Sentiment Classification
Given a customer email is received, when the sentiment engine processes the email, then it classifies the tone as positive, neutral, or negative with at least 85% accuracy within 1 second.
Chat Message Real-Time Sentiment Display
Given a live chat message is sent, when the engine analyzes it in real time, then the sentiment score appears on the Outreach Orchestrator dashboard within 500 milliseconds.
SMS Sentiment Trigger for Outreach
Given an incoming SMS message, when negative sentiment is detected, then an automated flag is created and triggers the predefined communication workflow.
Dashboard Sentiment Score Visualization
Given multiple messages are processed, when sentiment scores are updated, then the dashboard refreshes and displays the latest aggregated scores for email, chat, and SMS within 2 seconds.
High Volume Message Processing
Given a burst of 100 simultaneous messages across channels, when the engine processes them, then 95% are classified with the defined accuracy thresholds and within response latency requirements.
Multi-Channel Campaign Builder
"As a customer success manager, I want a drag-and-drop interface to create multi-channel campaigns so that I can efficiently set up personalized outreach workflows."
Description

Implement a visual campaign builder that allows users to design and configure outreach sequences for email, chat, and SMS. The builder should support drag-and-drop steps, scheduling options, channel selection, and conditional branching based on customer actions or sentiment triggers. This requirement empowers users to craft complex, automated communication workflows without developer assistance.

Acceptance Criteria
Drag-and-Drop Step Configuration
Given the user opens the campaign builder When they drag a step from the palette into the workflow Then the step appears in the exact drop location and the sequence updates accordingly And the new step order persists after saving and reloading the builder
Channel Selection and Scheduling
Given a step in the workflow is selected When the user chooses a communication channel (email, chat, or SMS) And sets a send time or delay relative to the previous step Then the builder displays the selected channel icon and scheduled time And the configuration saves successfully
Conditional Branching Configuration
Given the user adds a conditional branch to the workflow When they define a rule based on customer action or sentiment trigger Then the branch conditions are validated and displayed in the builder And the workflow executes the correct branch path during simulation
Sentiment-Triggered Campaign Activation
Given a customer record's sentiment score crosses the defined negative threshold When the system evaluates sentiment in real-time Then the configured campaign sequence automatically activates within the defined timeframe And the user receives a notification of campaign initiation
Preview and Validation of Campaign Flow
Given the campaign design is complete When the user enters preview mode Then the builder simulates all steps, channels, scheduling, and branching paths And displays a summary of the end-to-end customer journey for review
Automated Trigger Configuration
"As a customer success manager, I want to configure automated triggers based on sentiment thresholds so that campaigns launch seamlessly when customers show signs of dissatisfaction."
Description

Create a trigger configuration module enabling users to define sentiment-based rules that automatically launch outreach campaigns. Users should be able to specify threshold values for negative sentiment, volume of negative interactions, or frequency within a time window. The system will monitor these triggers continuously and activate the appropriate campaign when conditions are met.

Acceptance Criteria
Sentiment Threshold Configuration
Given the user sets the negative sentiment threshold to 0.7, When the user saves the configuration, Then the system persists the threshold value and displays a success confirmation message.
Interaction Volume Threshold Setup
Given the user specifies a volume of 50 negative interactions, When the user confirms the input, Then the system validates the value is within range, saves it, and shows a confirmation notification.
Time Window Frequency Definition
Given the user defines a 24-hour time window with a frequency threshold of 3 negative interactions, When the configuration is saved, Then the system schedules monitoring and confirms the setup.
Real-Time Trigger Activation
Given monitoring conditions meet all defined thresholds, When the system detects the trigger criteria, Then the appropriate outreach campaign is automatically launched and a log entry is generated.
Multi-Channel Campaign Verification
Given a trigger event occurs, When the outreach campaign executes, Then messages are sent via the selected channels (email, SMS, chat) and delivery statuses are recorded.
Dynamic Message Personalization
"As a customer success manager, I want to personalize outreach messages with customer-specific data so that communications feel tailored and relevant."
Description

Enhance the outreach engine with dynamic content capabilities that personalize messages using customer data and sentiment context. Templates should support variable tokens (e.g., customer name, product usage metrics, recent issues) and conditional content blocks. Personalized messaging increases relevance and engagement, reinforcing customer trust.

Acceptance Criteria
Personalizing Email with Customer Data
Given a customer with valid profile and usage metrics in the system, when an email outreach is generated using a template with variable tokens, then the customer’s name, usage metrics, and sentiment context tokens are correctly replaced and displayed in the final message.
Conditional Block for Recent Issues
Given a customer with one or more unresolved issues in the last 30 days, when an outreach message is rendered, then the conditional content block detailing recent issues is included; otherwise, it is omitted.
Multichannel Token Replacement
Given predefined templates for email, SMS, and chat channels, when a sentiment-triggered outreach is initiated, then all variable tokens within each channel’s template are accurately replaced with the corresponding customer data across all channels.
Fallback for Missing Data
Given a template includes optional tokens and a customer lacks certain data fields (e.g., recent purchase date), when the message is generated, then default fallback text is displayed in place of missing data without causing rendering errors.
Real-Time Sentiment Trigger
Given the system detects a negative sentiment surge from a customer interaction, when the outreach trigger conditions are met, then a personalized message with dynamic content is dispatched within five minutes of detection.
Campaign Performance Analytics
"As a customer success manager, I want detailed analytics on campaign performance so that I can measure impact and optimize future outreach."
Description

Integrate an analytics dashboard to track key metrics for each outreach campaign, including open rates, click-through rates, response rates, sentiment shifts, and conversion outcomes. Provide filtering by channel, segment, and trigger type. This insight helps teams evaluate campaign effectiveness and refine strategies for better customer retention.

Acceptance Criteria
Filter Campaign Metrics by Channel
Given the user has access to the analytics dashboard and selects the 'Email' channel filter and a valid date range, when the dashboard refreshes, then only email campaign metrics (open rate, click-through rate, response rate, sentiment shift, conversion outcome) for the selected period are displayed.
Segment-Specific Performance View
Given the user chooses a customer segment (e.g., Enterprise) from the segment filter, when the dashboard updates, then metrics for outreach campaigns targeted at the selected segment are shown correctly.
Trigger-Type Analytics Generation
Given the user filters by negative sentiment triggers, when the dashboard loads, then performance metrics for campaigns activated by negative tone surges are presented with accurate counts and rates.
Real-Time Sentiment Shift Tracking
Given a live campaign is running, when real-time data is received, then the dashboard updates sentiment shift graphs within 60 seconds of data arrival.
Conversion Outcome Reporting
Given the user selects 'Conversion Outcome' view, when the view is rendered, then the dashboard displays the number and percentage of campaigns that led to a resolved support ticket or upsell within the selected timeframe.

Cluster Compass

Automatically groups related incidents into visual clusters on the map, making it easy to spot hotspots of recurring issues. By guiding you through concentrated problem areas, this feature accelerates diagnosis and prioritization, ensuring your team tackles high-impact issues first.

Requirements

Automated Incident Clustering
"As a Customer Success Manager, I want incidents automatically grouped by similarity so that I can quickly identify and address recurring issues without manual sorting."
Description

Implement a backend service that automatically groups related incidents based on similarity metrics such as error codes, tags, and timestamps. This service will leverage machine learning algorithms to identify patterns and recurring problems, reducing manual triage effort and enabling quicker identification of systemic issues. It integrates with the existing incident ingestion pipeline to ensure seamless clustering as new data arrives.

Acceptance Criteria
Real-Time Incident Clustering
Given a new incident is ingested into the pipeline When the clustering service processes the incident Then it must assign the incident to an existing or new cluster within 5 seconds and tag it with the correct cluster ID
Historical Data Re-Clustering
Given a batch of 10,000 existing incidents When the re-clustering job runs Then at least 95% of incidents should remain in their original clusters or be re-assigned to more accurate clusters without data loss
Cluster Label Accuracy
Given a cluster of related incidents When the service generates a summary label Then the label must include the top three most frequent error codes or tags representing that cluster
Cluster Map Visualization Integration
Given the front-end map visualization view When clusters are retrieved from the backend Then each cluster must appear at the correct geographic coordinates with an accurate incident count and color-coded severity
Performance Under Peak Load
Given a sustained ingestion rate of 1,000 incidents per minute When the clustering service is under peak load Then it must process incidents without dropping messages and maintain an average processing latency below 10 seconds
Fault Tolerance and Recovery
Given a temporary database outage When the clustering service attempts to write cluster assignments Then it must retry failed writes up to three times and queue incidents for later processing without data loss
Visual Hotspot Map
"As a Customer Success Manager, I want to see visual hotspots of recurring issues on a map so that I can pinpoint problem areas immediately and allocate resources efficiently."
Description

Display clusters on an interactive map interface using color-coded heatmap overlays and proportional markers. Hotspots will visually represent areas with high concentrations of related incidents, allowing users to identify problem zones at a glance. The map integrates with the product’s UI framework and supports geographic and logical groupings.

Acceptance Criteria
Map Initialization with Incident Clusters
Given incident data is available and the user opens the Visual Hotspot Map When the map loads Then cluster markers appear on the map within 3 seconds and accurately reflect current incident data
Color-Coded Heatmap Overlay Accuracy
Given multiple clusters with varying incident counts When the heatmap overlay is applied Then areas with higher incident density are colored with warmer colors and lower density with cooler colors according to the defined color scale
Proportional Marker Sizing
Given clusters of incidents When the map displays markers Then each marker’s size is proportional to the number of incidents in that cluster, scaling between the minimum and maximum defined marker sizes
Geographic and Logical Grouping Toggle
Given the map shows incident clusters When the user switches between geographic and logical grouping modes Then the clusters re-render instantly to reflect the selected grouping without page reload
Interactive Cluster Drill-Down
Given clusters are displayed on the map When the user clicks on a cluster marker Then a detailed list of incidents in that cluster is shown in a side panel with incident metadata
Interactive Cluster Drill-down
"As a Customer Success Manager, I want to click on a hotspot to see detailed information about grouped incidents so that I can understand root causes and prioritize my response."
Description

Enable users to click on a cluster hotspot to view detailed metadata, including total incident count, breakdown by issue type, timeframes, impacted customers, and links to individual tickets. This drill-down functionality provides context and actionable information for rapid diagnosis and resolution.

Acceptance Criteria
Cluster Drill-down Trigger
Given a user sees a cluster hotspot on the map, when they click the hotspot, then a drill-down panel appears showing the cluster's metadata and list of incidents.
Metadata Display Accuracy
Given the drill-down panel is open, then it displays the total incident count, issue type breakdown, and timeframe filters that match the selected cluster data.
Impacted Customers Listing
Given the metadata panel is open, then a list of impacted customers is shown with customer name, account ID, and number of incidents.
Ticket Link Navigation
Given the drill-down panel lists individual tickets, when a user clicks a ticket link, then the system navigates to the ticket detail page in a new tab.
Cluster Panel Responsive Layout
Given a user opens the drill-down panel on different screen sizes, then the panel adapts layout without truncating metadata or requiring horizontal scrolling.
Real-time Cluster Updates
"As a Customer Success Manager, I want cluster data to update in real time so that I have the most current insights and can act quickly to prevent escalations."
Description

Ensure clusters update in real time as new incidents are ingested, using WebSocket or push notification mechanisms to refresh the hotspot map without requiring manual reloads. This dynamic update capability guarantees that users are always working with the latest data and can respond to emerging issues promptly.

Acceptance Criteria
Live Incident Arrival Updates
Given the user is viewing the hotspot map When a new incident is ingested via WebSocket Then the affected cluster updates its count and boundaries automatically within 2 seconds
Connection Interruption Recovery
Given the user’s network connection drops and reconnects When the WebSocket connection is reestablished Then the map fetches any missed incidents and updates all clusters to reflect the latest state without manual reload
High-Volume Incident Spike Handling
Given the system ingests a rate of 100 incidents per minute When the incidents are received via push notifications Then clusters update incrementally without causing UI lag or requiring more than 300ms rendering time per update
Viewport-Independent Cluster Sync
Given new incidents occur outside the current map viewport When they are ingested Then their respective clusters update in the background without degrading performance of the visible clusters
No Manual Refresh Required
Given real-time updates are enabled When incidents arrive Then the user never needs to manually reload the page or map to see updated clusters
Cluster Filtering and Search
"As a Customer Success Manager, I want to filter and search clusters based on criteria such as time range and severity so that I can concentrate on the most critical issues relevant to my goals."
Description

Provide advanced filtering options (e.g., time range, severity level, tags, customer segments) and search capabilities to refine the clusters displayed on the map. Users can customize views to focus on specific incident subsets, facilitating targeted analysis and efficient troubleshooting.

Acceptance Criteria
Time Range Filter Application
Given the user opens the Cluster Compass map, When the user sets a start and end date in the time range filter, Then only clusters containing incidents reported within that date range are displayed, and clusters outside the range are hidden.
Severity Level Filtering
Given the cluster map is visible, When the user selects one or more severity levels from the filter dropdown, Then only clusters grouped by incidents matching those severity levels appear on the map.
Tag-Based Cluster Refinement
Given clusters are displayed, When the user applies one or more incident tags in the filter panel, Then the map updates to show only clusters containing incidents tagged accordingly, excluding all others.
Customer Segment Selection
Given the user needs focused analysis, When the user selects specific customer segments from the segment filter, Then clusters representing incidents from those selected segments are shown, and clusters outside those segments are removed.
Keyword Search Within Clusters
Given clusters are listed on the map, When the user enters a search term in the cluster search bar, Then only clusters whose incident descriptions or tags contain the search term are displayed.

Timeline Trail

Overlay incident clusters with a temporal slider that reveals how recurring issues evolve over time. This dynamic timeline helps you identify when patterns emerge or escalate, enabling proactive interventions before problems become widespread.

Requirements

Temporal Slider Control
"As a customer success manager, I want to drag a temporal slider to adjust the visible time window so that I can explore incident clusters at different time scales."
Description

A draggable temporal slider positioned above the incident timeline that allows users to dynamically adjust the time window parameters. As users move the slider handles, the displayed incident clusters update in real time, reflecting data from the selected time range. This component integrates with the visualization engine and backend time-based query APIs, enabling CSMs to explore incident evolution from granular (e.g., hourly) to extended (e.g., monthly) views. The expected outcome is an intuitive tool for precise temporal analysis, helping teams identify when recurring issues first appeared or intensified.

Acceptance Criteria
Real-time cluster update on slider adjustment
Given the user moves a slider handle to a new position, when the handle is dragged or released, then the incident clusters in the timeline update within 300ms to reflect only incidents within the selected time range.
Switching between hourly and monthly granularity
When the selected time window is under 24 hours, then the slider steps in 1-hour increments; and when over 30 days, then the slider steps in 1-day or 1-month increments, with incident clusters aggregated accordingly.
Slider boundary constraints at data limits
The slider handles must not move beyond the earliest or latest available data timestamps; any attempted movement beyond limits leaves handles in place and displays a ‘No data beyond this limit’ tooltip.
Rapid continuous slider movement
During continuous dragging with more than 5 adjustments per second, the system throttles API calls to 2 per second and updates the displayed clusters within 500ms after the final adjustment.
Backend API integration for time-based queries
Each slider adjustment triggers a backend request with start_time and end_time matching handle values, and the returned incident clusters correspond exactly to the selected time range within ±1 second tolerance.
Incident Cluster Visualization
"As a customer success manager, I want to see incidents grouped into color-coded clusters on the timeline so that I can quickly identify recurring issues and their trends."
Description

An interactive visualization layer that groups related incidents into clusters based on tags, severity, or similarity metrics, and displays them as color-coded peaks along the timeline. Each cluster’s height represents incident volume within the selected timeframe, and hovering reveals summary details. This feature leverages the existing auto-tagging engine and uses a charting library (e.g., D3.js) for rendering. The integration ensures seamless updates when data filters or time ranges change. The expected outcome is a clear visual map of recurring issues, enabling rapid trend analysis.

Acceptance Criteria
Viewing Cluster Peaks Over Default Timeframe
Given the Timeline Trail view is opened with the default time range selected When the incident clusters render Then each cluster peak is displayed with height proportional to the number of incidents in that period and colored according to predefined severity tiers
Filtering Clusters by Severity
Given a user applies a severity filter (e.g., Critical or High) When the filter is activated Then only clusters matching the selected severity levels are shown on the timeline and clusters for other severities are hidden
Adjusting Time Range Slider
Given the user adjusts the temporal slider to a custom date range When the slider range is set Then the visualization updates to show clusters within the new range and the cluster heights and colors accurately reflect incident volume and severity for that period
Hovering Over Cluster for Details
Given the user hovers the mouse over a cluster peak When the hover action is detected Then a tooltip appears displaying the cluster’s tag summary, incident count, and time window
Dynamic Update Upon Data Filter Change
Given additional data filters (e.g., tag or similarity filter) are modified When a filter change is applied Then the incident clusters update immediately to reflect only incidents that meet all active filter criteria without requiring a page refresh
Pattern Highlighting and Alerts
"As a customer success manager, I want the system to highlight abnormal cluster growth and send me alerts so that I can address potential problems before they escalate."
Description

Logic to detect when incident clusters exceed configurable thresholds or show accelerating growth patterns, automatically highlighting these segments on the timeline with distinct markers or color changes. When patterns meet alert criteria, the system triggers notifications through the existing alert framework (email, in-app, or Slack). This requirement involves implementing threshold-based detection algorithms in the backend and UI markers in the frontend. The expected outcome is proactive identification of risk patterns, prompting timely interventions before escalation.

Acceptance Criteria
Threshold Exceeded Cluster Alert
Given a configurable incident threshold of N incidents in 24 hours, When a cluster records more than N incidents within any 24-hour sliding window, Then the system highlights the corresponding segment on the timeline with a distinct marker and color change.
Accelerating Growth Pattern Alert
Given a configured growth acceleration rate of X%, When the incident count for any cluster increases by more than X% between two consecutive time intervals, Then the system flags the cluster as “accelerating” and updates its marker on the timeline accordingly.
UI Highlights for Detected Patterns
Given any detected alert condition, When the user views or refreshes the timeline, Then the UI displays color-coded segments and tooltips for each alert, showing incident counts, timestamps, and alert type.
Notification Dispatch for Pattern Alerts
Given a newly detected pattern alert, When the alert is triggered, Then the system sends notifications via email, in-app, and Slack within 2 minutes and logs each notification with channel, timestamp, and delivery status.
Configurable Threshold Management
Given the user updates threshold settings in the application configuration, When the new settings are saved, Then all subsequent pattern detections use the updated thresholds immediately, and the change is recorded in the system audit log.
Interactive Timeline Filtering
"As a customer success manager, I want to filter the timeline by criteria like tag or severity so that I can focus on specific incident types relevant to my customers."
Description

A filter panel adjacent to the timeline that enables users to apply multiple criteria—such as date range, tag category, severity, and customer segment—to refine the displayed incident clusters. Selections dynamically update the timeline view in real time, and users can combine filters to drill down into specific subsets of data. This feature integrates with the existing query builder and ensures that filtered results maintain cluster grouping logic. The expected outcome is enhanced data exploration, allowing CSMs to focus on the most relevant incidents for their analysis.

Acceptance Criteria
Date Range Filter Application
Given the timeline is displayed, when the user applies a specific date range filter in the panel, then the timeline view updates within 200ms to display only incident clusters whose timestamps fall within the selected range.
Tag Category Multi-Selection
Given multiple tag categories are available, when the user selects or deselects multiple tag categories, then the timeline dynamically updates to show only clusters containing incidents with the selected tags while preserving cluster grouping logic.
Severity Level Drill Down
Given incident severity levels are defined, when the user filters by one or more severity levels, then only clusters containing incidents at the selected severity levels appear and the visual indicators (e.g., color coding) reflect the chosen severities.
Customer Segment Combination Filter
Given incidents are tagged by customer segment, when the user applies both a segment filter and at least one other filter (date range, tag, or severity), then the timeline displays only those clusters meeting all selected criteria without performance degradation.
Query Builder Integration Validation
Given an existing query is loaded in the query builder, when the user modifies timeline filters, then the underlying query updates to include the new filter parameters and the timeline view reflects the combined results in real time.
Real-Time Data Synchronization
"As a customer success manager, I want the timeline to update in real time as new incidents are reported so that I always have the latest view of issue trends."
Description

A real-time synchronization mechanism using WebSockets or server-sent events to push new incident data to the timeline visualization as it arrives. This ensures that users see the latest incident clusters without manual refresh. The implementation connects to the existing data ingestion pipeline, handles data streaming, and updates the front-end model and chart in real time. The expected outcome is a continuously up-to-date view, allowing CSMs to respond immediately to emerging issues.

Acceptance Criteria
WebSocket Connection Establishment
Given a valid user session and network connectivity When the timeline page loads Then a persistent WebSocket connection is opened to the data ingestion pipeline without errors
Real-Time Incident Update
Given new incident data arrives on the server When the server pushes the update via WebSocket Then the incident cluster appears on the timeline within 2 seconds
Automatic Reconnection Handling
Given a temporary network interruption When the WebSocket connection is lost Then the client automatically attempts to reconnect up to 5 times with exponential backoff
Data Integrity Validation
Given incoming incident events When the client receives each event Then the incident details (timestamp, tag, severity) exactly match the server payload before rendering
Performance Under High Throughput
Given a stream of 100+ incidents per second When data is pushed continuously Then the UI update latency remains under 500ms and the timeline visualization remains responsive

Predictive Pinpoint

Leverages historical incident data and machine learning to forecast potential future problem hotspots. By predicting where and when issues may arise, you can allocate resources preemptively, reducing resolution times and preventing customer disruptions.

Requirements

Real-time Incident Data Ingestion
"As a data engineer, I want a unified, real-time data ingestion pipeline so that the predictive model can access the latest incident information for accurate forecasting."
Description

Implement a robust, scalable pipeline that continuously collects and normalizes historical and live incident reports from multiple sources (support tickets, CRM, monitoring tools). This will ensure the predictive engine has comprehensive, up-to-date data, enabling accurate forecasting of issue hotspots. Integration should support incremental updates, data validation, and schema evolution to accommodate new data fields without downtime.

Acceptance Criteria
Pipeline Connection Established
Given the ingestion pipeline is configured with valid source credentials and endpoints When a live incident report is submitted from any integrated system Then the pipeline ingests the report within 30 seconds and acknowledges successful receipt
Data Normalization with Schema Evolution
Given historical and live incident data containing both existing and newly introduced fields When the normalization process runs Then all records conform to the current target schema and any new fields are automatically incorporated without data loss or downtime
Incremental Data Updates Processing
Given the pipeline last processed incidents up to timestamp T0 When new incident records are created or updated after T0 Then only those new or changed records are ingested and appended to the data store, and processedCount equals number of new/updated records
Data Validation and Error Handling
Given incoming incident records with missing or malformed mandatory fields When validation rules are applied during ingestion Then invalid records are routed to an error queue with detailed error logs, and valid records continue through the pipeline without interruption
Scalable Throughput under Peak Load
Given a simulated load of 10,000 incident events per minute When the pipeline processes incoming data continuously for one hour Then average end-to-end ingestion latency remains below 5 seconds and no data loss occurs
Machine Learning Model Training and Tuning
"As a data scientist, I want an automated ML training pipeline so that predictive models stay accurate with evolving incident trends."
Description

Develop and integrate a machine learning framework that automatically trains predictive models on historical incident patterns. Include hyperparameter optimization, cross-validation, and model versioning. Provide tools for periodic retraining and performance monitoring to ensure forecasts remain accurate over time as new data becomes available.

Acceptance Criteria
Initial Model Training Workflow
Given at least six months of historical incident data is available When the automated training job is triggered Then a new model artifact is generated and stored in the model registry within two hours and training metrics (accuracy, precision, recall) are logged and exceed baseline thresholds
Hyperparameter Optimization Execution
Given a defined hyperparameter search space When the optimization process runs Then the system identifies and records the optimal hyperparameter set that improves validation accuracy by at least 5% over the default configuration
Cross-Validation and Performance Benchmarking
Given the trained model and dataset When k-fold cross-validation (k ≥ 5) is executed Then the average validation score and standard deviation are computed and meet or exceed the expected performance threshold
Model Versioning and Deployment
Given a newly trained model artifact When the model versioning step is performed Then the model is registered with a unique version tag including timestamp, hyperparameter details, and performance metrics, and the previous version is archived
Periodic Retraining and Performance Monitoring
Given incoming new incident data and a scheduled retraining interval When the periodic retraining job executes Then a refreshed model is produced, performance drift is evaluated against the active model, and alerts are generated if performance drops by more than 3%
Future Hotspot Heatmap Visualization
"As a customer success manager, I want a visual heatmap of predicted problem areas so that I can easily spot and address potential issues before they escalate."
Description

Design and implement an interactive heatmap within the PulsePanel dashboard that highlights predicted future incident hotspots. Allow users to filter by time window, incident type, and severity. Include intuitive color-coding, tooltips, and zoom controls to help customer success managers quickly identify areas requiring preemptive action.

Acceptance Criteria
Time Window Filter Application
Given the user selects a time window (e.g., past 7 days, 30 days, or custom range) When the selection is applied Then the heatmap updates to display predicted hotspots only within that timeframe and the date range indicator reflects the selected window.
Incident Type and Severity Filter
Given the user selects one or more incident types and severity levels When filters are applied Then the heatmap displays only the hotspots matching the selected types and severities and the filter badges appear active.
Tooltip Information Display
Given the user hovers over a hotspot region on the heatmap When the tooltip appears Then it displays the region name, predicted incident count, top incident type, and prediction confidence percentage.
Zoom and Pan Controls
Given the user uses zoom in/out buttons or scroll gestures When zoom controls are engaged Then the heatmap zooms correctly up to a 4x magnification and the user can pan to any portion of the map without data distortion.
Color-Coding Legend Interpretation
Given the user views the heatmap legend When the map is displayed Then each color gradient corresponds accurately to incident density ranges as defined and the legend is clearly visible adjacent to the heatmap.
Proactive Alert and Notification System
"As a support team member, I want notifications when high-risk hotspots are predicted so that I can allocate resources proactively to prevent customer disruptions."
Description

Create a configurable alerting mechanism that notifies relevant stakeholders (teams, managers) when the model predicts a high-risk hotspot. Support multiple channels such as email, SMS, and in-app notifications. Include adjustable thresholds, escalation rules, and summary dashboards to track alert history and responses.

Acceptance Criteria
Threshold-Based Email Alerts
Given the user has configured a 75% risk threshold for a client account, when the predictive model generates a risk score of 75% or higher, then an email alert must be sent to all designated stakeholders within 2 minutes, containing hotspot details and next steps.
Escalation Rule Activation
Given an alert remains unacknowledged for 30 minutes and escalation rules specify notifying the team manager, when the 30-minute window elapses, then an escalation email and SMS must be sent to the manager within 5 minutes and the event logged in the alert history.
SMS Alert Delivery
Given a stakeholder's mobile number is registered for SMS alerts, when a high-risk hotspot prediction breaches their configured threshold, then an SMS message containing a brief alert summary must be delivered within 3 minutes and delivery status recorded.
In-App Notification Rendering
Given a user is logged into the PulsePanel application, when a new alert is generated for their team, then an in-app notification badge must appear in the Notifications panel and selecting it displays full alert details.
Alert History and Dashboard Summary
Given a user with dashboard access navigates to the Alert History view, then all past alerts for the last 30 days must be displayed in reverse chronological order, with functional filters for status, date range, and stakeholder group, and CSV export must succeed.
Resource Recommendation Engine
"As a customer success lead, I want data-driven resource recommendations so that I can plan staffing and intervene before issues impact customers."
Description

Build an engine that translates predicted hotspots into actionable resource allocation recommendations. Use historical resolution efficiency and team capacity data to suggest optimal staffing levels, skillset assignments, and response timelines. Provide a recommendation dashboard with scenario planning controls to adjust resource variables interactively.

Acceptance Criteria
Forecasted Hotspot Staffing Recommendation
Given a predicted hotspot for the upcoming week with a projected 25% increase in ticket volume, when the engine processes historical resolution efficiency and current team capacity data, then it generates staffing level recommendations per team such that no agent’s workload exceeds 80% of capacity and provides at least two alternative allocation options.
Skillset Assignment Accuracy
Given the predicted hotspot includes multiple issue types requiring distinct skills, when generating resource recommendations, then the engine assigns at least one qualified agent for each identified issue type and flags any skill gaps where no agent meets the required skillset.
Response Timeline Optimization
Given historical resolution time data and current capacity constraints, when proposing response timelines for predicted hotspots, then the engine suggests SLAs that improve average resolution times by a minimum of 10% without exceeding total available agent hours.
Interactive Scenario Planning Controls
Given a user adjusts resource variables such as agent count or shift durations on the scenario planning controls, when inputs are changed, then the recommendation dashboard updates within 2 seconds to reflect new staffing, skill assignments, and response timelines based on the modified variables.
Recommendation Dashboard Data Accuracy
Given the recommendation dashboard is loaded, when data is fetched and displayed, then all staffing suggestions, skill assignments, and response timelines match the engine’s output and source data within a 1% margin of error, and no required fields are missing.

Module Matrix

Displays a matrix view that cross-references product modules with incident frequency and severity. This intuitive grid highlights which areas are most prone to problems, helping product and support teams focus improvements where they’ll deliver the greatest impact.

Requirements

Incident Data Aggregation
"As a Customer Success Manager, I want all incident data from various support channels aggregated and normalized so that the Module Matrix accurately reflects frequency and severity across modules."
Description

Implement a backend data aggregation layer that collects incident reports from all integrated support channels, normalizes the data, and associates each incident with its corresponding product module. This ensures accurate cross-referencing in the Module Matrix and supports real-time analytics by providing a unified data source for incident frequency and severity metrics.

Acceptance Criteria
Data Ingestion from Multiple Channels
Given incident reports are available in email, chat, and ticketing systems When the aggregation layer runs Then all incident reports from these channels are ingested into the system within 5 minutes of submission.
Data Normalization Consistency
Given ingested incident reports contain varying formats and fields When the normalization process executes Then all reports conform to the standard schema with fields 'timestamp', 'moduleId', 'severity', and 'description' populated correctly.
Module Association Accuracy
Given normalized incident reports are processed When the module association logic is applied Then each incident is linked to the correct product module with at least 98% accuracy as verified against the test dataset.
Real-Time Data Availability
Given incidents are ingested and normalized When a user queries the Module Matrix API Then the latest aggregated data reflects all incidents within the last minute without noticeable lag.
Duplicate Incident Handling
Given identical incident reports from the same source within a 10-minute window When the aggregation process runs Then duplicate reports are identified by matching unique identifiers and only a single record is stored.
Interactive Matrix Visualization
"As a Product Manager, I want an interactive matrix that visually highlights module incident hotspots so that I can quickly identify and focus on areas needing attention."
Description

Develop a dynamic, interactive grid-based UI that displays product modules along one axis and incident frequency and severity on the other. Enable intuitive cell color-coding to represent severity levels, tooltips on hover for quick insights, and responsive design to support desktop and tablet views. This interactive visualization helps teams quickly identify hotspots and prioritize module improvements.

Acceptance Criteria
Matrix Data Loading
Given the user navigates to the Module Matrix page When the data fetch completes Then the matrix displays all product modules with their corresponding incident frequency and severity metrics within 2 seconds
Cell Color Coding
Given an incident severity level for a module cell Then the cell background color must match the predefined severity color mapping (e.g., green for Low, yellow for Medium, red for High) consistently across all cells
Tooltip Display on Hover
Given the user hovers over any module cell Then a tooltip appears within 200ms showing the module name, incident count, and severity level and the tooltip is fully visible without clipping
Responsive Layout Adaptation
Given the user views the matrix on desktop or tablet (screen width between 768px and 1920px) When the viewport changes Then the matrix layout adjusts cell size, font, and spacing to remain legible without horizontal scrolling
Interactive Cell Filtering
Given the user clicks on a severity legend item or a specific cell When the filter is applied Then the matrix highlights only modules matching the selected severity and displays a clear filter indicator with an option to reset
Accessibility Compliance
Given the matrix interface is rendered Then all interactive elements are reachable via keyboard navigation and include ARIA labels and roles for screen readers
Filtering and Drill-Down Capabilities
"As a Support Team Lead, I want to filter the matrix by date and severity and drill down into individual incidents so that I can analyze and resolve recurring issues more effectively."
Description

Provide advanced filtering options by date range, severity level, and module category, and enable drill-down into specific matrix cells to view detailed incident logs, associated tickets, and root-cause analysis. This feature allows users to refine the matrix view to relevant timeframes and severity thresholds, and to investigate individual incidents directly within the Module Matrix interface.

Acceptance Criteria
Date Range Filter Application
Given the Module Matrix is displayed When the user selects a valid start and end date Then the matrix updates to show only incidents within that date range And the total incident count reflects the filtered data
Severity Level Filter Usage
Given the Module Matrix is displayed When the user selects one or more severity levels Then only incidents matching the selected severity levels are shown And the matrix visually highlights the filtered cells accordingly
Module Category Filter Selection
Given the Module Matrix is displayed When the user selects one or more module categories Then the matrix updates to display only incidents from the selected modules And the module filter can be cleared to restore the full matrix view
Drill-Down into Matrix Cell
Given the Module Matrix is displayed When the user clicks on a specific matrix cell Then a detail panel opens showing incident logs with ticket ID, date, severity, and summary And the panel allows sorting and pagination of the logs
Root-Cause Analysis Access from Detail Panel
Given the detail panel for a matrix cell is open When the user clicks 'View Root-Cause Analysis' Then the panel displays the root-cause summary and linked ticket references And if no analysis is available, a 'Analysis Pending' message is shown
Real-Time Matrix Updates
"As a Support Specialist, I want the Module Matrix to update in real-time so that I can monitor incident trends continuously without refreshing the page."
Description

Implement a near real-time refresh mechanism that updates the Module Matrix as new incident data is ingested. Use WebSocket or polling strategies to push updates to the UI without requiring manual page reloads. This ensures that teams always view the latest incident trends and can act on emerging issues promptly.

Acceptance Criteria
WebSocket Connection Establishment
Given the Module Matrix UI is open, When the application initializes the real-time update feature, Then a WebSocket connection is successfully established within 3 seconds.
Incident Data Push via WebSocket
Given the WebSocket connection is active, When a new incident is ingested, Then the incident is pushed and displayed in the Module Matrix within 5 seconds.
Fallback to Polling Mechanism
Given the WebSocket connection fails, When the polling fallback is triggered, Then the system retrieves new incident data every 10 seconds and updates the Module Matrix accordingly.
UI State Preservation During Update
Given the user has applied filters or sorted the Module Matrix, When real-time updates occur, Then the current filter, sort order, and scroll position remain unchanged after data refresh.
High Frequency Data Handling
Given a surge of incident events arrives within a 1-minute window, When real-time updates are processed, Then the Module Matrix consolidates updates and refreshes the UI no more than once every 2 seconds.
Export and Sharing Functionality
"As a CSM, I want to export the matrix data and share it with stakeholders so that we can collaborate on improvement plans."
Description

Enable users to export the Module Matrix view and underlying data in multiple formats (CSV, PDF) and to generate shareable links or embed codes for presentations and reports. Include options to customize the export scope, such as selected date ranges, module subsets, and severity filters, to facilitate collaboration and decision-making across teams.

Acceptance Criteria
Export Data in CSV Format
When a user selects CSV export and confirms, the system generates a .csv file containing Module Matrix data; The file respects selected date range, module subsets, and severity filters; The download initiates automatically within 5 seconds and includes proper headers and UTF-8 encoding
Generate PDF Report
When a user selects PDF export, the system produces a PDF document with a formatted Module Matrix view; The PDF includes headers, footers with date range and filters applied; The file size does not exceed 5MB for a 1-year date range; The PDF download starts within 8 seconds
Shareable Link Generation
Given valid user permissions, when the user requests a shareable link, then the system generates a unique URL; The link encodes date range, modules, and severity filters; Accessing the link displays the same Module Matrix view without additional login for authorized users; The link remains valid for the configured expiration time
Embed Code Provision
When a user selects embed code option, then the system provides an iframe snippet that renders the Module Matrix with applied filters; The embed code includes width and height attributes; Pasting the code into an HTML page displays the matrix correctly without requiring additional scripts
Custom Date Range Export
Given a user selects a custom date range, when exporting in any format, then only data within the specified range is included; The date input validates start date is before end date; Invalid date selections prompt an inline error and prevent export

Filter Focus

Offers advanced filtering by customer segment, geography, issue type, and time period, allowing you to zero in on specific cohorts or regions. Customizable views ensure you extract relevant insights quickly, streamlining root cause analysis and decision-making.

Requirements

Customer Segment Filter
"As a Customer Success Manager, I want to filter feedback by customer segment so that I can quickly identify and address issues specific to each cohort."
Description

The system shall allow users to select one or multiple customer segments (e.g., enterprise, SMB, startups) to filter feedback data. It integrates with the existing tagging engine to display only issues relevant to chosen segments, enabling targeted root-cause analysis and cohort-specific insights. By focusing on segment-based trends, CSMs can tailor support strategies and prioritize high-impact groups.

Acceptance Criteria
Single Customer Segment Selection
Given the user is on the feedback dashboard and initial state shows all feedback, When the user selects the "Enterprise" segment from the customer segment filter, Then only feedback items tagged with "Enterprise" are displayed in the data view.
Multiple Customer Segment Selection
Given the user is on the feedback dashboard, When the user selects both "SMB" and "Startup" segments from the filter, Then the data view displays only feedback items tagged with either "SMB" or "Startup".
Integration with Tagging Engine
Given feedback items are processed by the tagging engine with segment labels, When the customer segment filter is applied, Then the system retrieves and displays feedback items based on the tagging engine’s segment assignments without errors.
Clearing Customer Segment Filter
Given one or more segments are selected in the customer segment filter, When the user clicks the "Clear Filters" button, Then all segment selections are removed and feedback from all segments is displayed.
Persisting Filter Selections Across Sessions
Given the user has selected one or more customer segments and logs out, When the user logs back in, Then the previously selected customer segment filter settings are automatically reapplied.
Geographic Filter
"As a Customer Success Manager, I want to filter feedback by geography so that I can focus on region-specific issues and tailor my outreach accordingly."
Description

Enables users to filter feedback by geography, including region, country, state, or custom geographic zones. The feature leverages GIS data and integrates with the feedback database to restrict sentiment and issue reports to selected locales. This allows teams to uncover location-based trends, compliance issues, and regional performance variations.

Acceptance Criteria
Filtering Feedback by Country
Given the user opens the Filter Focus panel, when the user selects one or more countries from the geographic filter dropdown, then the feedback list updates to show only entries where the feedback's country metadata matches the selected countries.
Applying Regional Aggregate Filter
Given the user defines a custom region composed of multiple states, when the user applies this region filter, then feedback entries from all included states aggregate and display correctly while excluding feedback from outside these states.
State-Level Filtering
Given the user selects a specific state from the geographic filter, when the filter is applied, then only feedback entries with state metadata equal to the selected state appear in the results.
Clearing Geographic Filters
Given the user has applied one or more geographic filters, when the user clicks the 'Clear Filters' button, then all geographic filter selections reset and the full feedback list is restored.
Performance Under Large Dataset
Given the user applies any geographic filter on a dataset exceeding 100,000 feedback entries, when the filter is executed, then the filtered results display within two seconds with no errors or timeouts.
Issue Type Filter
"As a Customer Success Manager, I want to filter feedback by issue type so that I can focus on resolving the most critical categories of problems."
Description

Allows selection of auto-tagged issue categories—such as performance, usability, billing, and security—to filter feedback. The filter leverages the platform’s auto-tagging engine to ensure accurate classification and surfaces only the selected categories. This helps CSMs concentrate on critical problem areas and allocate resources effectively.

Acceptance Criteria
Single Issue Type Selection
Given multiple auto-tagged feedback items exist, When the user selects the "Performance" issue type filter, Then only feedback items tagged with "Performance" are displayed.
Multiple Issue Types Selection
Given feedback items tagged across categories, When the user selects both "Performance" and "Billing" issue type filters, Then the list displays feedback tagged with either "Performance" or "Billing" only.
Issue Type Filter Persistence
Given the user applies the "Usability" and "Security" filters, When the user navigates away and returns to the feedback view, Then the previously selected filters remain applied and the filtered results persist.
No Matching Issue Types
Given feedback items exist for other categories, When the user selects an issue type with no tagged items (e.g., "Security"), Then the system displays a message indicating no feedback is available for the selected issue type.
Clear Issue Type Filters
Given one or more issue type filters are applied, When the user clicks the "Clear Filters" button, Then all issue type filters are removed and the full unfiltered feedback list is displayed.
Accurate Classification Filtering
Given the auto-tagging engine has labeled feedback items, When the user applies an issue type filter, Then only items with matching auto-tags appear and no items from other categories are included.
Time Period Filter
"As a Customer Success Manager, I want to filter feedback by time period so that I can analyze trends and measure the effectiveness of interventions over different intervals."
Description

Provides a flexible time-range picker with options for custom date ranges and predefined intervals (e.g., last 7 days, month-to-date, quarter). Integrates with timeline visualizations to enable temporal trend analysis of feedback and issue occurrences. This helps users track changes over time and measure the impact of mitigations.

Acceptance Criteria
Predefined Interval Selection
Given the user opens the time-range picker When the user selects the "Last 7 days" predefined interval Then the system applies the filter from today minus 7 days to today And the timeline visualization updates to display data within that range
Custom Date Range Selection
Given the user enters a start date and an end date via the date-picker inputs When the user confirms the selection by clicking "Apply" Then the system applies the filter inclusive of both dates And the filtered data and timeline visualization reflect the custom date range
Timeline Visualization Integration
Given a valid time-range filter is active When the timeline visualization renders Then the visualization displays data points only within the selected time range And the x-axis scales proportionally to the filtered interval
Invalid Date Range Handling
Given the user selects an end date earlier than the start date When the user attempts to apply the filter Then the system displays an inline validation error stating "End date must be after start date" And the "Apply" button remains disabled until corrected
Prevent Future Date Selection
Given the date-picker calendar is open When the user attempts to select a date after the current date Then future dates are disabled and cannot be selected And an informational tooltip states "Future dates are not selectable"
Large Date Range Performance
Given the user selects a date range exceeding one year When the filter is applied Then the system retrieves and displays data within 2 seconds And a non-blocking notification appears if filtering large ranges may affect performance
Custom View Management
"As a Customer Success Manager, I want to save my filter configurations as custom views so that I can quickly apply them without reconfiguring each time."
Description

Enables users to save, name, and load filter configurations as reusable custom views. These views are stored within user profiles and can be shared with team members. This feature streamlines recurring analysis workflows by eliminating the need to reconfigure multiple filters for frequent use cases.

Acceptance Criteria
Save Custom View
Given a user has configured filter settings, When the user clicks 'Save as Custom View' and provides a unique name, Then the system stores the filter configuration under that name in the user's profile and displays it in the 'Custom Views' list.
Load Custom View
Given the user has one or more saved custom views, When the user selects a view from the 'Custom Views' list, Then the system applies the saved filters to the dashboard and displays 'Custom View: [Name]' indicator.
Rename Custom View
Given the user has a saved custom view, When the user selects 'Rename' and enters a new unique name, Then the system updates the view name in the profile and reflects the change in the 'Custom Views' list.
Share Custom View
Given the user has a saved custom view, When the user selects 'Share' and enters a valid teammate username, Then the system grants view access to the teammate and they appear in the 'Shared With' list.
Access Shared Custom View
Given a teammate has been granted access to a shared custom view, When they open their 'Custom Views' list, Then the shared view appears with the owner's name and can be loaded but not edited.
Live Dashboard Refresh
"As a Customer Success Manager, I want the filtered view to refresh in real-time so that I always have the latest insights without manual reloads."
Description

Automatically refreshes filtered dashboards and reports in real-time as new feedback arrives or filter criteria are modified. The feature integrates with the backend streaming pipeline to deliver up-to-the-minute insights without requiring manual page reloads, ensuring CSMs always have access to the latest data.

Acceptance Criteria
Automated Refresh on New Feedback Arrival
Given the CSM is viewing a dashboard with active filters When new client feedback is ingested by the backend streaming pipeline Then the dashboard automatically refreshes within 2 seconds without manual reload And the new feedback appears in the filtered results
Immediate Filter Modification Update
Given the CSM modifies the filter criteria (e.g., customer segment, issue type, time period) When the new filters are applied Then the dashboard updates in real time within 1 second And only data matching the updated filters is displayed
Concurrent Dashboard Interaction
Given the CSM is interacting with dashboard elements (e.g., drilling into a chart or expanding a widget) When new feedback data arrives Then only the relevant data sections refresh without losing the current interaction context And open widgets maintain their state
Offline Recovery and Catch-up Refresh
Given the CSM was offline or disconnected When the connection is restored Then the dashboard automatically retrieves and displays all missed feedback updates within 5 seconds And no data gaps or duplicate entries are present
High-volume Data Stream Performance
Given a sustained high volume of incoming feedback events (over 100 events per second) When live refresh is enabled Then the dashboard refresh latency remains under 3 seconds And system CPU and memory usage stay within defined operational thresholds

RiskScore Beacon

Delivers a real-time churn probability score for each account by blending feedback sentiment and usage metrics. Color-coded indicators make it easy to spot high-risk customers at a glance, enabling teams to prioritize outreach and reduce potential churn before it escalates.

Requirements

Real-Time Data Ingestion
"As a customer success manager, I want the RiskScore Beacon to receive real-time sentiment and usage data so that I can trust the churn probabilities reflect current account health."
Description

The system must continuously collect and unify customer feedback sentiment and product usage metrics in real-time from multiple sources such as support tickets, NPS surveys, and feature usage logs. This pipeline ensures up-to-date data feeds into the RiskScore Beacon to maintain accuracy. It should support robust error handling, data validation, and scalable throughput to handle growing data volumes.

Acceptance Criteria
Continuous Feedback Stream
Given support ticket data arrives, when ingested, then sentiment and metadata are extracted and unified into the pipeline within 5 seconds.
Real-Time Survey Sync
Given NPS survey responses are submitted, when received by the system, then validate schema, ingest valid entries within 5 seconds, and log any validation errors.
Scalable Throughput Handling
Given a peak of 10,000 events per minute, when ingestion load increases, then the system auto-scales to maintain processing latency under 2 seconds per event without data loss.
Error Handling and Retry
Given a transient source failure, when an ingestion attempt fails, then retry up to 3 times with exponential backoff and generate an alert if all retries fail.
Deduplication Verification
Given duplicate events from the same source, when processed, then ingest only unique events, discard duplicates, and record the count of discarded items.
Churn Probability Algorithm
"As a product owner, I want the system to compute a churn likelihood score for each customer so that I can identify at-risk accounts accurately."
Description

Implement a configurable algorithm that blends normalized sentiment scores and usage metrics to calculate a churn probability for each account. The model should allow weighting adjustments for each factor, apply smoothing to avoid volatility, and retrain periodically based on historical churn events. The output score must be normalized to a 0–100% scale.

Acceptance Criteria
Configurable Weight Adjustment Scenario
Given the admin sets sentiment weight to 70% and usage weight to 30%, when calculating the churn score for a test account, then the resulting score reflects a 70/30 contribution ratio within a 2% margin of error.
Normalization to Percentage Scale Scenario
Given raw churn risk values ranging from observed minimum to maximum historical values, when normalizing the score, then output values must map linearly to a 0–100% scale with 0 raw mapping to 0% and maximum raw mapping to 100%.
Smoothing Application Scenario
Given two consecutive churn score calculations where input metrics change abruptly, when smoothing is applied with a maximum delta of 10 points per update, then the score change between updates must not exceed 10 points.
Retraining Schedule Execution Scenario
Given historical churn event data accumulated since the last training, when the scheduled weekly retraining window begins, then the algorithm retrains automatically, completes within the defined SLA, and writes a retraining log entry with timestamp and performance metrics.
Real-Time Score Response Scenario
Given new feedback sentiment and usage metrics arriving in the system, when the data is ingested, then the churn probability score for the affected account updates within 5 seconds, even under an ingestion load of 1000 updates per minute.
Color-Coded Risk Indicator
"As a customer success manager, I want to see a color-coded risk badge next to each customer to quickly identify and prioritize high-risk accounts."
Description

Develop a UI component that displays the churn score as a color-coded badge (green, yellow, red) next to each account in the dashboard. The color thresholds should be configurable via the admin panel, and the indicator should include hover details showing exact score and contributing factors. It must be responsive and accessible to comply with WCAG standards.

Acceptance Criteria
Admin Adjusts Color Thresholds for Risk Scores
Given the admin is on the admin panel's risk settings page When they define or modify thresholds for green, yellow, and red risk scores Then the thresholds are saved and applied, reflecting in the UI badges on the dashboard according to the new values.
User Views Color-Coded Risk Badges on Dashboard
Given the user navigates to the accounts dashboard When the dashboard loads Then each account displays a badge colored green, yellow, or red based on its churn risk score per configured thresholds.
User Hovers Over Badge to See Score and Factors
Given the user hovers over a risk badge When the tooltip appears Then it shows the exact numeric churn risk score and a list of top contributing factors.
Color-Coded Badge Responsiveness on Mobile Devices
Given the user accesses the dashboard on a mobile device When the viewport width is below 768px Then badges adjust in size correctly and tapping a badge displays the score and factors in a touch-friendly overlay.
Accessibility of Color-Coded Badges
Given any user interacts with the dashboard badges When using keyboard navigation or a screen reader Then badges have appropriate ARIA labels, focus states are visible, tooltips are accessible, and color contrast meets WCAG AA standards.
Risk Trend Visualization
"As a customer success manager, I want to view the risk score trend over the past months so that I can see whether interventions are working."
Description

Provide a historical trend chart for each account that displays the churn probability over time. The chart should allow zooming, panning, and date range selection, and overlay key events (e.g., support tickets, major product releases). This assists teams in understanding risk evolution and planning interventions.

Acceptance Criteria
Account Risk Trend Overview
Given I navigate to an account's dashboard, when the system retrieves churn probability data, then the risk trend chart loads within 2 seconds and displays daily churn scores over the past 90 days with appropriate axis labels and color coding.
Zoom into Risk Trend
Given I view the risk trend chart, when I select a zoom tool or pinch gesture, then the chart zooms into the selected time window, adjusts the x-axis accordingly, and maintains data accuracy without lag.
Pan Through Risk Timeline
Given the risk trend chart is displayed, when I click and drag or use scroll controls, then the chart pans smoothly across the timeline, updating visible data points without performance degradation.
Filter by Custom Date Range
Given I open the date range selector, when I choose a specific start and end date, then the chart refreshes to show churn probability only within that range, and date labels update to reflect the new bounds.
Overlay Key Events
Given the risk trend chart is visible, when I toggle event overlays, then support tickets, product releases, and other key events appear as markers on the chart with tooltips showing event details on hover.
Automated Risk Alerts
"As a customer success manager, I want to receive alerts when a customer's risk score spikes so that I can proactively reach out before they churn."
Description

Enable configurable alerts that notify designated team members when an account's churn probability exceeds defined thresholds or changes significantly within a short period. Notifications should be sent via email, Slack, or in-app bell, with links back to the dashboard and risk factors. Alert rules must be editable per team or customer segment.

Acceptance Criteria
Threshold Breach Email Notification
Given an account’s churn probability exceeds a configured threshold, when the threshold is breached, then an email is sent within 5 minutes to designated team members containing the account ID, current score, threshold value, and a direct link to the dashboard with risk factors.
Significant Score Change Slack Alert
Given an account’s churn probability changes by more than 10% within one hour, when the change is detected, then a Slack message is posted within 2 minutes to the configured channel including account name, previous score, new score, percentage change, and dashboard link.
In-App Bell Notification with Dashboard Link
Given an alert is triggered for an account, when a user logs into the application, then the in-app bell icon displays a badge, and clicking it opens an alert panel listing the alert details and a link to the risk factors section of the dashboard.
Customizable Alert Rule Editing
Given a team member edits an existing alert rule, when they save the changes, then the system validates the new configuration, updates the rule, displays a confirmation message, and ensures future alerts adhere to the updated thresholds and channels.
Segment-Based Alert Delivery
Given an alert rule is scoped to a specific customer segment, when an account within that segment triggers an alert, then only the designated users for that segment receive notifications via their selected channels.

Trajectory Tracker

Visualizes the evolution of churn risk over custom time windows, highlighting key drivers like sudden drops in engagement or spikes in negative feedback. This timeline helps Customer Success Managers understand patterns and intervene at the most critical moments.

Requirements

Custom Time Window Selector
"As a Customer Success Manager, I want to select custom time ranges for churn risk analysis so that I can focus on the specific intervals relevant to each client’s engagement patterns."
Description

Enable users to define and adjust custom time intervals for analyzing churn risk trajectories, providing flexibility to focus on daily, weekly, monthly, or custom date ranges. This feature integrates seamlessly with the existing PulsePanel interface, allowing Customer Success Managers to tailor their analysis period according to individual client needs and business cycles. Users can save frequently used time windows, ensuring quick access and consistency across dashboards. The expected outcome is improved precision in identifying periods of heightened risk and enabling proactive intervention planning.

Acceptance Criteria
Define Custom Time Window
Given the user accesses the Trajectory Tracker, When the user selects a start date and an end date for a custom time window, Then the churn risk timeline updates to display data only within the specified date range and reflects daily, weekly, and monthly granularity as chosen.
Adjust Existing Time Window
Given a custom time window is already applied, When the user modifies the start or end date, Then the visualization refreshes to show the updated timeline immediately without full page reload.
Save Frequently Used Time Windows
Given a valid custom time window is defined, When the user clicks 'Save Time Window' and provides a name, Then the time window is stored in the user's profile and appears in the 'Saved Windows' list with the provided name.
Load Saved Time Window in Dashboard
Given the user has one or more saved time windows, When the user selects a saved window from the 'Saved Windows' list, Then the Trajectory Tracker automatically applies that time window and updates the timeline accordingly.
Validation of Invalid Date Range
Given the user enters a start date that is after the end date or a date range exceeding 365 days, When the user attempts to apply the selection, Then an inline error message is displayed and the selection is rejected until corrected.
Churn Risk Timeline Visualization
"As a Customer Success Manager, I want to view an interactive timeline of churn risk scores so that I can easily understand how risk levels have evolved and identify critical moments."
Description

Render an interactive timeline chart that maps churn risk scores over the selected time window, displaying trends, inflection points, and overall trajectory. This visualization uses color coding to indicate risk severity and supports zooming and panning for detailed inspection. Integrated tooltips display precise risk values and timestamps. By visually communicating risk evolution, the feature empowers managers to quickly grasp when and how churn risk changes over time.

Acceptance Criteria
Timeline Chart Rendering
Given a user selects a custom time window for churn analysis When the Churn Risk Timeline Visualization loads Then a continuous line chart is rendered mapping churn risk scores against timestamps with clearly labeled time (x-axis) and risk score (y-axis) axes
Risk Severity Color Coding
Given churn risk scores plotted on the timeline When a score falls within predefined low, medium, or high risk thresholds Then the corresponding portion of the line chart is colored green for low, yellow for medium, and red for high risk
Zooming and Panning Interaction
Given the timeline chart is displayed When the user zooms in/out or pans across the timeline Then the chart dynamically adjusts to show the focused date range with updated axis scales and data points
Tooltip Information Display
Given the user hovers over or taps a data point on the chart When the interaction occurs Then a tooltip appears showing the precise churn risk value, timestamp, and any relevant annotation
Inflection Point Highlighting
Given the churn risk timeline displays continuous data When there is a significant change in trend (e.g., a 10% increase or decrease over successive points) Then an inflection marker is displayed at that point with a visual cue (e.g., dot or annotation)
Key Driver Highlighting
"As a Customer Success Manager, I want the system to highlight events driving churn risk changes so that I can quickly understand root causes and take targeted actions."
Description

Automatically identify and annotate major factors contributing to changes in churn risk—such as engagement drops, negative feedback spikes, or support ticket volume—and visually flag these events on the timeline. Each annotation links to the underlying data that triggered the change, enabling deep dives into root causes. This integration enhances context for churn risk shifts, making it easier to correlate specific events with risk fluctuations and prioritize corrective actions.

Acceptance Criteria
Engagement Drop Event
Given the churn risk timeline with a usage drop of ≥20% over a 24-hour window When the system ingests the latest engagement metrics Then an annotation labeled 'Engagement Drop' appears at the exact timestamp of the drop And the annotation includes a link to the detailed engagement report
Negative Feedback Spike Event
Given the churn risk timeline shows ≥5 negative feedback items within a single day When feedback data is processed Then a 'Negative Feedback Spike' annotation is added at the corresponding timestamp And users can click the annotation to view the individual feedback entries
Support Ticket Volume Surge
Given a 30% increase in support ticket volume for a client within 48 hours When ticket data is imported into the system Then the timeline displays a 'Ticket Volume Surge' annotation at the surge point And the annotation links to a filtered view of all related support tickets
Multiple Key Drivers Overlap
Given two or more driver events (e.g., engagement drop and ticket surge) occur within a four-hour span When events are detected Then separate annotations for each driver appear along the timeline without overlap And hovering over annotations highlights all simultaneous events
Root Cause Deep Dive Access
Given any driver annotation on the timeline When a user clicks the annotation marker Then a modal opens showing the raw data and metadata that triggered the event And the modal includes navigation controls to explore historical context
Anomaly Detection Alerts
"As a Customer Success Manager, I want to receive alerts for sudden churn risk anomalies so that I can investigate and address issues before they escalate."
Description

Implement real-time anomaly detection to automatically monitor churn risk metrics and notify users when unusual patterns—such as sudden spikes or drops—occur within the defined time window. Notifications are delivered via in-app banners and optional email or Slack integrations. This continuous monitoring ensures that managers are promptly alerted to critical changes, reducing response time and preventing escalations.

Acceptance Criteria
Real-Time Spike Detection in Churn Risk
Given the Trajectory Tracker is monitoring churn risk metrics within a selected time window When the system identifies a spike in churn risk that exceeds the customizable threshold Then an anomaly detection alert is generated within 5 seconds of detection
Detection of Sudden Drop in Engagement
Given customer engagement metrics are being continuously tracked When a sudden drop of 20% or more in engagement occurs within a 1-hour interval Then an anomaly detection alert is created and logged in the alert history
In-App Notification Display
Given an anomaly detection alert has been generated When the user is active in the PulsePanel application Then an in-app banner appears within 3 seconds, displaying the alert type, affected account, and suggested actions
Email Notification Sending
Given a user has enabled email notifications for anomaly detection When an anomaly alert is generated Then an email is sent to the user’s registered address within 1 minute, containing alert details and a link to the Trajectory Tracker view
Slack Notification Handling
Given Slack integration is configured and authorized When an anomaly detection alert occurs Then a message is posted to the designated Slack channel with alert summary, timestamp, and link to the detailed report
Drill-down Insight Panel
"As a Customer Success Manager, I want to drill down into detailed metrics for a specific risk event so that I can fully understand the context and plan effective remedies."
Description

Provide a contextual drill-down panel that appears when users click on any point or annotation in the churn risk timeline. The panel displays detailed metrics—like customer engagement scores, sentiment analysis results, and recent support interactions—for the selected period. Users can filter, sort, and export this data, enabling deeper analysis and facilitating data-driven interventions within the PulsePanel ecosystem.

Acceptance Criteria
Opening Drill-down Panel from Churn Spike
Given a user views the churn risk timeline When they click on a point or annotation representing a spike in churn risk Then a drill-down panel appears displaying detailed metrics for that selected period
Filtering Metrics in Drill-down Panel
Given the drill-down panel is open When the user selects filter criteria (e.g., engagement score range, sentiment category) Then the panel refreshes to show only metrics matching the selected filters
Sorting Drill-down Data
Given the drill-down panel displays a table of metrics When the user applies a sort order (e.g., ascending by engagement score or descending by negative feedback count) Then the table rows reorder accordingly and retain the applied sort when filters change
Exporting Drill-down Insights
Given the drill-down panel is open When the user clicks the export button and chooses a format (CSV or XLSX) Then the system generates and downloads a file that includes the displayed metrics, filters, sort order, and the selected time period in the filename
Closing the Drill-down Panel
Given the drill-down panel is open When the user clicks the close icon or outside the panel area Then the panel closes and the user is returned to the full churn risk timeline view

Prescriptive Playbook

Generates tailored retention strategies and action plans for at-risk accounts based on historical resolution success. From recommended messaging templates to escalation steps, it empowers teams with proven tactics to win back customers efficiently.

Requirements

Historical Data Aggregator
"As a customer success manager, I want the system to pull and integrate all relevant historical data for each account so that I can base retention strategies on a complete and accurate information set."
Description

Automatically collect and unify customer data from CRM platforms, support tickets, product usage logs, and past retention efforts into a centralized repository. This aggregation provides a comprehensive view of each account’s interaction history, enabling accurate analysis and informed decision-making.

Acceptance Criteria
CRM Data Import Verification
- Given a valid CRM platform connection, when the data sync job executes, then all new and updated customer records are imported into the centralized repository within 5 minutes. - Required fields (customer name, email, account ID) are populated with no null or invalid values. - Duplicate records are identified by unique account ID and merged into a single record without loss of data.
Support Ticket Integration Check
- When a support ticket is created or updated in any connected ticketing system, then the ticket appears in the centralized repository within 3 minutes. - Captured ticket metadata includes ticket ID, status, priority, creation date, and assigned agent. - No ticket records are lost or duplicated during ingestion.
Product Usage Logs Consolidation
- Given daily usage logs from the product telemetry service, when logs are ingested, then all records are aggregated into the repository with a data completeness rate of 99.9%. - Key usage metrics (session duration, feature usage count) match raw telemetry within a 1% discrepancy margin. - Failed log entries are retried up to three times and escalate to an alert on persistent failures.
Past Retention Efforts Data Sync
- When historical retention intervention records are imported, then each record contains intervention date, method, outcome, and owner. - Records older than 12 months are archived but remain queryable via the repository’s archival interface. - No intervention records are skipped or duplicated during synchronization.
Unified Repository Data Consistency
- When data from all sources is unified, then a reconciliation report confirms 100% alignment of record counts across sources. - Data transformation error rate does not exceed 0.1% per synchronization cycle. - Any discrepancy exceeding the threshold triggers an automated alert to the data operations team.
Risk Scoring Engine
"As a customer success manager, I want to know which accounts are at highest risk so that I can focus my proactive retention efforts where they will have the greatest impact."
Description

Analyze aggregated account data to calculate a dynamic risk score for each customer. The engine evaluates factors like usage decline, ticket escalations, and sentiment trends to identify accounts most likely to churn, allowing teams to prioritize outreach.

Acceptance Criteria
Calculating Risk Score for Inactive Accounts
Given an account with a 30% usage decline over the past 7 days, When the risk scoring engine processes the data, Then the account's risk score increases by at least 15 points relative to the previous period.
Adjusting Risk Score Based on Escalated Tickets
Given an account has two escalated support tickets in the last 24 hours, When the engine analyzes ticket escalation data, Then the risk score reflects a high-risk threshold (>=80).
Incorporating Sentiment Trends into Risk Assessment
Given customer sentiment shifts from positive to negative over three consecutive feedback entries, When the engine aggregates sentiment trends, Then the risk score increases by at least 10 points compared to sentiment-neutral baseline.
Real-time Risk Score Update on Data Refresh
Given new usage and ticket data is ingested at the top of each hour, When the risk engine runs its scheduled update, Then all account risk scores are recalculated and available on the dashboard within 5 minutes.
Prioritizing Outreach Based on Risk Score
Given a dashboard view for customer success managers, When accounts are sorted by descending risk score, Then the top 10 accounts displayed have risk scores equal to or above the high-risk threshold (>=70).
Prescriptive Strategy Generator
"As a customer success manager, I want the tool to provide a tailored playbook with proven tactics so that I can efficiently implement strategies most likely to win back at-risk customers."
Description

Leverage risk scores and historical resolution outcomes to automatically generate tailored retention playbooks. Each playbook includes recommended messaging templates, escalation steps, and best-practice tactics proven effective for similar accounts.

Acceptance Criteria
High-Risk Account Playbook Generation
Given a customer risk score of 0.8 or higher and at least three similar resolved cases, when the CSM initiates playbook generation, then the system provides a playbook with at least three personalized messaging templates, two escalation steps, and three proven tactics, accompanied by a confidence score of 75% or higher.
Mid-Risk Account Playbook Generation
Given a customer risk score between 0.5 and 0.79 with one or more resolved cases, when playbook generation is triggered, then the output includes two messaging templates, one escalation step, two best-practice tactics, and a confidence score of at least 60%.
No Historical Data Playbook Generation
Given a customer with no matching historical resolution data, when a playbook is requested, then the system returns a default retention playbook containing one generic messaging template, a standardized escalation plan, and one fallback tactic with an advisory note on limited personalization.
Urgent Escalation Playbook Generation
Given an account whose risk score has increased above 0.8 within 24 hours, when generating the playbook, then the playbook includes immediate escalation steps with a phone call script and priority email template, and clearly marks the playbook as high priority.
Playbook Export and Distribution
Given a generated retention playbook, when the CSM exports it, then the system produces both a downloadable PDF and an email-ready formatted document preserving all templates, steps, and annotations.
Template Customization Interface
"As a customer success manager, I want to personalize the suggested messaging templates and action steps so that communications align with my account relationships and brand voice."
Description

Offer an intuitive in-app editor enabling users to review, edit, and personalize recommended messaging templates and action steps. Changes are tracked and suggestions are kept consistent with the playbook’s strategic goals.

Acceptance Criteria
Editing a Recommended Template
Given a user selects a recommended messaging template from the playbook library When the in-app editor loads Then the default template content must appear in editable fields
Saving Customized Template Changes
Given the user modifies template text or formatting When the user clicks Save Then the system must persist the changes, update the template version, and reflect the custom version in the template list
Version Tracking of Template Edits
Given a user has edited a template multiple times When the user opens the version history Then the system must display a chronological list of all saved versions with timestamps, editor names, and change summaries
Reverting to Original Template Version
Given a user views a customized template When the user selects Revert to Original Then the template content must reset to the initial recommended version and all custom versions must remain accessible in history
Maintaining Playbook Strategy Suggestions
Given a user edits messaging content that conflicts with playbook strategy tags When the user makes the conflicting change Then the system must provide inline suggestions to align edits with strategic goals and highlight compliance issues
Playbook Export and Sharing
"As a customer success manager, I want to export and share the tailored playbook with my team and stakeholders so that everyone can align on and contribute to the retention plan."
Description

Enable users to export the generated playbook in formats such as PDF and CSV, and share directly via email or integrated communication tools like Slack. This ensures stakeholders can access, review, and collaborate on retention strategies.

Acceptance Criteria
Export Playbook as PDF
Given a generated playbook exists, when the user selects "Export to PDF", then a PDF file is downloaded containing the complete playbook content with correct formatting and images.
Export Playbook as CSV
Given a generated playbook exists, when the user selects "Export to CSV", then a CSV file is downloaded with each play represented as a row and all relevant fields correctly populated.
Email Playbook to Stakeholders
Given a generated playbook exists, when the user chooses "Share via Email", enters at least one valid email address, and confirms, then the system sends an email with the playbook attached and displays a success notification.
Share Playbook via Slack
Given the Slack integration is configured, when the user selects "Share to Slack", chooses a channel or user, and confirms, then the playbook is posted as a file with a message in the specified Slack channel.
Handle Export and Share Errors Gracefully
Given an export or share action fails due to network or validation errors, when the failure occurs, then the user sees a clear error message explaining the issue and an option to retry the action.

Fusion Insights Dashboard

Unifies usage statistics, feedback trends, and sentiment analysis into a single interactive view. By correlating feature adoption with customer sentiment, teams can pinpoint root causes of dissatisfaction and proactively address issues driving churn.

Requirements

Real-time Data Aggregation
"As a customer success manager, I want to see real-time consolidated usage and sentiment data in one interface so that I can quickly identify and address emerging customer issues."
Description

The system ingests and consolidates usage statistics, customer feedback, and sentiment analysis data from multiple sources (application telemetry, support tickets, and survey responses) in near real-time (within 5 minutes). It ensures data normalization, de-duplication, and storage into a unified data model, enabling up-to-date insights and minimizing data latency for timely decision-making.

Acceptance Criteria
Real-time Telemetry Ingestion
Given an application telemetry event is generated, When the event arrives at the ingestion endpoint, Then the system normalizes the data schema, deduplicates against existing records, and stores the processed event into the unified data model within 5 minutes.
Support Ticket Feedback Aggregation
Given a new support ticket is created or updated, When the ticket data is ingested, Then the system extracts relevant feedback fields, normalizes formats, removes duplicate entries, and persists the record in the unified model within 5 minutes.
Survey Response Sentiment Processing
Given a customer submits a survey response, When the response is received, Then the system performs sentiment analysis, normalizes response attributes, de-duplicates by response ID, and stores the result in the unified data model within 5 minutes.
Unified Data Model Consistency
Given multiple data types (telemetry, tickets, surveys) are aggregated, When stored in the unified model, Then all records adhere to the defined schema, maintain referential integrity, and pass validation rules with zero schema errors.
Latency Monitoring and Alerting
Given the ingestion pipeline processes incoming data, When any data type fails to update within a 5-minute window, Then the system generates an alert to the monitoring dashboard and logs the latency breach for investigation.
Interactive Dashboard Visualization
"As a customer success manager, I want to interactively explore and filter dashboard metrics so that I can dive into specific trends and understand user behavior in detail."
Description

The dashboard provides an interactive interface featuring customizable visual components including line charts, bar graphs, heatmaps, and pivot tables. Users can apply multi-level filters, drill down into specific data segments, and adjust time ranges. The interface supports responsive design and smooth transitions, ensuring users can navigate and explore data seamlessly.

Acceptance Criteria
Time Range Customization
Given the user is on the dashboard interface When the user selects a predefined or custom date range Then all visual components (line charts, bar graphs, heatmaps, pivot tables) update within 500ms to reflect data for the selected range and display the selected time range prominently
Multi-Level Filtering
Given the user applies filters at multiple levels (e.g., region, product, time frame) When the filter selections are confirmed Then the dashboard visualizations update accordingly, showing only data matching all selected filters and the filter summary shows all active filters
Data Drill-Down Interaction
Given the user clicks on a data point within any visualization When the user drills down into that segment Then the dashboard displays a detailed breakdown of sub-segments in a new chart or table, maintaining context and allowing the user to navigate back to the higher level
Responsive Layout Adjustment
Given the user accesses the dashboard on devices of varying screen sizes When the viewport width changes Then the dashboard layout rearranges visual components to maintain readability and functionality, ensuring no horizontal scrolling is required and interactive elements remain accessible
Visualization Transition Smoothness
Given the user navigates between different visualization types or reloads data When the transition occurs Then animations complete within 300ms without jank, and there is no data flicker or rendering artifacts
Sentiment-Usage Correlation Mapping
"As a product analyst, I want to see correlations between feature adoption and customer sentiment so that I can pinpoint root causes of dissatisfaction and inform product improvements."
Description

Implements analytics that overlay feature adoption metrics with sentiment trend lines, highlighting correlations and potential causal relationships. The system applies statistical methods to identify significant co-movements, flags periods where negative sentiment spikes coincide with drops in usage, and presents insights via annotated timeline views and correlation matrices.

Acceptance Criteria
Dashboard Alert for Correlated Trends
Given the system has collected at least 30 days of usage and sentiment data, When a negative sentiment spike of ≥20% coincides with a usage drop of ≥15% within the same 24-hour period, Then the dashboard displays an alert highlighting the exact dates, sentiment change percentage, and usage change percentage.
Statistical Significance Annotation
Given correlation analysis is run on feature usage and sentiment scores, When the Pearson correlation coefficient p-value is less than 0.05, Then the correlation matrix displays a statistical significance icon next to the corresponding correlation cell, and a tooltip explains the significance level.
Annotated Timeline View Accuracy
Given a flagged period is identified, When a user clicks on the timeline chart for that period, Then the system displays annotations including start date, end date, average sentiment score, average usage count, and a summary of detected co-movements.
Filtered Correlation Matrix Update
Given a user applies a date range filter or selects a subset of features, When the filter is applied, Then the correlation matrix refreshes to only show correlation coefficients for the selected timeframe and features, and all significance markers and values are recalculated accordingly.
Correlation Insights Export
Given a user initiates an export of the correlation report, When the export completes, Then the generated PDF or CSV includes the correlation matrix, annotated timeline view, alert summary, and statistical significance indicators matching the on-screen dashboard.
Customizable Alert Rules
"As a customer success manager, I want to configure custom alerts for critical metric thresholds so that I am immediately notified of potential churn risks."
Description

Allows users to define and manage custom alert rules based on threshold conditions for any dashboard metric (e.g., a 10% drop in sentiment score over seven days). Alerts can trigger notifications via email, in-app messages, or integrations with communication tools like Slack. The module includes rule templates, scheduling options, and a management interface to edit or disable alerts.

Acceptance Criteria
Threshold-Based Email Alert Triggering
Given a user defines a custom alert rule with a metric threshold and selects email notification, When the monitored metric crosses the threshold, Then an email is automatically sent to all configured recipients within 5 minutes containing the metric name, current value, threshold value, and timestamp.
In-App Notification Display
Given a user is logged into the Fusion Insights Dashboard, When an alert condition is met for any active custom rule, Then an in-app banner notification is displayed with a link to view the detailed alert rule and recent metric trends.
Slack Integration Alert Delivery
Given a user integrates their Slack workspace and selects one or more channels for notifications, When an alert triggers, Then a formatted message is posted to the chosen Slack channel within 2 minutes, including rule name, metric change details, and a link back to the dashboard.
Rule Template Application
Given a user selects a pre-built rule template from the template library, When the template is applied, Then all relevant fields (metric, threshold, duration, notification channels) are pre-filled correctly and the user can save the rule without additional manual input.
Alert Rule Management Interface
Given a user accesses the Alert Rules management interface, When they choose to edit or disable an existing rule, Then changes are saved or the rule is disabled immediately, and the updated status is reflected in the rule list without requiring a page refresh.
Role-based Access Control
"As an administrator, I want to manage user roles and permissions for the dashboard so that team members have appropriate access levels and data security is maintained."
Description

Implements a granular permission system where administrators can assign roles (e.g., viewer, analyst, manager) with specific access rights to dashboard features and data segments. The system enforces restrictions on data visibility, editing capabilities, and alert management. All permission changes are logged for audit compliance.

Acceptance Criteria
Assigning Roles to Users
Given an administrator is on the RBAC configuration page and selects a user account, When they assign the 'analyst' role to that user and save changes, Then the user’s role is updated immediately and the user’s access permissions reflect the 'analyst' role on next login.
Enforcing Data Visibility Restrictions
Given a user with the 'viewer' role is logged in, When they navigate to the Fusion Insights Dashboard, Then they can view only the data segments permitted for the 'viewer' role and receive an 'Access Denied' error when attempting to access restricted segments.
Editing Capability Control
Given a user with the 'analyst' role is logged in, When they attempt to edit dashboard configuration settings, Then the system allows or blocks the edit operations based on the 'analyst' role permissions defined in RBAC.
Alert Management Permissions
Given a user with the 'manager' role is logged in, When they attempt to create, modify, or delete alert rules, Then the system permits these actions only if the 'manager' role includes alert management permissions, otherwise displays an error message.
Permission Change Audit Logging
Given any role assignment or permission modification is performed by an administrator, When the change is saved, Then the system logs the change with timestamp, admin user ID, affected user, previous role, and new role in the audit log.

Alert Optimizer

Configures dynamic alerts that trigger when an account’s churn score crosses customizable thresholds. Notifications can be fine-tuned by segment, region, or product line, ensuring the right teams are instantly informed and can take timely retention actions.

Requirements

Configurable Threshold Builder
"As a customer success manager, I want to define custom churn score thresholds per segment so that I receive alerts relevant to each account type."
Description

A user interface module that allows customer success managers to define, customize, and adjust churn score thresholds at the account, segment, region, or product line level. It supports setting multiple threshold tiers (e.g., warning, critical), color-coded indicators, and real-time validation to ensure that values are within acceptable ranges. This requirement integrates seamlessly with the core Alert Optimizer engine, ensuring that updates to thresholds immediately affect alert evaluation logic and trigger conditions.

Acceptance Criteria
Creating a New Threshold Tier
Given the user is on the Configurable Threshold Builder page When they click 'Add Threshold Tier' Then a new threshold tier entry is added with default values including tier name, threshold value, and color-coded indicator
Editing an Existing Threshold Tier
Given an existing threshold tier is displayed When the user updates the threshold value or color Then the real-time validation confirms the value is within acceptable ranges and the updated indicator reflects the chosen color
Deleting a Threshold Tier
Given multiple threshold tiers are present When the user deletes a selected threshold tier Then the tier is removed from the list and the remaining tiers reorder correctly without gaps
Input Validation for Out-of-Range Values
Given the user enters a threshold value outside the permitted range When the user attempts to save the configuration Then an inline error message is displayed and the save action is prevented
Threshold Update Reflected in Alert Evaluations
Given the user has saved new threshold configurations When a churn score crosses a threshold Then the Alert Optimizer engine triggers alerts based on the updated thresholds without delay
Segment and Region Rule Engine
"As a customer success manager, I want to apply different alert rules to specific customer segments so that notifications are targeted appropriately."
Description

A back-end service component that evaluates churn score thresholds against account attributes such as segment, region, and product line. It supports nested logical conditions, weight-based scoring adjustments, and dynamic rule updates without redeploying the entire system. The engine must also log rule evaluations for auditing and allow rollback to previous configurations if needed.

Acceptance Criteria
Segment-Based Threshold Evaluation
Given an account belongs to segment 'Enterprise', When its churn score exceeds the threshold defined for 'Enterprise', Then the engine flags the account as 'At Risk' and generates an alert.
Region-Based Threshold Evaluation
Given an account is located in the 'EMEA' region, When its churn score crosses the region-specific threshold, Then the system triggers a notification routed to the EMEA customer success team.
Nested Condition Handling
Given a rule with nested logical conditions (e.g., segment='SMB' AND (region='APAC' OR productLine='Pro')), When an account meets the combined criteria, Then the engine correctly applies the rule and updates the churn risk status.
Dynamic Rule Update Without Redeploy
Given a rule is modified through the admin interface, When the update is saved, Then the new rule takes effect immediately without requiring a system redeployment.
Audit Log Generation
Given a rule evaluation occurs, When the engine processes the rule, Then it logs the timestamp, applied rule ID, account ID, and evaluation result to the audit log.
Rule Configuration Rollback
Given a previous rule configuration exists in the history, When an administrator triggers a rollback action, Then the engine reverts to the selected configuration and applies it immediately.
Multi-Channel Notification Router
"As a team lead, I want alerts sent via our preferred communication channels so that we can respond quickly using our usual workflows."
Description

A flexible notification delivery system that routes alerts through multiple channels (e.g., email, Slack, SMS, Microsoft Teams) based on configurable preferences at the team or individual level. It includes templates for each channel, retry logic for failed deliveries, and a management console for mapping alert types to channels and recipients. The router ensures guaranteed delivery and supports throttling to prevent notification storms.

Acceptance Criteria
Configurable Channel Preferences
Given a user sets channel preferences for email and Slack, when an alert triggers, then notifications are sent only via the selected channels and not via others.
Retry Logic for Failed Deliveries
Given a notification attempt fails, when the retry policy is configured to 3 attempts with exponential backoff, then the system retries delivery up to 3 times with increasing intervals and logs each attempt.
Throttling to Prevent Notification Storms
Given a spike in alerts for a single account, when alerts exceed 10 per minute, then the system throttles subsequent notifications to a maximum of 5 per minute and queues the rest.
Template Rendering per Channel
Given a notification template is defined for Microsoft Teams and SMS, when an alert is dispatched, then the content is formatted correctly per channel with placeholders replaced by actual alert data.
Management Console Mapping Updates
Given an administrator updates the mapping of alert types to recipient groups in the console, when the changes are saved, then new alerts follow the updated mapping and the change is reflected in the audit log.
Dynamic Alert Escalation Workflow
"As a support operations manager, I want alerts escalated if not addressed within a set time so that critical issues are not overlooked."
Description

An automation workflow that escalates unacknowledged alerts according to configurable SLAs and business hours. It includes timers, escalation paths (e.g., from customer success rep to manager to director), and fallback recipients. The workflow integrates with the notification router and provides an escalation dashboard with status, history, and acknowledgement tracking.

Acceptance Criteria
Timely Alert Escalation Within SLA
Given an alert is generated and remains unacknowledged for the configured SLA duration within defined business hours, When the SLA timer expires, Then the workflow escalates the alert to the next recipient in the escalation path and logs an escalation event.
Escalation Respects Business Hours
Given the current time falls outside configured business hours, When an alert is pending escalation, Then the SLA timer pauses immediately and resumes at the start of the next business day without triggering any escalation notifications during off-hours.
Manager Acknowledgment Prevents Further Escalation
Given an alert has escalated to the manager level, When the manager acknowledges the alert via the dashboard or notification link, Then all subsequent escalation timers are canceled and the acknowledgement is recorded in the alert’s history.
Fallback Recipient Notification
Given all primary escalation recipients in the path are unavailable or unresponsive within their SLA windows, When the final escalation step is reached, Then the alert is automatically routed to the predefined fallback recipients and the system records the fallback notification event.
Dashboard Displays Escalation History and Status
Given an alert has undergone one or more escalations, When a user views the escalation dashboard, Then the dashboard must display the current acknowledgement status, detailed history of each escalation with timestamps, SLA timer progress, and recipient names.
Real-time Analytics Dashboard Integration
"As a product manager, I want to view real-time alert performance on the dashboard so that I can track response effectiveness and adjust thresholds."
Description

A dashboard widget within PulsePanel that visualizes active alerts, historical threshold changes, average response times, and churn score trends. It provides filtering by time range, segment, and channel, along with drill-down capabilities into individual alert events. The integration uses real-time WebSocket streams to update charts and tables instantly as new data arrives.

Acceptance Criteria
Active Alerts Live Update
Given the dashboard widget is open and connected via WebSocket When a new alert is generated for any monitored account Then the 'Active Alerts' chart updates to display the new alert within 2 seconds without requiring a page refresh
Historical Threshold Adjustment Tracking
Given the user selects a custom time range that includes past threshold modifications When the dashboard loads historical data Then the 'Threshold Changes' timeline visual accurately reflects all threshold adjustments within the selected range, displaying date and new threshold value for each event
Churn Score Trend Visualization
Given the real-time data stream is active When churn score updates are pushed via WebSocket Then the 'Churn Score Trend' line chart refreshes dynamically, plotting new data points in chronological order without losing existing trend data
Filter by Segment and Channel
Given the user chooses specific segment and channel filters When the filters are applied Then all dashboard widgets—charts and tables—update simultaneously to show data exclusively for the selected segment and channel
Drill-Down into Individual Alert Events
Given an alert event is displayed in any dashboard component When the user clicks on an alert entry Then a detailed view opens showing the alert’s timestamp, severity, affected account ID, and any related metadata

FusionView

A unified interface that aggregates support tickets, chat logs, and survey responses into customizable, drag-and-drop widgets. Users can view and filter all feedback streams in one pane, ensuring they never miss critical customer insights.

Requirements

Drag-and-Drop Widget Customization
"As a customer success manager, I want to customize my dashboard layout by dragging and dropping widgets so that I can prioritize the most relevant customer feedback streams for my workflow."
Description

Allow users to add, remove, resize, and reposition widgets within the FusionView dashboard using a drag-and-drop interface. Widgets should snap to a customizable grid layout, support real-time reordering, and persist user-specific configurations across sessions.

Acceptance Criteria
Adding a Widget to the Dashboard
Given a user is on the FusionView dashboard, When they drag a widget from the widget panel onto the grid and release it, Then the widget snaps to the nearest grid cell and appears in the dashboard layout.
Removing a Widget from the Dashboard
Given a user hovers over an existing widget, When they click the remove icon, Then the widget is removed from the dashboard and the layout updates immediately.
Resizing a Widget within the Grid
Given a user selects a widget, When they drag a resize handle, Then the widget adjusts size in grid units without overlapping neighbors and snaps to grid boundaries.
Repositioning a Widget on the Grid
Given a user selects a widget, When they drag it to a new grid position, Then the widget moves to the target cell and nearby widgets shift to avoid overlap.
Persisting User Layout Across Sessions
Given a user configures their dashboard and logs out, When they log back in, Then the dashboard loads with the same widget positions, sizes, and configurations as the previous session.
Multi-Source Data Aggregation
"As a customer success manager, I want to view support tickets, chat transcripts, and survey results in one interface so that I have a complete, real-time view of customer sentiment without switching between tools."
Description

Integrate support tickets, chat logs, survey responses, and other feedback channels into a unified data model. Normalize and tag incoming data automatically so that all feedback streams can be displayed cohesively within FusionView.

Acceptance Criteria
Support Ticket Stream Inclusion
Given the FusionView interface is open, when support tickets are received from the ticketing system API, then each ticket must appear in the Support Tickets widget within 30 seconds of receipt.
Chat Log Normalization
Given chat logs are imported from connected messaging platforms, when a new chat message is received, then the system normalizes metadata (timestamp, user ID, channel) and displays it in FusionView under Chat Logs in ISO 8601 format.
Survey Response Integration
Given survey responses are available from the survey tool, when the response batch is synced, then each response is tagged with survey ID, customer segment, and sentiment score and appears in the Survey Responses widget sorted by timestamp descending.
Real-Time Tagging and Normalization
Given any feedback item (ticket, chat, or survey) is ingested, when its content contains predefined keywords or patterns, then the system automatically applies the correct category tags within 5 seconds and updates the item record.
Unified Data Model Display
Given multiple feedback sources are active, when the user filters for a specific customer or issue type, then FusionView displays combined data from all sources in a single, sortable table without duplicates.
Real-Time Filtering and Search
"As a customer success manager, I want to filter and search across all feedback streams in real time so that I can quickly locate critical issues and emerging trends."
Description

Provide dynamic filtering and full-text search capabilities across all aggregated feedback streams. Filters should include date range, channel type, issue tags, sentiment scores, and custom metadata, with results updating instantly as criteria change.

Acceptance Criteria
Applying Date Range Filters
Given the user is viewing the FusionView panel When the user selects a start and end date in the date range filter Then the feedback list must update within 300ms to display only items with timestamps falling within the selected range
Filtering by Channel Type
Given the user has the filter menu open When the user selects one or more channel types (tickets, chats, survey responses) Then the results pane must show only feedback from the selected channels and display the active filters badge
Searching by Issue Tags
Given the user enters one or more issue tags in the tag filter field When the tags are confirmed Then the feedback stream must display only entries tagged with the specified issues and the total count must match the backend query
Filtering by Sentiment Scores
Given the sentiment score slider is visible When the user adjusts the minimum and maximum sentiment values Then only feedback with sentiment scores within the selected range must be shown and the UI must refresh in under 200ms
Refining Search with Custom Metadata
Given the user has defined a custom metadata key-value pair When the user applies the custom metadata filter Then the results must include only feedback items with matching metadata and the filter must persist for the session
Persistent Dashboard Layouts
"As a customer success manager, I want to save my custom FusionView layouts so that I can rapidly switch between different views for various clients or campaigns."
Description

Enable users to save, name, and switch between multiple custom dashboard layouts. Layout configurations should be stored per user or team, allowing quick retrieval of preferred FusionView setups for different contexts or clients.

Acceptance Criteria
Saving a New Dashboard Layout
Given a user has customized the FusionView dashboard widgets When they click 'Save Layout', enter a unique layout name, and confirm Then the layout is saved to their personal layout list and appears in the layout selection menu.
Overwriting an Existing Dashboard Layout
Given a user selects an existing layout and modifies widget positions When they click 'Save Layout' and enter the same layout name Then the system prompts for confirmation of overwrite and updates the existing layout with the new configuration.
Switching Between Saved Layouts
Given a user has multiple saved layouts When they open the layout selection dropdown and choose a named layout Then the dashboard updates immediately to match the chosen layout configuration.
Team-Level Layout Sharing
Given a user belongs to a team with shared layouts When they save a layout to the team scope and other team members access their layout list Then the shared layout appears and can be applied by any team member.
Deleting a Saved Dashboard Layout
Given a user views their list of saved layouts When they select a layout and choose 'Delete' and confirm deletion Then the layout is removed from the list and cannot be selected or applied.
User Access Controls
"As an admin, I want to control which users can view or modify specific FusionView dashboards so that sensitive client information is accessed only by authorized personnel."
Description

Implement role-based access control (RBAC) for FusionView dashboards. Administrators should assign permissions for viewing, editing, and sharing layouts, ensuring data visibility aligns with organizational policies.

Acceptance Criteria
Assigning Dashboard Permissions by Administrator
Given an Administrator is on the FusionView dashboard settings page When they assign 'view', 'edit', or 'share' permissions to a user role Then users with that role can perform the permitted actions on the dashboard
Unauthorized Access Attempt to View Dashboard
Given a user without 'view' permission When they attempt to access the FusionView dashboard Then access is denied and an 'Unauthorized' message is displayed
Editing Dashboard Layout by Authorized User
Given a user with 'edit' permission When they modify and save the dashboard layout Then the updated layout is persisted and visible to other authorized users
Sharing Dashboard Layout with Specific Roles
Given a user with 'share' permission When they share a dashboard layout with a selected role Then users with that role receive access and can view the shared layout
Role Permission Updates Reflected Immediately
Given an Administrator updates role permissions When the changes are saved Then all affected users’ access rights update in real time without requiring re-login
Custom Widget Library
"As a customer success manager, I want to add and configure different widget types to visualize feedback data so that I can tailor dashboards to highlight the metrics most important to my team."
Description

Offer a library of pre-built widgets (charts, KPI indicators, sentiment heatmaps) and allow users to create or import custom widgets. Widgets should support configurable data bindings and visual settings.

Acceptance Criteria
Using Pre-built Widgets in Dashboard
Given the user is on the FusionView widget library page When the user selects a pre-built widget (e.g., KPI indicator) Then the widget is added to the dashboard with default data bindings and settings.
Creating a Custom Widget
Given the user is on the custom widget creation page When the user defines a widget name, selects a chart type, and maps required data fields Then the widget preview updates correctly and the widget is saved to the library upon confirmation.
Importing a Custom Widget from External Source
Given the user has a valid widget definition file When the user uploads the file through the import functionality Then the widget appears in the library with all configurations and visual settings intact.
Configuring Data Bindings for a Widget
Given a widget is present on the dashboard When the user edits the data binding settings and selects a new data source or field Then the widget displays data from the new source in real-time without errors.
Customizing Widget Visual Settings
Given the user is editing a widget’s settings When the user modifies visual properties such as color scheme, font size, and layout Then the widget UI updates immediately to reflect the new visual settings.

Sentiment Synth

Applies AI-driven sentiment analysis across all feedback channels, normalizing scores into a single, intuitive metric. This unified sentiment pulse helps teams quickly gauge customer mood and prioritize responses effectively.

Requirements

Unified Sentiment Normalization
"As a Customer Success Manager, I want a single, normalized sentiment score across all feedback channels so that I can quickly assess overall customer mood and make informed decisions without reconciling different scoring systems."
Description

Aggregate sentiment scores from disparate customer feedback channels—such as email, chat transcripts, social media mentions, and survey responses—and convert them into a consistent, normalized metric on a 0–100 scale. This ensures that all feedback types are comparable, simplifies cross-channel analysis, and provides a clear, single sentiment pulse that reflects overall customer mood. Integrated into the processing pipeline, this normalization enables accurate trend detection and prioritization across the entire feedback ecosystem.

Acceptance Criteria
Batch Aggregation of Multi-Channel Sentiment
Given daily email, chat, social media, and survey sentiment inputs in their native scales, when the batch normalization job executes, then each channel’s sentiment score is converted to a 0–100 scale and stored in the central sentiment table.
Real-Time Chat Feedback Normalization
Given a new live chat message with an AI sentiment score, when the message is processed, then the system normalizes the score to a 0–100 scale and displays it in the real-time sentiment API within 5 seconds.
Social Media Sentiment Score Conversion
Given incoming social media sentiment scores ranging from -1 to +1, when normalization is applied, then the resulting scores must fall within 0–100 and match the expected linear transformation within a ±1% tolerance.
Survey Response Score Normalization
Given survey responses scored on a 1–5 Likert scale, when aggregated and normalized, then the final metric accurately reflects the average sentiment on a 0–100 scale with a deviation no greater than 0.5 points from manual calculation.
Dashboard Rendering of Unified Sentiment Pulse
Given normalized channel scores and predefined weights, when the dashboard is loaded, then the overall sentiment pulse computes as the weighted average of channel scores and displays a single 0–100 metric consistent with backend data.
Automated Trend Detection and Alerting
Given historical normalized sentiment data, when sentiment drops by more than 10 points within a rolling 7-day window, then the system generates an alert to the customer success team within one hour of detection.
Multi-Channel Feedback Ingestion
"As a Customer Success Manager, I want all feedback automatically collected from every channel so that I can view a unified stream of customer comments without manually exporting and merging data."
Description

Implement connectors and data ingestion pipelines to securely pull customer feedback from multiple sources—including CRM systems, support ticket platforms, live chat, social media, and survey tools—on a continuous basis. The pipeline should standardize incoming data formats, handle rate limits, and ensure data completeness. By centralizing feedback ingestion, teams gain a holistic view of customer sentiment and eliminate data silos.

Acceptance Criteria
CRM Feedback Ingestion
Given the CRM connector is configured, when new customer feedback is created in the CRM system, then the pipeline ingests the feedback within 5 minutes, without duplicates, and stores it in the centralized repository successfully.
Support Ticket Platform Ingestion
Given the support ticket API credentials are valid, when a new support ticket is submitted or updated, then the system ingests the ticket feedback within 10 minutes, ensuring all relevant fields are captured and mapped correctly.
Social Media Feedback Handling
Given social media APIs are connected, when customer mentions or messages occur on configured platforms, then the pipeline retrieves and ingests all posts and messages in real time, tagging them with source metadata.
Data Standardization and Format Validation
Given feedback from multiple sources is ingested, when data enters the pipeline, then all feedback is normalized to the standard schema (e.g., date formats, field names) and any format errors are flagged for review.
Rate Limit and Retry Mechanism
Given source APIs enforce rate limits, when the ingestion rate exceeds the limit, then the system implements exponential backoff retries and logs incidents, ensuring no data loss.
Sentiment Threshold Alerting
"As a Customer Success Manager, I want to receive immediate alerts when sentiment scores drop below a set threshold so that I can promptly intervene with at-risk customers and prevent churn."
Description

Define configurable sentiment thresholds that trigger automated alerts when customer mood dips below acceptable levels. Alerts should be deliverable via email, in-app notifications, or Slack integrations, and include context such as customer ID, channel, and recent feedback excerpts. This proactive alerting helps teams respond quickly to at-risk accounts and mitigate potential churn.

Acceptance Criteria
Critical Alert for High-Risk Customers
Given a VIP customer sentiment score falls below the critical threshold When the sentiment engine processes the feedback Then an alert is generated within 60 seconds and delivered via email, in-app notification, and Slack with customer ID, channel, and most recent feedback excerpt
Custom Threshold Configuration
Given an admin user sets a new sentiment threshold value When the user saves the configuration Then the system persists the new threshold and applies it to all subsequent sentiment analyses and alert evaluations
Multi-Channel Alert Delivery
Given that email, in-app notifications, and Slack integrations are enabled When a sentiment alert is triggered Then the alert is dispatched to all three channels and a delivery confirmation is recorded for each
Alert Context Enrichment
Given an alert is being generated for a low sentiment score When the alert notification is assembled Then it includes customer ID, feedback channel, timestamp, normalized sentiment score, and up to three recent feedback excerpts
Slack Integration Failover
Given Slack API fails to accept an alert message When the system encounters the error Then it retries twice, falls back to email and in-app notification, and logs the integration failure details in the audit log
Sentiment Dashboard Visualization
"As a Customer Success Manager, I want a visual dashboard showing sentiment trends and breakdowns so that I can monitor customer health at a glance and identify patterns or sudden changes."
Description

Develop an interactive dashboard that displays the unified sentiment pulse over time, broken down by channel, product line, and customer segments. Include visual elements such as trend lines, heat maps, and gauge charts to highlight peaks, troughs, and emerging issues. Users should be able to filter by date range, account, and sentiment category for targeted analysis.

Acceptance Criteria
View Sentiment Trend Over Time
Given the dashboard is loaded, When the user selects a product line, Then the sentiment trend line chart displays sentiment scores over the selected date range, with data points at daily intervals.
Filter Dashboard by Channel
Given multiple feedback channels exist, When the user applies a channel filter (email, chat, survey), Then the dashboard updates all visual elements to reflect only data from the selected channel.
Drill Down by Customer Segment
Given the customer segments are defined, When the user selects a segment from the segment filter, Then the dashboard displays sentiment trends and heat maps solely for that customer segment.
Highlight Peak Sentiment Events
Given sentiment scores have peaks and troughs, When the sentiment pulse exceeds predefined thresholds, Then the dashboard visually highlights these events on the trend line and heat map with annotations.
Adjust Date Range Filters
Given the dashboard supports date range selection, When the user sets custom start and end dates, Then all charts and gauges update to reflect sentiment data within that specified date range.
Customizable Sentiment Models
"As a Customer Success Manager, I want to customize the sentiment model with our own keywords and rules so that the analysis accurately reflects the language and priorities specific to our business."
Description

Allow administrators to fine-tune sentiment analysis by adjusting model parameters, adding custom keywords, and defining contextual rules. Users should be able to upload domain-specific lexicons and set weightings for phrases that are particularly relevant to their industry. This customization ensures that the sentiment analysis aligns closely with organizational needs and captures nuanced customer language.

Acceptance Criteria
Uploading domain-specific lexicon
Given an administrator is on the sentiment customization page When they upload a valid domain-specific lexicon file Then the system imports all entries without errors and applies them in sentiment analysis within 30 seconds
Adjusting phrase weightings
Given an administrator selects a custom phrase When they assign a new weighting value and save Then the system persists the weighting and reflects the change in sentiment scores for newly analyzed feedback
Defining contextual rules for negation
Given an administrator creates a contextual rule to handle negation When the rule is activated and test feedback containing the negated phrase is analyzed Then the sentiment model correctly interprets the negation and adjusts the sentiment score accordingly
Previewing impact of customization
Given an administrator has configured custom keywords and weightings When they click the ‘Preview Impact’ button Then the system displays a side-by-side comparison of standard vs. customized sentiment scores using a sample data set
Reverting to default model
Given an administrator has applied custom sentiment settings When they choose to reset to default model Then all custom parameters are cleared and the system confirms restoration of default settings

ThemeLens

AI-powered thematic analysis that automatically clusters feedback into relevant topics and emerging trends across channels. By surfacing the most discussed themes, teams can address root causes and identify improvement opportunities faster.

Requirements

Real-time Feedback Aggregation
"As a customer success manager, I want feedback from all channels aggregated in real time so that I can address emerging customer issues without delay."
Description

The system shall ingest and unify incoming customer feedback from multiple channels—including support tickets, surveys, chat transcripts, and emails—in real time. By normalizing data formats, deduplicating entries, and preserving metadata, it provides a single source of truth for thematic analysis. This capability ensures CSMs have the most current insights, eliminates manual consolidation overhead, and accelerates response to emerging issues.

Acceptance Criteria
Ingesting Feedback from Multiple Channels
Given new feedback arrives via support ticket, survey, chat, or email, when the system ingests the feedback, then it normalizes the data format to the standard schema within 5 seconds.
Data Format Normalization
Given incoming feedback with varying field structures, when processed, then all entries conform to the defined JSON schema and include required metadata fields.
Duplicate Entry Detection
Given two feedback submissions with identical content and metadata within a 1-minute window, when deduplication runs, then only one unique record is retained in the system.
Real-Time Data Availability
Given feedback ingestion is completed, when processing is done, then the unified feedback is available for thematic analysis in the UI within 10 seconds.
Metadata Preservation
Given feedback from any channel contains metadata (timestamp, user ID, channel), when ingested, then the system stores and displays all metadata fields intact alongside the normalized content.
AI-driven Theme Clustering
"As a customer success manager, I want the system to automatically cluster similar feedback into themes so that I can quickly identify recurring issues and improvement opportunities."
Description

Leveraging natural language processing and machine learning algorithms, the platform automatically analyzes aggregated feedback to identify and cluster similar comments into coherent themes. The AI-driven clustering adapts to evolving language patterns, surfaces root causes, and highlights high-impact topics, empowering teams to prioritize efforts based on data-driven insights.

Acceptance Criteria
Real-time Feedback Aggregation Clustering
Given a batch of 1,000 new feedback items When the system receives the batch Then it clusters them into themes within 2 minutes And each theme contains at least 3 items with a cohesion score ≥ 0.7 And emerging trends (themes with ≥ 5% of total feedback) are highlighted
Adaptive Language Pattern Recognition
Given feedback containing slang, abbreviations, and new product terms When clustering is performed Then the AI updates its language model to include unknown terms And maintains clustering accuracy ≥ 85% on a held-out validation set
Historical Data Re-Clustering
Given historical feedback from the last quarter When a re-clustering job is executed Then themes correspond to ≥ 90% of their previous labels And any new theme covering ≥ 10% of data is added to the theme list
Prioritization of High-Impact Topics
Given clustered themes with calculated impact scores When the system ranks themes Then it orders them by descending impact score And flags all themes with impact ≥ 70 for customer success review
User Review and Feedback Integration
Given a user renames or merges clusters When the user submits changes Then the AI updates cluster definitions to reflect the change And the next clustering iteration aligns with the updated labels ≥ 95% of the time
Customizable Theme Tagging Rules
"As a product manager, I want to configure custom tagging rules for feedback themes so that alerts focus on the most relevant topics for my domain."
Description

Users can define custom rules and thresholds for tagging feedback themes, specifying keywords, sentiment criteria, or feedback volume triggers. This customization enables tailoring of theme detection to specific product areas or customer segments, ensuring that alerts and reports align with organizational priorities and reduce noise from irrelevant data.

Acceptance Criteria
Custom Keyword Rule Creation
Given a user creates a tagging rule with keyword 'checkout error' and assigns the 'Payment Issues' theme, When incoming feedback contains 'checkout error', Then the feedback is automatically tagged with 'Payment Issues'.
Sentiment-Based Tagging Rule Application
Given a user sets a sentiment threshold of negative sentiment below -0.4 for the 'UI Frustration' theme, When feedback with a sentiment score of -0.6 arrives, Then the feedback is tagged with 'UI Frustration'.
Feedback Volume Trigger Rule Enforcement
Given a user configures a rule to tag any theme when at least 10 feedback items mention 'performance' in 24 hours, When 10 or more feedback items with the keyword 'performance' are received within 24 hours, Then the system tags those items with 'Performance Concern'.
Combined Keyword and Sentiment Rule Evaluation
Given a user defines a rule requiring both the keyword 'navigation' and sentiment below -0.2 for 'Navigation Issues', When a feedback item includes 'navigation' with sentiment -0.3, Then the item is tagged 'Navigation Issues'.
Rule Override and Priority Validation
Given a user sets two conflicting rules where 'login' tags as 'Authentication' priority 1 and as 'Account Access' priority 2, When feedback contains 'login', Then the system applies the 'Authentication' tag due to higher priority.
Trend Visualization Dashboard
"As a customer success manager, I want an interactive dashboard showing trend metrics over time so that I can monitor the impact of our interventions and share insights with stakeholders."
Description

An interactive dashboard visualizes theme trends over time, presenting metrics such as frequency, sentiment distribution, and emerging spikes. With filters for date range, customer segment, and feedback channel, CSMs can explore patterns, track the effectiveness of interventions, and share snapshot views with stakeholders.

Acceptance Criteria
Filter Trends by Date Range
Given the user selects a start and end date, when the filter is applied, then the dashboard displays only themes with feedback within the selected date range and updates all metrics accordingly.
Filter Trends by Customer Segment
Given the user selects one or multiple customer segments, when the filter is applied, then the dashboard displays theme trends exclusively for the selected segments, and metrics adjust to reflect segment-specific data.
Filter Trends by Feedback Channel
Given the user chooses feedback channels (e.g., email, chat, survey), when the filter is applied, then the dashboard displays theme trends solely for the selected channels, updating frequency and sentiment metrics accordingly.
Visualize Theme Frequency Over Time
Given the dashboard is loaded, when no filters are applied, then the line chart displays the total count of feedback items per theme over time with hover tooltips showing exact values.
Display Sentiment Distribution
Given a theme is selected, when the user views sentiment distribution, then the dashboard shows a bar chart with percentages of positive, neutral, and negative feedback for the selected theme over the chosen time period.
Identify Emerging Spikes in Themes
Given the dashboard is active, when a theme’s feedback count increases by more than 20% week-over-week, then the spike is highlighted in red on the trend line and an alert badge is displayed.
Theme Drill-down Reporting
"As a customer success manager, I want detailed drill-down reports for each theme so that I can investigate the root causes and collaborate effectively with my team."
Description

The platform provides drill-down reporting capabilities for each identified theme, allowing users to access the underlying feedback entries, view associated sentiment scores, and export data for further analysis. This detailed reporting supports root-cause investigations, cross-functional collaboration, and evidence-based decision-making.

Acceptance Criteria
Theme Drill-down Data Access
Given the user is on the Themes dashboard and selects a theme, When the theme detail view loads, Then the platform displays at least 10 underlying feedback entries with their sentiment scores and supports pagination to navigate additional entries.
Sentiment Score Display
Given feedback entries are displayed for a selected theme, Then each entry shows a sentiment score between -1 and 1, color-coded (positive green, neutral gray, negative red) and includes a tooltip explaining the score meaning.
Data Export Functionality
Given the user clicks the 'Export' button in the theme drill-down view and selects CSV format, Then a CSV file is downloaded containing columns: Theme Name, Feedback ID, Feedback Text, Sentiment Score, Date Submitted.
Filter by Sentiment
Given the user applies a sentiment filter (positive, neutral, negative) in the drill-down view, When they confirm the filter selection, Then only feedback entries matching the selected sentiment category are displayed.
Cross-functional Sharing
Given the user clicks the 'Share Report' button, When they enter valid email addresses and optional message, Then the system sends an email containing a link to the drill-down report and includes a summary of total feedback count and average sentiment score.

ThreadLink

Links related feedback items from different channels into cohesive conversation threads. This feature preserves context and chronology, enabling users to trace issue evolution and collaborate on resolutions seamlessly.

Requirements

Automatic Thread Aggregation
"As a customer success manager, I want the system to automatically group related feedback from email, chat, and support tickets so that I can view unified conversation threads without manual effort."
Description

The system automatically identifies and groups related feedback items across email, chat, and support ticket channels into unified conversation threads. It preserves context and chronological order, tags each thread, and updates thread membership as new items arrive. This reduces manual effort, prevents duplicate investigations, and ensures consistent resolution workflows.

Acceptance Criteria
Email Matches Existing Thread
Given an existing thread with prior email feedback tagged “Login Issue” When a new email arrives containing the same login error keywords Then the system appends the new email to that thread preserving message order and context
Chat and Ticket Consolidation
Given a live chat conversation exists on error “Payment Declined” When a ticket is created with matching error description Then the system merges both items into a single thread and updates all participants
Chronological Integrity Maintenance
Given multiple feedback items from email, chat, and tickets When they are aggregated into a thread Then the system orders them by received timestamp and displays a complete chronological history
Dynamic Thread Tagging
Given a thread contains feedback mentioning “API Timeout” When new related feedback arrives Then the system assigns or updates the thread tag to “API Timeout” and notifies subscribers
Non-Matching Feedback Isolation
Given a new feedback item arrives with content unrelated to any existing threads When the aggregation process runs Then the system creates a new thread and does not merge it with unrelated threads
Thread Timeline Visualization
"As a product manager, I want to view a timeline of all feedback interactions within a thread so that I can understand issue evolution over time."
Description

Provide a visual timeline UI for each thread that displays feedback items in chronological order, including timestamps, channel origins, and author details. The visualization supports zooming, filtering by channel or date range, and highlights key events or status changes within the thread. This feature helps users track issue evolution and identify milestones at a glance.

Acceptance Criteria
Viewing Thread Timeline
Given a thread with multiple feedback items When the user opens the thread timeline Then all feedback items appear in chronological order with correct timestamps
Zooming Into Timeline
Given the thread timeline is displayed When the user zooms in on a specific time period Then the timeline granularity adjusts and feedback items reposition accurately
Filtering Feedback Items
Given the thread timeline is displayed When the user applies a channel or date range filter Then only feedback items matching the selected channel and date range remain visible
Highlighting Key Events
Given the thread timeline contains milestone events or status changes When such an event occurs Then the event is visually highlighted and annotated on the timeline
Displaying Author and Channel Details
Given feedback items on the timeline When the user hovers over or selects an item Then the author’s name and channel origin are displayed clearly
Manual Thread Merging
"As a support agent, I want to merge separate threads when I identify they refer to the same issue so that I maintain coherent and singular discussion history."
Description

Allow users to manually merge two or more existing threads when they recognize they address the same underlying issue. The merge action combines all associated feedback items, retains the original context and metadata, and appends a merge note documenting the action. This ensures that no feedback is lost and threads remain coherent.

Acceptance Criteria
User Initiates Manual Merge
Given the user selects multiple threads addressing the same issue, When the user clicks the 'Merge Threads' button, Then the system should open the merge interface listing selected threads.
All Feedback Items Combined Correctly
Given two or more threads are merged, When the merge completes, Then all feedback items from each original thread appear under the new combined thread without duplication.
Original Metadata Retained
Given threads with distinct metadata (timestamps, authors, tags) are merged, When viewing the merged thread, Then each feedback item retains its original metadata exactly as before.
Merge Note Appended
Given the user confirms the merge, When the merge action is executed, Then the system appends a merge note at the top of the combined thread documenting which threads were merged, by whom, and when.
Merge Confirmation Displayed
Given the merge process finishes, When the thread is successfully merged, Then a confirmation message is displayed to the user, and the merged thread appears in place of the original threads.
Cross-Channel Thread Search
"As a CSM, I want to search for threads across all channels using keywords and tags so that I can quickly locate relevant customer feedback discussions."
Description

Enable a global search feature that indexes threads and their constituent feedback items across all channels. Support filtering by keywords, tags, date ranges, and channel types. Search results link directly to the corresponding thread view, allowing users to quickly locate and dive into relevant discussions.

Acceptance Criteria
Searching Threads by Keyword and Date Range
Given the user enters a keyword and selects a start and end date range When the user executes the search Then the system returns only threads containing the keyword created within the specified date range And the total number of results matches the count displayed
Filtering Threads by Channel Type
Given the user selects one or more channel types from the filter menu When the user executes the search Then the returned threads are exclusively from the selected channel types
Navigation from Search Results to Thread Detail
Given the search results are displayed When the user clicks on a thread result Then the system navigates to the detailed view of the selected thread And loads all constituent feedback items in chronological order
Combining Keyword, Tag, and Channel Filters
Given the user applies a keyword filter, one or more tag filters, and one or more channel type filters When the user executes the search Then the system returns threads that meet all applied filters simultaneously
Handling Searches with No Matching Threads
Given the user executes a search with filters that match no threads When the search completes Then the system displays a “No threads found” message And suggests broadening or removing filters
Thread Activity Notifications
"As a team lead, I want to receive notifications when new items are added to threads I follow so that I'm alerted to updates in real time."
Description

Implement real-time notification mechanisms for thread activity updates. Users can subscribe to specific threads and receive alerts via email, in-app, or push notifications when new feedback items are added, threads are merged, or statuses change. Notification preferences are configurable by channel, frequency, and event type.

Acceptance Criteria
Subscription to Thread Activity Alerts
Given the user has subscribed to a thread via email notifications; When a new feedback item is added to that thread; Then the system sends an email to the user’s registered address within 2 minutes containing the feedback content and thread link.
In-App Notification Display
Given the user is logged into the application with notifications enabled; When a subscribed thread receives a new feedback item; Then an in-app notification appears in the notification center within 30 seconds, showing the thread title and a summary of the new feedback.
Push Notification Delivery
Given the user has enabled push notifications on their mobile device; When a subscribed thread undergoes a status change or merge event; Then a push notification is delivered within 30 seconds, indicating the event type and thread reference.
Configurable Notification Frequency
Given the user has set notification frequency to a specific interval (e.g., real-time, hourly, daily); When the configured interval elapses with thread activity events; Then the system sends notifications aggregated per the selected frequency, listing all relevant events.
Event Type Filtering
Given the user has selected specific event types to receive notifications for (e.g., new feedback, merge, status change); When an event of an unselected type occurs; Then no notification is sent; And when a selected event type occurs; Then a notification is delivered via the configured channel and frequency.

PulseBrief

Automatically generates concise, shareable summaries of key feedback highlights, sentiment trends, and top themes. PulseBrief reports are customizable and exportable, streamlining stakeholder updates and executive briefings.

Requirements

Automated Summary Generation
"As a customer success manager, I want to automatically generate concise summaries of client feedback so that I can quickly understand key issues and share actionable insights with stakeholders."
Description

Implement a natural language generation engine that processes aggregated customer feedback, auto-identifies key highlights, sentiment trends, and recurring themes, then composes a concise, coherent summary. This functionality should integrate seamlessly with the existing feedback ingestion pipeline to ensure real-time updates, reduce manual analysis overhead, and provide CSMs with instant actionable insights.

Acceptance Criteria
On-Demand Summary Request
Given aggregated customer feedback is available, When a CSM requests a summary via the PulseBrief interface, Then the NLG engine returns a concise summary under 300 words highlighting top 5 feedback points, overall sentiment distribution, and 3 recurring themes within 5 seconds.
Real-Time Feedback Pipeline Integration
Given new feedback entries are ingested, When the ingestion pipeline processes them, Then the NLG engine automatically updates the summary in real time with no more than a 1-minute delay and displays updated highlights.
Sentiment Trend Identification
Given a 30-day feedback history, When generating the summary, Then the engine identifies positive, negative, and neutral sentiment trends and reports percentage changes compared to the previous period.
Recurring Theme Extraction
Given multiple feedback items containing similar keywords, When the summary is generated, Then the engine auto-tags and lists the top 5 recurring themes sorted by frequency of occurrence.
Exportable Summary Generation
Given a generated summary, When a CSM selects the export option, Then the system provides downloadable PDF and CSV files preserving formatting and all key summary details.
Customizable Report Templates
"As a customer success manager, I want customizable report templates so that I can create tailored summaries that match each stakeholder’s preferences and branding requirements."
Description

Enable users to design and customize PulseBrief report templates by selecting which data sections to include (e.g., highlights, sentiment graphs, top themes), choosing layout configurations, applying branding elements (logo, color scheme), and adjusting summary depth. This feature should ensure consistency across reports and allow teams to tailor outputs to different stakeholder needs.

Acceptance Criteria
Creating a New Template and Selecting Data Sections
Given the user opens the Create Template interface, when they choose one or more data sections (e.g., highlights, sentiment graphs, top themes), then the preview panel updates to reflect only the selected sections.
Applying Branding Elements to Template
Given the user accesses branding settings, when they upload a logo and select a color scheme, then the logo appears in the report header and all chart elements adopt the chosen color scheme in the preview.
Configuring Layouts and Previewing Changes
Given the user configures layout options (e.g., single-column, two-column), when they select a layout, then the report preview reorganizes sections according to the chosen layout without distortion or overlap.
Adjusting Summary Depth for Different Stakeholders
Given the user adjusts summary depth settings (e.g., brief, detailed), when they save the setting, then generated reports reflect the chosen level of detail in feedback highlights and section summaries.
Saving and Exporting Customized Report Templates
Given the user finalizes the template, when they click Export and select a format (PDF or PPT), then the system generates and downloads a report using the customized template with all selected sections, branding, layout, and depth settings intact.
Multi-Format Export Support
"As a customer success manager, I want to export summaries in different formats so that I can share reports with stakeholders in their preferred medium."
Description

Provide functionality to export PulseBrief reports in multiple formats—PDF for polished distribution, PPTX for presentation-ready slides, and CSV for raw data analysis. Ensure exports preserve formatting, charts, and branding, and support both single-report and bulk-export workflows to streamline stakeholder communications.

Acceptance Criteria
Exporting a single PulseBrief report to PDF
Given a completed PulseBrief report When the user selects “Export as PDF” Then the downloaded file preserves all branding elements, formatting, images, charts, and page layouts exactly as shown in the UI and the filename follows the pattern “PulseBrief_{ReportTitle}_{YYYYMMDD}.pdf”
Exporting a single PulseBrief report to PPTX
Given a completed PulseBrief report When the user selects “Export as PPTX” Then each key theme and chart is placed on its own slide with correct formatting, branding, and slide layout, and the resulting file downloads within 30 seconds
Exporting a single PulseBrief report to CSV
Given a completed PulseBrief report When the user selects “Export as CSV” Then the CSV file contains rows for each feedback item with columns for theme, sentiment score, timestamp, and raw comment, and is encoded in UTF-8 without data truncation
Bulk exporting multiple PulseBrief reports to PDF
Given multiple selected PulseBrief reports When the user chooses “Bulk Export as PDF” Then each report is generated as an individual PDF, packaged in a single ZIP file named “PulseBrief_BatchExport_{YYYYMMDD}.zip”, and the export completes within 60 seconds
Bulk exporting multiple PulseBrief reports to PPTX
Given multiple selected PulseBrief reports When the user chooses “Bulk Export as PPTX” Then each report is converted into a separate PPTX file with correct slide formatting and branding, all files are returned in a single ZIP package, and no file exceeds 5MB
Bulk exporting multiple PulseBrief reports to CSV
Given multiple selected PulseBrief reports When the user chooses “Bulk Export as CSV” Then a single combined CSV is generated containing all feedback items across reports with an additional “ReportID” column, and the file is provided for download within 30 seconds
Scheduled Report Delivery
"As a customer success manager, I want to schedule automatic delivery of summary reports so that stakeholders receive timely updates without manual intervention."
Description

Implement scheduling capabilities that allow users to configure recurring generation and automated distribution of PulseBrief reports via email or in-app notifications. Users should be able to set delivery frequency (daily, weekly, monthly), select recipients or distribution lists, and customize subject lines and messages to automate stakeholder updates.

Acceptance Criteria
Daily Email Report Scheduling
Given a user configures a schedule with frequency set to Daily, time set to 8:00 AM, and at least one valid email recipient, when the scheduled time occurs, then the system automatically generates the PulseBrief report and sends an email including the report attachment to all recipients, and updates the schedule history with a 'Success' status.
Weekly In-App Notification Setup
Given a user selects Weekly frequency and in-app as distribution type, when the scheduled delivery time arrives on the specified weekday, then the system generates the PulseBrief report and creates a notification in the user's in-app notification center with a link to download the report.
Monthly Report Delivery to Distribution List
Given a user selects Monthly frequency, chooses a saved distribution list named 'Executive Team', and sets the delivery time to 9:00 AM on the first day of the month, when that date and time occur, then the system sends the PulseBrief report email to all members of the distribution list and logs the delivery status as 'Sent'.
Subject Line and Message Customization Applied
Given a user customizes the email subject line to 'PulseBrief Update - {{month}}' and adds a custom message body, when the system dispatches the scheduled report, then the email subject and body reflect the user-defined templates with all placeholders correctly replaced.
Manage Recurring Schedule Modification and Cancellation
Given a user views an existing report schedule and chooses to modify frequency or recipients or cancel the schedule, when the user saves changes, then the system updates or deactivates the schedule accordingly, sends a confirmation notification to the user, and records the change in the schedule history log.
Sentiment Trend Visualization
"As a customer success manager, I want sentiment trend visualizations in my summaries so that I can monitor changes in customer sentiment over time and respond proactively."
Description

Integrate interactive sentiment trend charts and key theme visualizations within PulseBrief summaries, highlighting sentiment shifts and theme prevalence over configurable time ranges. This will help stakeholders quickly grasp evolving customer sentiment and identify emerging issues before they escalate.

Acceptance Criteria
Daily Sentiment Overview
Given the user views a PulseBrief summary When they select the "Daily" timeframe Then the sentiment trend chart displays data points for each of the past seven days with positive, neutral, and negative sentiments distinctly color-coded And hovering over any point reveals the specific date and percentage breakdown
Theme Prevalence Breakdown
Given the user examines the theme prevalence section When they enable the top themes toggle Then the chart lists the top five themes sorted by frequency within the selected period And the cumulative percentage accurately reflects each theme’s proportion of total feedback
Custom Time Range Analysis
Given the user inputs a custom start and end date When they apply the date range filter Then the sentiment trend chart refreshes to show only data within those dates And the summary compares sentiment shifts against the preceding period
Real-time Sentiment Alerts
Given the user configures real-time sentiment thresholds When overall sentiment falls below the defined threshold Then the system sends an alert notification to the user And highlights the sentiment drop on the trend chart in red
Exportable Visualization Reports
Given the user opts to export the summary When they choose PDF or PNG format Then the exported file includes the sentiment trend chart, key theme visualization, legends, axis labels, and summary text exactly as displayed on screen

Product Ideas

Innovative concepts that could enhance this product's value proposition.

PulsePush Alerts

Send instant mobile alerts when feedback spikes exceed thresholds, ensuring teams tackle critical issues within minutes.

Idea

Sentiment Spark

Analyze tone variations in real time to highlight surging frustration, guiding proactive outreach before issues escalate.

Idea

RootCause Radar

Visualize recurring issue origins on an interactive map, revealing patterns across customers and modules at a glance.

Idea

Churn Crystal Ball

Predict at-risk accounts by combining feedback trends with usage metrics, empowering prescriptive retention actions.

Idea

Feedback Fusion Hub

Aggregate support tickets, chat logs, and survey responses into one pane, delivering a unified 360° customer voice.

Idea

Press Coverage

Imagined press coverage for this groundbreaking product concept.

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PulsePanel Unveils AI-Driven Emotional Intelligence Suite to Revolutionize Customer Success

Imagined Press Article

City, State – 2025-05-28 – PulsePanel, the leading unified feedback intelligence platform for SaaS organizations, today announced the launch of its groundbreaking AI-Driven Emotional Intelligence Suite. Designed to equip Customer Success, Support, and Product teams with unprecedented visibility into customer sentiment, the new suite combines real-time sentiment analytics, predictive risk scoring, and prescriptive insights to enable proactive engagement and retention strategies. The AI-Driven Emotional Intelligence Suite introduces three flagship capabilities: Frustration Flash, Tone Trendlines, and Emotional Heatmap. Together, these features pinpoint emergent customer issues, visualize sentiment trajectories, and map areas of high emotional intensity across channels, modules, and geographies. Frustration Flash instantly detects sudden spikes in negative sentiment across customer interactions, surfacing critical feedback as it happens. By leveraging natural language processing and machine learning, PulsePanel identifies underlying keywords and context to alert teams within seconds of a potential escalation. "Our customers told us they needed to know the moment something went wrong, and not days later when tickets pile up," said Jamie Roberts, Chief Product Officer at PulsePanel. "With Frustration Flash, we’re delivering real-time intelligence that empowers teams to intervene within minutes, preventing small issues from snowballing into customer churn." Tone Trendlines adds an additional layer of clarity by visualizing how sentiment fluctuates over customizable time windows for individual accounts and customer segments. Users can isolate specific channels—such as support chat, email, or in-app messages—to track emerging patterns of frustration or delight. This granular view enables Customer Success Managers to tailor outreach and messaging based on solid data rather than intuition. Emotional Heatmap translates sentiment scores into an intuitive geographic and module-based heatmap, providing a bird’s-eye view of sentiment hotspots. Customer Support Directors and Product Insight Specialists can quickly pinpoint which product features or regions require immediate attention. "Emotional Heatmap is a game-changer for our global support operations," remarked Anna Lopez, Senior Director of Customer Support at CloudScale Software. "We used to rely on manual ticket audits to identify problem areas. Now, we see at a glance where our customers are struggling, and we can allocate resources strategically to address the root causes." In addition to these new analytics, the suite integrates seamlessly with PulsePanel’s existing FusionView interface, enabling users to layer sentiment data alongside feedback themes, incident clusters, and churn risk scores. The result is a unified dashboard that correlates usage metrics, support tickets, and emotional signals in a single pane. PulsePanel’s AI-Driven Emotional Intelligence Suite is available immediately as a standalone module or as part of the company’s enterprise tier. Early adopters have already reported a 23% reduction in response times to critical issues, a 19% increase in customer satisfaction scores, and improved alignment between Support, Success, and Product teams. "We believe the future of customer success lies in anticipating needs before they surface in tickets or surveys," added Roberts. "By leveraging AI to decode emotions at scale, PulsePanel is setting a new standard for proactive, human-centered customer care." Key benefits of the AI-Driven Emotional Intelligence Suite: • Instant Detection: Real-time alerts on sentiment spikes prevent escalations before they impact retention. • Historical Context: Trendlines reveal sentiment patterns over customizable intervals, illuminating when and why customer moods shift. • Strategic Focus: Heatmaps guide resource allocation to high-impact areas, ensuring support teams target the right issues. • Unified View: Seamless integration with FusionView correlates sentiment data with feedback themes and churn risk metrics. For more information on PulsePanel’s AI-Driven Emotional Intelligence Suite, please visit www.pulsepanel.com/emotional-intelligence, or contact: Press Contact: Taylor Nguyen VP of Marketing, PulsePanel Email: taylor.nguyen@pulsepanel.com Phone: (555) 123-4567 About PulsePanel: PulsePanel empowers SaaS customer success organizations with instant, unified client feedback by centralizing frontline reports and auto-tagging issues as they arise. Teams pinpoint and resolve 37% more recurring problems before escalation, reducing churn risk by 21%. With a suite of advanced analytics and AI-driven features, PulsePanel transforms scattered tickets into immediate, actionable insights that keep customers satisfied and loyal.

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PulsePanel Surpasses 50 Million User Interactions, Drives 30% Reduction in Churn for Clients

Imagined Press Article

City, State – 2025-05-28 – PulsePanel, the premier customer feedback intelligence platform for SaaS businesses, today announced that it has processed over 50 million user interactions through its unified feedback engine. This milestone underscores PulsePanel’s rapid market adoption and its transformative impact on client retention strategies. Since its inception, PulsePanel has centralized support tickets, chat logs, survey responses, and frontline reports into a single, actionable dashboard. By auto-tagging issues and delivering AI-driven insights, PulsePanel enables Customer Success Managers, Support teams, and Product leaders to address recurring problems swiftly, reducing customer churn risk by an average of 21%. “In a landscape where customer expectations are skyrocketing, consolidating feedback streams and surfacing trends in real time isn’t just a nice-to-have—it’s a necessity,” said Morgan Lee, CEO of PulsePanel. “Processing 50 million interactions is a testament to the trust our customers place in our platform and the quantifiable business outcomes we deliver.” PulsePanel’s growing feature set has been a key driver of its adoption. Recent enhancements include: • Prescriptive Playbook: AI-generated action plans tailored for at-risk accounts, leveraging historical resolution success stories. • Fusion Insights Dashboard: A unified view correlating product usage metrics with sentiment and feedback themes. • Cluster Compass and Timeline Trail: Visual clustering and temporal sliders that reveal how incident hotspots evolve over time. PulsePanel’s customers span fast-growing startups to enterprise SaaS providers. Early adopters report significant gains: • A leading marketing automation company achieved a 30% reduction in churn within six months by integrating PulsePanel’s RiskScore Beacon and Prescriptive Playbook into their customer success workflows. • A global fintech firm shortened incident resolution times by 35% using the Cluster Compass and Smart Escalation features to prioritize high-impact issues. • A healthcare technology provider increased NPS by 18% after deploying Tone Trendlines and Emotional Heatmap to inform product roadmap decisions. Case Study Spotlight: BrightWave Analytics, a mid-market analytics provider, leveraged PulsePanel to centralize feedback from support emails, in-app chats, and customer surveys. By configuring FusionView to display real-time sentiment alongside usage spikes, BrightWave’s CS team identified a critical onboarding bug that impacted first-week activation rates. “PulsePanel alerted us to a dip in sentiment users weren’t even vocalizing through formal support channels,” said Spencer Kim, Director of Customer Success at BrightWave. “We resolved the issue within 48 hours and saw a 25% lift in trial-to-paid conversion in the following quarter.” To celebrate the milestone, PulsePanel is launching a webinar series, "Feedback to Foresight," featuring industry experts discussing best practices for using customer intelligence to drive growth. The series kicks off on June 10, 2025, and is open to current and prospective PulsePanel users. “We’re committed to empowering teams with the tools and knowledge they need to turn feedback into foresight,” said Lee. “Our webinar series will share actionable playbooks and real-world success stories to help organizations of every size maximize their customer intelligence investments.” For more details on the 50 million interactions milestone, customer case studies, or to register for the "Feedback to Foresight" webinar series, visit www.pulsepanel.com/milestone. Press Contact: Taylor Nguyen VP of Marketing, PulsePanel Email: taylor.nguyen@pulsepanel.com Phone: (555) 123-4567 About PulsePanel: PulsePanel equips SaaS customer success teams with instant, unified client feedback by centralizing frontline reports and auto-tagging issues as they arise. Customers pinpoint and resolve recurring problems before escalation, reducing churn risk and driving stronger retention outcomes.

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PulsePanel Partners with Zendesk to Deliver Seamless Feedback Integration for Enhanced Customer Insights

Imagined Press Article

City, State – 2025-05-28 – PulsePanel, the unified feedback intelligence leader for SaaS enterprises, today announced a strategic partnership with Zendesk to provide seamless integration between Zendesk’s customer support platform and PulsePanel’s advanced feedback analytics. This collaboration will enable thousands of joint customers to consolidate ticket data, chat transcripts, and survey responses into one centralized interface, delivering deeper insights and faster resolution workflows. By linking Zendesk’s Ticketing API with PulsePanel’s FusionView and Sentiment Synth engine, users can now: • Automatically ingest all support tickets and chat logs from Zendesk in real time. • Apply AI-driven sentiment analysis and thematic clustering to identify emerging issue patterns. • Visualize feedback trends alongside usage data and churn risk metrics in a single dashboard. • Trigger Smart Escalation workflows and Prescriptive Playbook recommendations directly within Zendesk’s agent console. “This integration represents a significant leap forward for support and success teams,” said Priya Patel, Chief Technology Officer at PulsePanel. “By marrying Zendesk’s world-class ticketing capabilities with PulsePanel’s AI-driven insights, customers gain a 360-degree view of the customer experience, enabling proactive interventions and data-driven decision making.” Zendesk’s VP of Partnerships, Alex Chen, added: “We’re excited to partner with PulsePanel to enrich our platform’s analytics ecosystem. Customers want more than just ticketing data—they want actionable intelligence that drives retention and satisfaction. This integration delivers exactly that.” Key Benefits for Joint Customers: • End-to-End Visibility: Eliminate data silos by unifying support interactions and feedback analytics. • Proactive Issue Resolution: Leverage Frustration Flash alerts to notify teams of negative sentiment spikes detected in Zendesk tickets. • Contextual Guidance: Access Intervention Advisor prompts and Response Templates within the Zendesk agent interface for swift, empathetic communications. • Executive Reporting: Automatically generate PulseBrief summaries summarizing top themes, sentiment shifts, and risk scores for C-suite stakeholder briefings. “Since piloting the integration, we’ve slashed our average response time by 28% and improved customer satisfaction by 12 points,” said Michael Turner, Head of Support Operations at Cloudify Solutions. “Having all our Zendesk data enriched with AI-driven insights in PulsePanel has streamlined our workflows and elevated our customer experience.” The integration is now available to all Zendesk and PulsePanel customers. To get started, customers can visit the PulsePanel Marketplace or the Zendesk App Directory and install the PulsePanel Feedback Intelligence app. Detailed setup guides, API documentation, and onboarding videos are available at www.pulsepanel.com/zendesk-integration. In conjunction with the launch, PulsePanel and Zendesk will host a joint webinar on June 5, 2025, titled "Unlocking Unified Customer Insights: Best Practices for Zendesk and PulsePanel Users." Registration is open at www.pulsepanel.com/webinars. For more information about the Zendesk integration or to schedule a demo, please contact: Press Contact: Taylor Nguyen VP of Marketing, PulsePanel Email: taylor.nguyen@pulsepanel.com Phone: (555) 123-4567 Media Relations: Aisha Malik Director of Communications, Zendesk Email: aisha.malik@zendesk.com Phone: (555) 987-6543 About PulsePanel: PulsePanel empowers SaaS companies with instant, unified customer feedback by centralizing frontline reports and auto-tagging issues as they arise. Teams leverage advanced analytics and AI-driven recommendations to resolve recurring problems, reduce churn, and enhance customer loyalty. About Zendesk: Zendesk builds software designed to improve customer relationships. It enables businesses to use data across support, sales, and knowledge bases to drive customer satisfaction and retention. For more information, visit www.zendesk.com.

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