Retail Analytics

Heatway

Turn Footsteps Into Sales Insight

Heatway transforms any smartphone into a real-time foot traffic heatmap generator for independent shop owners, instantly revealing customer movement patterns and overlooked products. Shop owners quickly pinpoint layout blind spots, optimize displays, and boost sales—no expensive hardware, just actionable, in-store insights to unlock hidden revenue in minutes.

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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 independent retailer to unlock hidden sales potential through instant, accessible in-store insights that transform their business.
Long Term Goal: Within five years, empower 100,000 independent retailers worldwide to boost in-store sales by $500 million through real-time, mobile-driven foot traffic insights—eliminating hardware barriers for all.
Impact: Enables independent shop owners to increase product exposure by 25% and boost sales conversion rates by 12% in one month, using instant mobile heatmaps to resolve layout blind spots—eliminating hardware costs and cutting analysis turnaround from weeks to real-time actionable insights.

Problem & Solution

Problem Statement: Independent shop owners struggle to understand customer movement and product engagement in their stores, as existing foot traffic analytics require costly hardware or complex setup, leaving them without actionable insights to improve layouts and boost sales.
Solution Overview: Heatway turns any smartphone into a live foot traffic heatmap generator—simply scan your store to instantly see where shoppers linger or pass by, revealing overlooked products and layout blind spots, so shop owners can optimize displays without expensive hardware or complex setup.

Details & Audience

Description: Heatway turns any smartphone into a powerful, real-time foot traffic heatmap generator for independent shop owners. Retailers instantly see where customers go and where products get overlooked, helping them optimize store layouts to drive more sales. Unlike costly systems, Heatway requires no special hardware—just scan your space and get instant, actionable visuals that reveal hidden opportunities, every day.
Target Audience: Independent shop owners (30-55) in retail, frustrated by low sales, eager for actionable, tech-friendly insights.
Inspiration: One afternoon in my neighborhood bookstore, I watched the owner shuffle displays after guessing which shelves attracted customers. Despite her effort, sections stayed quiet and questions repeated. Seeing her frustration, I grabbed my phone, mapped the store’s footpaths, and showed her the surprising patterns. That moment revealed how instant, visual insights could turn guesswork into smarter sales for every small retailer.

User Personas

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

Nomadic Vendor Nora

- Age 35 - High school diploma - Part-time market vendor for 5 years - Annual revenue ~$40k

Background

As a nomadic vendor for five years, Nora learned layout tricks by trial and error but lacks reliable analytics to justify changes.

Needs & Pain Points

Needs

1. Quick foot traffic metrics across venues 2. Easy stall layout performance comparisons 3. Low-effort analytics without extra hardware

Pain Points

1. Unpredictable visitor flow at crowded markets 2. Manual traffic estimates cause poor sales 3. Time-consuming setup with bulky hardware

Psychographics

- Loves hands-on layout experiments - Thrives on adaptability and spontaneity - Values data-backed decisions - Seeks cost-effective tools

Channels

1. Instagram Stories reviews 2. Facebook Market Groups 3. WhatsApp Vendor Chats 4. Local Market Forums 5. Vendor association newsletters

Café Cartographer Chloe

- Age 29 - Bachelor's in Hospitality - Runs one café for 2 years - Annual profit ~$55k

Background

After culinary school, Chloe opened her café and struggled with empty seating corners, prompting her to seek granular flow insights.

Needs & Pain Points

Needs

1. Clarity on peak seating zones 2. Data to optimize product placements 3. Simple in-app layout recommendations

Pain Points

1. Empty seating pockets reducing turnover 2. Overlooked menu displays missing revenue 3. Manual observation prone to human error

Psychographics

- Values a warm, welcoming atmosphere - Detail-oriented perfectionist - Motivated by customer satisfaction - Embraces tech-savvy enhancements

Channels

1. Instagram business profile 2. Pinterest decor boards 3. LinkedIn hospitality groups 4. Twitter coffee chats 5. Facebook local ads

Virtual Venue Vicky

- Age 32 - Master’s in Marketing - E-commerce store for 5 years - Annual revenue ~$70k

Background

Vicky expanded her online success into pop-up shops but lacked physical store metrics, driving her to seek rapid analytics solutions.

Needs & Pain Points

Needs

1. Rapid setup for short-term pop-ups 2. Clear data to justify retail tests 3. Seamless online/offline analytics transition

Pain Points

1. Uncertain foot traffic in unfamiliar venues 2. High pop-up costs without performance data 3. Inconsistent customer engagement patterns

Psychographics

- Embraces data experimentation - Values customer-centric insights - Agile marketing risk-taker - Seeks seamless online-offline integration

Channels

1. Shopify community forum 2. Instagram Live demos 3. Email newsletters 4. TikTok marketing tips 5. LinkedIn retail groups

Community Curator Carla

- Age 45 - Bachelor's in Education - Bookstore owner for 8 years - Annual budget ~$30k

Background

Former teacher turned bookstore owner, Carla hosts workshops and struggles to measure event engagement without hard data.

Needs & Pain Points

Needs

1. Insights into high-traffic reading zones 2. Optimal event seating arrangements 3. Data to enhance community engagement

Pain Points

1. Poorly attended event corners 2. Stagnant book section visits 3. Lack of measurable engagement metrics

Psychographics

- Values community connection - Mission-driven service - Passionate about learning experiences - Seeks inclusive environments

Channels

1. Facebook community groups 2. Meetup event listings 3. Local newsletter ads 4. Instagram event stories 5. Twitter book clubs

Product Features

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

Blindspot Meter

Provides a real-time visibility score for each shelf segment, quantifying foot traffic deficits so shop owners can quickly prioritize areas needing attention.

Requirements

Real-time Visibility Scoring

"As a shop owner, I want to see a real-time visibility score for each shelf segment so that I can quickly identify and address low-traffic areas."

Description

The system calculates and displays a dynamic visibility score for each shelf segment by analyzing live foot traffic data captured via smartphones. Scores are updated in real time, reflecting current customer movement patterns. This requirement integrates with the core Heatway data pipeline, enabling instantaneous insights into underperforming areas and facilitating immediate decision-making.

Acceptance Criteria

Live Foot Traffic Data Stream Handling

Given the system receives foot traffic data from smartphones every 5 seconds, When new data arrives, Then the visibility score for each shelf segment is recalculated within 2 seconds.

Real-Time Visibility Score Calculation

Given calibrated data inputs, When processing live foot traffic data, Then the system normalizes scores on a 0–100 scale for each shelf segment without error.

Visibility Score UI Update

Given a updated visibility score, When the UI refreshes, Then the new score is displayed on the dashboard within 3 seconds for all active segments.

Score Accuracy Validation Under Peak Load

Given a simulated peak load of 1,000 data points per second, When the system processes the data, Then visibility scores remain within ±5% of the baseline reference values.

Low Visibility Alert

Given a segment’s visibility score falls below the predefined threshold, When the score update occurs, Then a visual alert is triggered on the dashboard within 1 second.

Historical Score Retrieval

Given a user requests past visibility scores, When the request is made for data up to 10 minutes old, Then the system returns the historical scores without delay.

Shelf Segment Calibration

"As a shop owner, I want to calibrate my store’s shelf segments in the app so that the visibility scores accurately reflect my actual layout."

Description

Provides an in-app interface for shop owners to define, adjust, and label shelf segment boundaries on their store layout. Calibration ensures that the visibility scores correspond accurately to physical shelf locations and dimensions. This functionality ties into the mapping engine, allowing precise alignment between sensors and displayed segments.

Acceptance Criteria

Define New Shelf Segment Boundaries

Given a blank store layout canvas When the shop owner draws a rectangular area on a shelf image Then the system creates a new shelf segment with accurate coordinates, displays the boundary overlay, and prompts for a label

Adjust Existing Shelf Segment Dimensions

Given a pre-defined shelf segment selected When the shop owner drags and resizes the segment handles Then the segment boundary updates in real-time, and the new dimensions are saved upon confirmation

Label Shelf Segments

Given a new or existing shelf segment When the shop owner inputs a text label and clicks save Then the label is displayed on the segment overlay and stored with the segment metadata

Persist Calibration Settings Across Sessions

Given calibrated shelf segments and labels saved in the system When the shop owner logs out and logs back in Then all previously defined segments and labels load correctly on the layout

Align Calibrated Segments with Mapping Engine Coordinates

Given saved shelf segment calibrations When foot traffic data is processed by the mapping engine Then the generated heatmap overlays align with each shelf segment boundary within a two-pixel margin of error

Visual Heatmap Overlay

"As a shop owner, I want a clear heatmap overlay on my store map so that I can visually pinpoint blind spots at a glance."

Description

Implements a visual overlay that combines heatmap gradients and numerical visibility scores on the user’s store floorplan. The overlay updates in real time, using color coding and tooltips to highlight segments with traffic deficits. This UI component integrates seamlessly with the existing dashboard and mobile views.

Acceptance Criteria

Desktop Dashboard Overlay Rendering

Given the user opens the store dashboard, when the visual heatmap overlay is enabled, then the heatmap gradient is correctly overlaid on the floorplan within 300ms and all shelf segments display the appropriate color based on current foot traffic data.

Mobile View Overlay Consistency

Given the user accesses the store view on a mobile device, when the visual heatmap overlay is active, then the overlay is responsive and scales correctly to screen size, maintaining legibility of color gradients and numeric scores.

Real-Time Update Responsiveness

Given new foot traffic data is received every 5 seconds, when the data is updated, then the heatmap overlay automatically refreshes to reflect the latest traffic patterns without requiring a page reload.

Tooltip Visibility Score Display

Given the user hovers over (on desktop) or taps on (on mobile) a shelf segment, then a tooltip appears within 200ms displaying the numeric visibility score and segment identifier.

Color-Coding Accuracy

Given predefined traffic thresholds, then each shelf segment's overlay color must match the correct gradient step for its visibility score according to the legend with no more than 1% deviation.

Threshold-based Notifications

"As a shop owner, I want to receive alerts when a shelf’s visibility drops below a set threshold so that I can promptly address potential sales issues."

Description

Enables configurable alerts when a shelf segment’s visibility score falls below a user-defined threshold. Notifications are delivered via push notifications and in-app banners, prompting shop owners to take corrective action. This feature hooks into the notification service and user settings module.

Acceptance Criteria

Configuring Threshold Alerts

Given the user is on the notification settings screen When they input a valid visibility score threshold and enable threshold-based notifications Then the system saves the threshold and displays a success confirmation message within 2 seconds

Threshold Exceeded Push Notification

Given a shelf segment’s visibility score falls below the user-defined threshold When the system processes real-time foot traffic data Then a push notification is delivered to the user’s device within 30 seconds containing the segment name, current score, and action prompt

In-App Banner Notification

Given the user opens the app after a threshold breach occurs When the home dashboard loads Then an in-app banner appears at the top displaying the shelf segment with low visibility score and a link to view details

Threshold Update Propagation

Given the user modifies the visibility score threshold in settings When the user saves the new threshold Then all subsequent notifications and banners use the updated threshold value without requiring an app restart

Notification Preference Enforcement

Given the user has disabled push notifications but kept in-app banners enabled When a shelf segment breaches the threshold Then no push notification is sent and only the in-app banner is displayed upon app launch

Historical Data Comparison

"As a shop owner, I want to compare today’s visibility scores with past performance so that I can understand long-term trends and measure layout changes."

Description

Allows shop owners to compare current visibility scores against historical data, presenting trends and identifying persistent blind spots over time. This requirement includes charting capabilities, date range selectors, and export options. It integrates with the analytics service to fetch and process past traffic metrics.

Acceptance Criteria

Date Range Selection and Chart Update

1. Given the shop owner opens the historical comparison panel, when selecting a valid start and end date within the past year, then the chart updates within 2 seconds to display both current and historical visibility scores. 2. The date picker prevents selection of start dates after end dates and ranges exceeding one year, displaying an inline validation message if violated.

Historical vs Current Data Trend Visualization

1. Given a valid date range is selected, then the line chart presents two distinct series (current period vs historical period) with color-coded lines, a legend, and tooltips showing date, segment ID, and score. 2. Data points reflect accurate values fetched from the analytics service and match raw data within a 1% tolerance.

Persistent Blind Spot Identification

1. Given historical data spans multiple periods, when a shelf segment’s visibility score remains below the threshold for three consecutive comparable periods, then that segment is highlighted in red on the chart and listed under 'Persistent Blind Spots.' 2. The threshold is configurable and changes take effect immediately without requiring a page reload.

Data Export Functionality

1. Given the chart is rendered for a selected date range, when the shop owner clicks 'Export CSV,' then a CSV file downloads containing columns: Date, Segment ID, Current Score, Historical Score, Score Difference. 2. When clicking 'Export Chart,' then a PNG of the current chart view downloads with legible annotations and legend.

Analytics Service Data Integration

1. Given a request for historical data, then the application sends an API call to the analytics service and receives a response within 3 seconds. 2. If the service returns an error or timeout, a user-friendly error banner appears with retry option, and no empty or partial charts are displayed.

No Historical Data Available

1. Given there is no historical data for the selected range, then the chart area displays a placeholder message 'No historical data available' and disables export buttons. 2. The placeholder message guides the shop owner to adjust the date range or contact support if data is expected.

Beacon Alerts

Sends instant push notifications when any shelf's traffic falls below a set threshold, ensuring timely intervention to re-energize overlooked zones.

Requirements

Threshold Configuration Interface

"As a shop owner, I want to configure traffic thresholds for each shelf so that I receive alerts when traffic drops below optimal levels."

Description

Provide a dedicated interface within the Heatway dashboard where shop owners can set and adjust traffic thresholds for individual shelves. The interface should offer default recommendations based on historical data, real-time validation of input values, and the ability to apply settings globally or to specific zones. Integration with the existing layout visualization ensures owners immediately see which shelves have active thresholds and can easily modify them as store conditions change.

Acceptance Criteria

Display Default Threshold Recommendations

Given the shop owner opens the Threshold Configuration Interface When no custom thresholds exist Then recommended threshold values based on the past three months of foot traffic are displayed for each shelf with the recommendation icon highlighted

Real-Time Threshold Input Validation

Given the shop owner enters a threshold value below the minimum allowed or above the maximum allowed When the value is input Then an inline error message appears next to the field and the Save button is disabled until a valid value is entered

Apply Thresholds Globally or Per Zone

Given the shop owner selects either 'Apply Globally' or specific zones When they click the Apply button Then the chosen threshold values are saved accordingly and a confirmation message is displayed

Immediate Layout Visualization Update

Given the shop owner saves new threshold values When the save action completes Then the store layout visualization updates immediately to highlight any shelves that fall below the new thresholds in red

Persist and Load Custom Thresholds

Given the shop owner revisits the Threshold Configuration Interface After previously saving custom thresholds When the interface loads Then the last saved threshold values and scope selections are automatically populated in the input fields

Real-time Traffic Monitoring Service

"As a system, I want to continuously monitor foot traffic data in real-time so that I can detect when shelf traffic falls below configured thresholds."

Description

Implement a backend microservice that continuously aggregates foot-traffic heatmap data from connected smartphones, evaluates current counts against configured thresholds for each shelf, and flags any threshold breaches within seconds. The service must scale to handle multiple stores simultaneously, maintain latency under 60 seconds, and integrate seamlessly with Heatway’s data pipeline and alerting module.

Acceptance Criteria

Immediate Threshold Breach Alerting

Given a configured shelf threshold of X customers per minute When the aggregated count drops below X within a 60-second window Then the service flags a breach and emits an alert event within 5 seconds

High-Load Data Aggregation

Given 10,000 concurrent smartphone connections streaming traffic data When the service processes incoming data for all shelves Then it aggregates counts without data loss and completes each aggregation cycle within 60 seconds

Concurrent Multi-Store Monitoring

Given 50 stores each with independent threshold settings When traffic data arrives from all stores in parallel Then the service correctly applies each store’s thresholds and maintains separate breach states without interference

Seamless Data Pipeline Integration

Given the service’s output topic in the Heatway data pipeline When a threshold breach event is generated Then the event is published to the designated Kafka topic and consumed successfully by the alerting module

Latency Verification Under Stress

Given a simulated traffic drop scenario during peak hours and injected network latency up to 500ms When the service processes data under these conditions Then end-to-end breach detection latency remains under 60 seconds

Push Notification Dispatch System

"As a shop owner, I want to receive push notifications on my smartphone as soon as shelf traffic is low so that I can take timely action to re-energize that zone."

Description

Develop a reliable notification dispatch system tailored to mobile platforms (iOS and Android) that instantly delivers push alerts to shop owners when any shelf’s traffic count breaches its threshold. The system should support retries, handle offline devices gracefully, and provide delivery receipts. It must integrate with existing Heatway authentication to ensure only authorized devices receive alerts.

Acceptance Criteria

Threshold Breach Notification Trigger

Given a shelf’s traffic count falls below its configured threshold, When the system dispatches a push notification to the shop owner’s authenticated device, Then the notification is delivered within 5 seconds and displays the shelf identifier and timestamp.

Offline Device Retry Mechanism

Given the shop owner’s device is offline at dispatch time, When network connectivity is restored within 24 hours, Then the system retries sending the notification up to three times at increasing intervals (5, 15, 30 minutes) and records each attempt.

Delivery Receipt Confirmation

Given a push notification is sent to an online device, When the device acknowledges receipt, Then the system updates the delivery status to "Delivered" in the audit log with a UTC timestamp.

Unauthorized Device Blocking

Given a device without valid Heatway authentication tokens requests notifications, When the system processes the request, Then it rejects the subscription, returns an unauthorized error code, and logs the attempt.

Platform Compatibility Verification

Given the push notification service targets iOS and Android platforms, When sending test notifications to devices running iOS 14+/Android 10+, Then notifications appear correctly via native push frameworks without errors or formatting issues.

Notification Customization Settings

"As a shop owner, I want to customize how and when I receive alerts so that I only get notifications at appropriate times and via my preferred channel."

Description

Offer a customization panel allowing shop owners to configure alert preferences, including preferred notification channels (push, email, SMS), quiet hours to mute alerts outside business hours, and escalation rules for repeated threshold breaches. Preferences should be stored per user and synchronized across devices, with real-time updates applied to incoming alerts.

Acceptance Criteria

Notification Channel Selection

Given the shop owner opens the Notification Customization Settings panel, when they select one or more notification channels (push, email, SMS) and save, then alerts for threshold breaches are delivered only through the chosen channels.

Quiet Hours Configuration

Given the shop owner defines quiet hours in the settings, when an alert-triggering event occurs during those hours, then no notifications are sent until the quiet period ends.

Escalation Rule Setup

Given the shop owner configures escalation rules for repeated threshold breaches, when the breach count exceeds the defined limit within the timeframe, then an escalated alert is sent via the specified secondary channels.

Preference Persistence Across Devices

Given the shop owner saves customization settings on one device, when they log in on another device, then the same notification preferences are displayed and applied.

Real-time Update Application

Given the shop owner updates their notification preferences during an active session, when a new traffic threshold breach occurs immediately after the update, then the alert respects the newly configured settings in real time.

Alert History and Analytics Logging

"As a shop owner, I want to review a history of the alerts and related traffic data so that I can analyze patterns and measure the effectiveness of my interventions."

Description

Store all triggered beacon alerts in an analytics subsystem, capturing details such as timestamp, shelf ID, threshold value, and follow-up actions taken. Provide a reporting dashboard where owners can filter alert history by date, shelf, and outcome, and export data for external analysis. This feature supports trend analysis, helping owners measure the impact of interventions over time.

Acceptance Criteria

Logging of Triggered Alerts

Given an alert is triggered for a shelf when its foot traffic falls below the set threshold When the system logs the alert Then the analytics subsystem records an entry containing: timestamp in ISO format, shelf ID, threshold value, and any follow-up action taken

Accessing Alert History Dashboard

Given a shop owner navigates to the Alert History Dashboard When the dashboard page loads Then a list of all recorded alerts is displayed in reverse chronological order with columns for timestamp, shelf ID, threshold, outcome, and follow-up action

Filtering Alert History

Given the Alert History Dashboard is displayed When the owner applies filters by date range, shelf ID, and outcome Then the dashboard updates to show only alerts matching the selected criteria

Exporting Alert Data

Given the owner has applied desired filters on the Alert History Dashboard When the owner clicks the 'Export' button Then a CSV file is generated containing all filtered alert records and the browser prompts the owner to download the file

Trend Analysis Reporting

Given the owner selects a time period for trend analysis on the dashboard When the system aggregates alert logs for that period Then the dashboard displays a chart showing the number of alerts per day and the percentage of successful follow-up interventions over time

Layout Suggestions

Delivers AI-driven product placement recommendations based on blindspot data, guiding owners on optimal display adjustments to boost visibility and sales.

Requirements

Real-Time Data Capture

"As a shop owner, I want the system to capture customer movements in real time so that I can see up-to-date traffic patterns and make immediate layout decisions."

Description

Implement continuous data collection from smartphone sensors and app interactions to track customer movement patterns within the store in real time. This requirement ensures accurate and timely acquisition of foot-traffic data with minimal latency, forming the foundation for heatmap generation and subsequent layout suggestions.

Acceptance Criteria

Sensor Activation on App Launch

Given the shop owner launches the Heatway app, When the real-time data capture feature is enabled, Then accelerometer, gyroscope, and location sensor streams must start within 2 seconds at a minimum sampling rate of 10 Hz.

Real-Time Data Transmission

Given continuous sensor data capture is active, When data packets are generated, Then each packet must be transmitted to the backend server with end-to-end latency not exceeding 500 ms at least 95% of the time.

Movement Data Timestamp Integrity

Given sensor data includes client-side timestamps, When data arrives at the server, Then the discrepancy between client and server timestamps must be less than or equal to 50 ms.

Sensor Data Buffering and Retry

Given a network connectivity interruption occurs during data capture, When the connection is lost, Then data must be buffered locally and automatically retried for transmission within 10 seconds of reconnection, ensuring zero data loss.

Multi-User Concurrent Data Capture

Given multiple customers (up to 10 devices) are sending data simultaneously in the same store, When data capture is active, Then the system must maintain end-to-end latency under 1 second per packet and achieve 100% data integrity.

Heatmap Visualization Generation

"As a shop owner, I want to view a visual heatmap of customer paths in my store so that I can quickly identify which areas attract the most foot traffic."

Description

Develop a module that processes raw movement data into a dynamic heatmap overlay. The visualization should highlight high, medium, and low traffic zones, offering intuitive color coding and zoom controls for detailed analysis. Integration with the mobile app UI must support smooth rendering on common smartphone models.

Acceptance Criteria

High Traffic Zones Rendering

Given processed movement data for a store layout, when the heatmap visualization loads, then high traffic zones are highlighted in red, medium traffic zones in yellow, and low traffic zones in blue.

Zoom Controls Functionality

Given the heatmap visualization is displayed, when the user performs pinch-to-zoom gestures, then the map scales smoothly without lag and maintains accurate color-coding at all zoom levels.

Smooth Rendering Performance

Given the application is running on common smartphone models, when the heatmap is generated, then the visualization renders fully within 2 seconds and maintains at least 30 frames per second during panning and zooming.

Interactive Layer Toggle

Given the heatmap overlay is active, when the user toggles the heatmap layer on or off, then the base store layout displays instantly without residual heatmap artifacts or rendering delays.

Color Legend Clarity

Given the heatmap display, when the visualization is visible, then a legend appears showing color swatches labeled for high, medium, and low traffic zones, and remains legible on screens as small as 4.7 inches.

Blindspot Detection Engine

"As a shop owner, I want the system to automatically detect under-visited areas so that I can know which product displays are being overlooked."

Description

Create an analytical engine that identifies areas of low or no customer engagement ('blindspots') by comparing traffic heatmap data against predefined thresholds. The engine should flag these zones automatically and provide metrics on how long and how often they persist.

Acceptance Criteria

Baseline Threshold Blindspot Flagging

Given the engine is configured with a traffic threshold of 10 customers/hour and a duration threshold of 15 minutes, when a zone records traffic below 10 customers in any 15-minute interval, then the engine flags the zone as a blindspot.

Persistent Blindspot Duration Tracking

Given a flagged blindspot zone, when the zone remains below the defined traffic threshold for consecutive time intervals, then the engine accurately records and displays the total duration the zone has persisted as a blindspot.

Blindspot Recurrence Frequency Reporting

Given historical heatmap data over a 7-day period, when the engine analyzes zone traffic patterns, then it reports the number of distinct blindspot occurrences per zone within that period.

Threshold Configuration Validation

Given an administrator updates the traffic or duration thresholds, when the new configuration is saved, then the engine applies the updated thresholds to all subsequent heatmap analyses without requiring a restart.

Real-time Blindspot Alert Generation

Given live heatmap data streaming, when a zone meets the blindspot criteria in real time, then the engine generates an alert notification within 5 seconds and logs the event in the system.

AI-Driven Layout Recommendation

"As a shop owner, I want AI-driven placement suggestions based on real traffic data so that I can rearrange products to maximize visibility and revenue."

Description

Implement an AI algorithm that ingests blindspot and traffic data to generate optimized product placement suggestions. Recommendations should consider product categories, past sales performance, and store layout constraints, delivering prioritised actions to improve visibility and sales.

Acceptance Criteria

Uploading Blindspot and Traffic Data

Given the shop owner uploads blindspot and traffic data, when the data transfer completes, then the system validates and stores the data within 5 seconds with no errors.

Generating Initial Placement Recommendations

Given validated data is available, when the AI algorithm runs, then the system produces at least three layout recommendations, each with specified product positions, within 60 seconds.

Incorporating Product Categories and Sales Performance

Given product category and past sales data, when generating recommendations, then high-margin and top-selling categories account for at least 50% of suggested placements.

Applying Store Layout Constraints

Given store layout constraints (such as aisle width and display zone limitations), when generating placement suggestions, then no recommended placement violates any constraint and all dimensions comply with input parameters.

Delivering Prioritized Action Plan

Given the set of layout recommendations, when presented to the shop owner, then suggestions are ranked by expected sales uplift, include clear action steps, and display visual indicators for priority level.

Suggestion Feedback Loop

"As a shop owner, I want to provide feedback on layout suggestions so that the system can learn my preferences and produce more relevant recommendations."

Description

Build a feedback mechanism allowing shop owners to accept, reject, or customize AI-generated layout suggestions and report outcome metrics. The system will learn from this feedback to refine future recommendations and improve accuracy over time.

Acceptance Criteria

Accepting an AI Layout Suggestion

Given an AI-generated suggestion is presented, when the shop owner clicks 'Accept', then the system records the acceptance, updates the digital layout within 5 seconds, and displays a confirmation message.

Rejecting an AI Layout Suggestion

Given an AI-generated suggestion is presented, when the shop owner clicks 'Reject' and selects a rejection reason, then the system logs the rejection reason, leaves the current layout unchanged, and prompts for alternative actions within 3 seconds.

Customizing an AI Layout Suggestion

Given an accepted AI suggestion is active, when the shop owner drags and drops products to new positions on the digital layout, then the system saves the custom layout changes, attributes them to the original suggestion feedback, and shows a success notification.

Reporting Outcome Metrics

Given a layout change has been implemented, when the shop owner requests performance metrics, then the system displays before-and-after foot traffic heatmaps and sales figures for the selected date range with at least 95% data accuracy.

Learning from Feedback

Given feedback entries exist, when the AI model retrains during scheduled maintenance, then the system incorporates acceptance, rejection, and customization data to adjust recommendation algorithms and logs updated accuracy metrics.

Time-Lapse Viewer

Offers a chronological replay of foot traffic heatmaps, enabling owners to track blindspot emergence and measure the impact of layout changes over time.

Requirements

Timeline Playback Controls

"As a shop owner, I want clear playback controls so that I can review specific time intervals of foot traffic and identify when and how blind spots develop."

Description

Implement intuitive playback controls allowing shop owners to play, pause, rewind, and fast-forward the foot traffic heatmap over selected time periods. This functionality enables users to seamlessly navigate through historical data, observe how customer flow evolves throughout the day, and pinpoint exact moments when blind spots emerge. The controls should include clear UI elements, keyboard shortcuts, and smooth transition animations to maintain context during time jumps.

Acceptance Criteria

Play and Pause Controls

- Given the user clicks the play button, then the timeline heatmap playback starts within 100ms at 1× speed with smooth animation. - Given playback is active, when the user clicks pause, then playback stops within 100ms and the heatmap frame remains visible. - When replaying after pause, playback resumes from the paused timestamp.

Rewind and Fast-Forward Controls

- Given playback is active or paused, when the user clicks rewind, then the playback moves backward by 10-second increments per click with smooth transition within 150ms. - Given playback is active or paused, when the user clicks fast-forward, then playback moves forward by 10-second increments per click with smooth transition within 150ms. - When reaching the start or end of the timeline, further rewind or fast-forward clicks have no effect and the corresponding control is disabled.

Keyboard Shortcuts Functionality

- Given the timeline is focused, when the user presses the spacebar, playback toggles between play and pause within 100ms. - When the user presses the left arrow, the timeline rewinds by 10 seconds with smooth transition. - When the user presses the right arrow, the timeline fast-forwards by 10 seconds with smooth transition. - Shortcut presses during transitions are queued and executed in order.

Time Period Selection Navigation

- Given a time period is selected via the date/time picker, when the user starts playback, playback covers only the selected timeframe from start to end. - When the user adjusts the time period during playback, playback resets to the new start point and continues normally. - Playback controls and timeline slider update to reflect the adjusted timeframe accurately.

Smooth Transition Animations

- Given playback jumps to any timestamp (via rewind, fast-forward, or time selection), the heatmap transitions smoothly with animation duration under 200ms. - Animations maintain context by crossfading adjacent frames without jarring changes. - During continuous playback, frame rate remains consistent at 30fps for smooth playback.

Date Range Selector

"As a shop owner, I want to choose specific date and time ranges so that I can focus on analyzing traffic patterns during high-traffic periods or after layout changes."

Description

Provide a flexible date range selection tool enabling users to define custom start and end dates for the time-lapse replay. The selector should support presets (e.g., last hour, today, this week) as well as manual date and time input. It must validate input ranges, handle time zones correctly, and integrate with the playback controls to ensure the replay reflects the chosen interval accurately.

Acceptance Criteria

Preset Date Range Selection

Given the user opens the Date Range Selector When they select the 'Last 24 Hours' preset Then the Start Date field is set to the exact timestamp 24 hours prior And the End Date field is set to the current timestamp And the Time-Lapse Viewer updates to display data only from that interval

Custom Date and Time Input

Given the user manually enters a Start Date and End Date including time values When they apply the selection Then the fields accept the input without error And the Time-Lapse Viewer displays the heatmap replay covering the exact custom interval

Invalid Date Range Rejection

Given the user enters an End Date earlier than the Start Date When they attempt to apply the range Then the selector rejects the input And an inline validation message explains that the End Date must be after the Start Date

Time Zone Consistency

Given the user’s device is set to a non-UTC time zone When they select a preset or enter custom dates and times Then the system converts and displays dates consistently in the user’s local time zone And the Time-Lapse Viewer replay aligns with local timestamps

Playback Control Synchronization

Given the user has set a valid date range When they start, pause, or scrub the time-lapse playback Then the controls respect the selected Start and End dates And no data outside the chosen interval is shown

Speed and Resolution Adjustment

"As a shop owner, I want to change the playback speed and resolution so that I can quickly scan large timeframes or closely examine specific moments."

Description

Enable users to adjust the playback speed and visual resolution of the heatmap time-lapse. Speed options should range from slow-motion to accelerated (e.g., 0.5x, 1x, 2x, 4x), while resolution settings allow toggling between high fidelity and performance-optimized views. This ensures both detailed analysis and efficient overview capabilities on a variety of devices.

Acceptance Criteria

PlaybackSpeedSelection

Given the Time-Lapse Viewer is open, when the user selects a playback speed option (0.5x, 1x, 2x, or 4x), then the heatmap replay adjusts to the chosen speed within 0.5 seconds and displays the selected speed label prominently.

ResolutionToggle

Given the Time-Lapse Viewer is active, when the user switches between high-fidelity and performance-optimized resolution, then the heatmap re-renders in the chosen resolution within 2 seconds without loss of continuity.

MidPlaybackSpeedChange

Given a time-lapse playback is in progress, when the user changes the speed setting, then the remaining replay adapts to the new speed immediately and the total remaining duration updates correctly.

MidPlaybackResolutionChange

Given a time-lapse playback is in progress, when the user changes the resolution setting, then the heatmap seamlessly transitions to the new resolution within 1 second and playback continues without restarting.

LowPerformanceDevicePlayback

Given the feature is accessed on a low-performance device, when performance-optimized resolution is selected, then playback maintains at least 24 frames per second with no more than 5% frame drops over a 5-minute replay.

Snapshot Export

"As a shop owner, I want to export snapshots of the heatmap so that I can document and share customer flow insights with my team."

Description

Allow users to capture and export snapshots of the heatmap at any point during playback. Export formats should include PNG and PDF, with options to annotate, timestamp, and include metadata such as date range and playback speed. This feature supports sharing insights with team members or integrating visuals into reports and presentations.

Acceptance Criteria

Exporting a heatmap snapshot mid-playback for team review

Given the time-lapse playback is active When the user clicks the export snapshot button Then a PNG or PDF file is generated containing the heatmap image with timestamp, date range, and playback speed metadata

Annotating the exported snapshot before download

Given a snapshot preview is displayed When the user selects annotation tools and adds notes Then the final exported file includes the annotations at the correct positions on the heatmap

Including metadata in exported snapshots for reporting

Given the export options panel is open When the user toggles metadata inclusion Then the exported PNG or PDF file contains the selected metadata fields (date range, playback speed)

Handling export errors due to large date ranges

Given a playback session exceeds the maximum supported date range When the user attempts to export a snapshot Then the system displays an error message explaining the limit and prevents the export

Verifying export format selection persists

Given the user selects PDF as the export format When the user exports multiple snapshots during the session Then each exported file is in PDF format without requiring repeated format selection

Change Impact Overlay

"As a shop owner, I want to see how layout changes affected traffic patterns so that I can evaluate the success of my display adjustments."

Description

Overlay markers and annotations that highlight layout modifications made between two points in time and quantify their impact on foot traffic. The system should detect changes to product displays or shelves, correlate them with shifts in customer density, and present a side-by-side comparison view. This overlay guides users in assessing the effectiveness of reconfigurations and optimizing store layout strategies.

Acceptance Criteria

Detect Layout Changes Between Timeframes

Given two floor plan snapshots at Time A and Time B, when the system compares layout metadata, then all display modifications (moves, additions, removals) are correctly identified and highlighted on the overlay.

Quantify Foot Traffic Shift Post-Modification

Given detected layout changes, when correlating foot traffic data, then the system calculates and displays the percentage increase or decrease in customer density for each modified zone.

Side-by-Side Heatmap Comparison View

Given the Time-Lapse Viewer is open and two time points are selected, when the Change Impact Overlay is activated, then two synchronized heatmap panels are rendered with overlay markers indicating layout changes.

Annotation of Changed Display Areas

Given overlay markers highlight modified displays, when a user hovers or taps a marker, then a tooltip shows previous vs. current item details, timestamp of change, and associated traffic delta.

Performance with Large Data Sets

Given a store with ≥1,000 change events and ≥100,000 foot traffic data points, when generating the side-by-side overlay view, then it loads within 3 seconds and maintains pan/zoom latency under 200ms.

Overlay Customizer

Allows users to personalize glowing overlays—choosing color schemes, intensity levels, and category filters—to highlight blindspots in a way that aligns with brand aesthetics and focus areas.

Requirements

Custom Color Palette Selection

"As a shop owner, I want to select custom color palettes for my heatmap overlays so that they match my store’s branding and are visually intuitive."

Description

Enable users to choose and save custom color palettes for overlays, allowing multiple colors with hex code input, preset brand templates, and the ability to store favorite palettes. Integration ensures these palettes align with the user’s branding and can be easily applied to any heatmap overlay.

Acceptance Criteria

Hex Code Palette Creation

Given the user is on the Custom Color Palette screen, When they input between 2 and 6 valid hexadecimal color codes and click Save, Then the new palette is stored in their palette list, available for immediate application to any heatmap overlay.

Preset Brand Template Application

Given the user opens the Preset Templates section, When they choose a brand template and confirm selection, Then the custom color palette updates with the template’s colors and appears in the user’s palette library.

Favorite Palettes Management

Given the user’s palette library contains multiple palettes, When they mark or unmark a palette as a favorite, Then the palette’s favorite status toggles, and favorite palettes are displayed at the top of the list and persist across sessions.

Invalid Hex Code Handling

Given the user enters an invalid hex code (wrong format or unsupported character) in any palette slot, When they attempt to save, Then the system displays an inline validation error message specifying the invalid entry and prevents saving until corrected.

Overlay Color Application Persistence

Given the user applies a saved custom palette to a heatmap overlay, When they reload or revisit the heatmap view, Then the selected palette remains applied and visible without requiring re-selection.

Intensity Adjustment Slider

"As a shop owner, I want to adjust the intensity of overlays so that I can see data clearly without obstructing the underlying floor plan."

Description

Provide a real-time slider control to adjust the opacity and intensity of glowing overlays, enabling fine-tuning of brightness levels to balance visibility of foot traffic patterns with clarity of the store layout underneath.

Acceptance Criteria

Subtle Intensity Fine-Tuning

Given the user selects a low visibility overlay category and moves the intensity slider between 0% and 20%, When the slider value changes, Then the overlay’s opacity updates in real time at 5% increments without noticeable delay.

High-Contrast Visibility Check

Given a brightly lit store layout underlay and the slider set above 80%, When the slider position is between 80% and 100%, Then the overlay brightness ensures foot traffic patterns are distinguishable while maintaining at least 20% visibility of the underlay.

Real-Time Adjustment Responsiveness

Given the slider is adjusted during an active heatmap session, When the user moves the slider, Then the overlay intensity updates within 100ms and does not require a manual refresh.

Boundary Value Intensity Levels

Given the slider positioned at 0% and at 100%, When applied, Then at 0% the overlay is fully transparent and at 100% the overlay is fully opaque according to the selected filter color.

Session Persistence of Slider Settings

Given the user closes and reopens the app after setting the slider to a specific value, When the app restarts, Then the slider retains the last saved value and reapplies it to the overlay automatically.

Category-Based Filtering

"As a shop owner, I want to filter overlays by product category so that I can focus on specific items’ traffic patterns."

Description

Allow users to filter heatmap data by product category, time range, or custom tags, displaying overlays only for selected segments. This helps in isolating traffic patterns around specific product groups or peak hours.

Acceptance Criteria

Filtering by Product Category

Given a shop owner is viewing the heatmap, When they select one or more product categories from the filter, Then only heatmap overlays for those categories are displayed with no data from unselected categories shown and the overlay updates within 2 seconds.

Time Range Filtering

Given the shop owner opens the time range filter, When they set a custom start and end time, Then the heatmap overlay updates to reflect foot traffic only within that time frame accurate to the minute and a time range label is displayed.

Custom Tag Filtering

Given a custom tag is created for a product group, When that tag is applied in the filter, Then the heatmap shows overlays corresponding only to tagged products and hides data for untagged or other tags.

Combined Filters Application

Given the shop owner applies both category and time range filters, When both filters are active, Then the heatmap overlay displays data matching both criteria and the filtering indicators reflect the applied filters.

Reset Filters to Default

Given the shop owner has applied filters, When they click the 'Reset Filters' button, Then all filters clear and the heatmap returns to showing all categories, all time ranges, and no tags with the default overlay visible.

Preset Theme Library

"As a shop owner with limited time, I want to apply preset overlay themes so that I can quickly visualize patterns without customizing each time."

Description

Offer a library of predefined overlay themes—such as brand-aligned, high-contrast, pastel, and night mode—for one-click application. Users can preview and select themes to quickly adapt the visualization without manual customization.

Acceptance Criteria

User Previews and Applies a Brand-Aligned Theme

Given the user opens the Preset Theme Library, when the user selects a brand-aligned theme, then the overlay preview updates to match the selected color scheme, intensity level, and filter settings within 2 seconds.

Shop Owner Switches to High-Contrast Theme for Accessibility

Given the live heatmap is displayed, when the user clicks the high-contrast preset, then the overlay colors update to the high-contrast palette and meet WCAG 2.1 contrast ratio standards.

Operator Applies Night Mode Theme After Hours

Given the store manager toggles to after-hours view, when the night mode theme is applied, then the overlay background dims and highlights adjust to low-light settings immediately.

Marketer Selects Pastel Theme for Content Export

Given the user is in shareable image export mode, when the pastel theme is selected, then the exported heatmap image reflects the pastel color scheme and maintains accurate data visualization.

User Rapidly Switches Themes During Peak Hour Analysis

Given the heatmap is actively updating, when the user selects a different preset theme, then the new theme applies within 1 second without interrupting live data collection.

Live Preview Mode

"As a shop owner, I want a live preview of overlay customizations so that I can see changes in real time and fine-tune settings."

Description

Implement a live preview pane that reflects overlay customization changes in real time before saving. Users receive instant visual feedback on adjustments to color, intensity, and filters, ensuring precise configuration.

Acceptance Criteria

Real-time Color Adjustment Update

Given the user selects a new overlay color in the Overlay Customizer, when the color picker value changes, then the live preview pane updates to reflect the new color within 1 second.

Immediate Intensity Level Feedback

Given the user adjusts the intensity slider in the Overlay Customizer, when the slider value is changed, then the live preview pane updates the overlay opacity to match the new intensity within 1 second.

Dynamic Category Filter Application

Given the user enables or disables category filters in the Overlay Customizer, when a filter toggle is switched, then the live preview pane instantly shows or hides overlay highlights for the selected categories within 1 second.

Preview Reaction Under High Data Volume

Given there are more than 1,000 foot traffic data points loaded, when the user modifies any overlay customization, then the live preview pane updates the overlay within 2 seconds without freezing or errors.

Preview Reset on Cancel or Default Restore

Given the user clicks the “Cancel” or “Reset to Default” button in the Overlay Customizer, when the action is confirmed, then the live preview pane reverts to the last saved or default overlay configuration instantly.

SurgeSignal

Delivers real-time alerts pinpointing surges in foot traffic with contextual data—time of day, aisle location, and traffic intensity—so staff can immediately respond to customer hotspots and maximize engagement opportunities.

Requirements

Real-time Surge Detection

"As a shop owner, I want to receive immediate alerts when foot traffic surges occur so that I can promptly allocate staff to high-traffic areas and improve customer service."

Description

Implement an algorithm that continuously processes live foot traffic data to identify significant increases in customer movement across defined store zones. The algorithm must detect surges within seconds of occurrence, ensuring minimal latency. It should analyze patterns against historical baselines to distinguish normal fluctuations from true surges, triggering alerts only for meaningful events. The feature will integrate with Heatway’s existing data pipeline and leverage mobile sensors to feed instant insights into the SurgeSignal engine.

Acceptance Criteria

Midday Traffic Surge Detection

Given zone A’s foot traffic exceeds its 7-day average by 50% within any 30-second window between 12:00PM and 2:00PM, when the data is processed, then the system must generate a surge alert for zone A within 2 seconds.

Historical Baseline Comparison

Given live data for any zone, when the current 1-minute traffic count deviates by more than 3 standard deviations from its historical average over the past 14 days, then the system flags it as a true surge and suppresses alerts for deviations below this threshold.

False-Positive Prevention During Low Traffic

Given random foot traffic fluctuations below 20% of historical averages outside peak hours, when the algorithm analyzes these readings, then no surge alerts are generated.

Real-Time Alert Delivery Integration

Given a surge is detected, when the alert payload is created, then it must be delivered to the SurgeSignal API endpoint with zone ID, timestamp, and intensity level within 1 second and return a 2XX HTTP status.

Zone-Specific Surge Isolation

Given simultaneous traffic changes in adjacent zones, when a surge occurs only in zone B, then the system should generate an alert exclusively for zone B and not for any neighboring zones.

Contextual Alert Payloads

"As a shop owner, I want detailed context in each surge alert so that I can immediately understand the location, time, and magnitude of the increase and respond effectively."

Description

Generate alerts enriched with contextual metadata including timestamp, specific aisle or zone location, surge intensity metrics, and time-of-day indicators. Each alert must clearly present the percentage increase over baseline traffic and the exact location within the store layout. This contextual information empowers shop owners to make quick, informed decisions by understanding the when, where, and scale of each surge. The alerts will feed directly into the Heatway mobile dashboard and any configured notification channels.

Acceptance Criteria

Morning Rush Surge Detection

Given real-time foot traffic data at 8:00 AM, When the traffic count in aisle 3 increases by at least 25% over its baseline, Then the system generates an alert payload containing the timestamp, 'Aisle 3' location, surge intensity metric (≥25%), time-of-day indicator 'Morning', and percentage increase value.

Aisle-Specific Traffic Surge

Given continuous monitoring of store aisles, When aisle 7’s traffic intensity metric surpasses baseline by 40% during peak hours, Then the alert payload must include the precise zone identifier 'Aisle 7', the surge intensity of 40%, baseline percentage, timestamp, and peak-hour indicator.

After-Hours Unexpected Spike Alert

Given foot traffic detected after 9:00 PM, When a sudden 15% spike over baseline occurs in any zone, Then the notification must deliver to the mobile dashboard and configured channels with contextual metadata including timestamp, zone name, 15% increase, and 'After-Hours' tag.

Simultaneous Multi-Zone Surge

Given two or more zones record surges over baseline within a 5-minute window, When each zone’s increase is ≥20%, Then the combined alert payload must list all impacted zones, individual surge percentages, timestamps for each, and a summary traffic intensity score.

Real-Time Percentage Comparison

Given baseline traffic metrics are stored for each zone, When an alert is generated, Then the payload must compute and display the exact percentage increase over baseline alongside the raw surge intensity value.

Configurable Alert Thresholds

"As a shop owner, I want to set custom thresholds for surge alerts so that I only receive notifications for changes that matter to my business."

Description

Provide a user interface within the Heatway app that allows shop owners to define custom surge detection thresholds based on absolute counts or percentage increases relative to historical baselines. Threshold options should include multiple sensitivity levels (low, medium, high) and advanced settings for peak hours. Customizable thresholds will reduce false positives and ensure alerts are tailored to each store’s unique traffic patterns.

Acceptance Criteria

Custom Absolute Count Threshold Setup

Given the shop owner is on the Alert Threshold Settings screen, when they select 'Absolute Count', enter a value of 20 customers over 10 minutes, and click 'Save', then the system stores the custom threshold and displays a confirmation message.

Configuring Percentage Increase Threshold

Given the shop owner is on the Alert Threshold Settings screen, when they select 'Percentage Increase', set a threshold of 30% increase over the historical baseline and click 'Save', then the system applies the threshold and shows the updated percentage threshold in the overview.

Selecting Sensitivity Levels

Given the shop owner is on the Alert Settings page, when they choose 'High' sensitivity level, then the system automatically adjusts thresholds to high sensitivity parameters and updates the preview values accordingly.

Scheduling Peak Hour Thresholds

Given the shop owner is on the Peak Hours Configuration screen, when they add a time window (e.g., 6:00 PM–8:00 PM) and assign a custom threshold, then the application applies that threshold only during the specified peak hours and indicates the active schedule in the UI.

Validating Threshold Trigger Accuracy

Given custom thresholds are configured, when foot traffic changes occur, then the system generates alerts only when defined thresholds are exceeded and suppresses alerts for fluctuations below the threshold, ensuring false positives are minimized.

Multi-Channel Notification Delivery

"As a shop owner, I want to receive surge notifications on my preferred channel so that I never miss urgent traffic alerts, even when I’m away from the app."

Description

Enable delivery of surge alerts through multiple notification channels, including in-app push notifications, SMS, and email. The system must support user preferences for channel selection, allow fallback options if the primary channel is unreachable, and ensure reliable delivery within seconds of event detection. This multi-channel approach will enhance accessibility and ensure critical alerts are seen without delay.

Acceptance Criteria

User Preference Configuration

Given a shop owner visits the notification settings page, when they select preferred channels (in-app, SMS, email), then the system saves these preferences and displays a confirmation message.

Primary Channel Delivery Success

Given a surge alert is generated, when the primary channel is reachable, then the alert is delivered via that channel within 5 seconds and logged as sent.

Primary Channel Unreachable Fallback

Given the primary channel fails to deliver within 5 seconds, when a fallback channel is available, then the system automatically retries delivery via the next preferred channel within 10 seconds and logs the fallback event.

Delivery Latency Verification

When a batch of surge alerts is triggered, then at least 95% of notifications across all channels are delivered within 10 seconds of event detection as measured by delivery timestamps.

Notification Content Consistency Across Channels

Given a surge alert template is updated, when notifications are sent via any channel, then the content (including time, location, and intensity) is identical in all channels.

Surge Event History Log

"As a shop owner, I want to review a history of surge events so that I can analyze trends over time and optimize store operations accordingly."

Description

Maintain a persistent log of all detected surge events within the Heatway dashboard, including date, time, location, intensity, and threshold settings that triggered the alert. Provide filtering and export capabilities for offline analysis and reporting. This history log will help shop owners review past events, identify recurring patterns, and refine store layout or staffing strategies based on empirical data.

Acceptance Criteria

Surge Event Logging

Given a surge event is detected When it meets the configured intensity threshold Then the dashboard automatically logs an entry with date, time, aisle location, intensity value, and threshold settings.

Date Range Filtering

Given a user selects a start and end date When the date filter is applied Then only surge events whose timestamps fall within the selected range are displayed in the history log.

Location and Intensity Filtering

Given a user applies filters for aisle location and minimum intensity When the filters are submitted Then the history log shows only surge events matching the selected locations and at or above the specified intensity.

Export Surge Event Log

Given the user clicks the 'Export CSV' button When the export completes Then a CSV file downloads including columns for date, time, location, intensity, and threshold settings, and the file can be opened by standard spreadsheet applications.

History Persistence

Given the user logs out and logs back in When accessing the Surge Event History Log Then all previously recorded surge events remain available and unchanged in the history log.

PredictPulse

Leverages historical heatmap trends and AI forecasting to predict upcoming traffic surges, allowing teams to proactively position staff and promotional displays for optimal customer interaction and sales impact.

Requirements

Historical Data Aggregation

"As a store manager, I want the system to aggregate historical foot traffic heatmap data so that I can analyze customer movement trends over time."

Description

Collect and process past foot traffic heatmap data, normalizing timestamps and mapping customer density patterns to a unified data store. This functionality enables accurate trend analysis by consolidating disparate data points into a consistent historical record, ensuring seamless integration with AI forecasting modules and providing the foundation for reliable predictions.

Acceptance Criteria

Aggregated Timestamp Normalization

Given historical heatmap entries from multiple days with varied timestamp formats, when the data is ingested, then all timestamps are converted to UTC ISO 8601 format and stored without loss of precision.

Unified Density Mapping

Given customer location points with raw x/y coordinates, when processing is complete, then all points are binned into a standardized grid resolution (e.g., 1m x 1m) and aggregated into density values per cell.

Cross-Store Data Consolidation

Given heatmap data from multiple shop locations, when loaded into the unified data store, then records include a validated store identifier, and no duplicate entries exist for the same timestamp and grid cell across stores.

Data Integrity Verification

Given the normalized and consolidated dataset, when running validation checks, then the system detects and flags any missing or outlier data exceeding three standard deviations and generates a summary report.

Integration with AI Forecasting

Given the consolidated historical dataset, when the AI forecasting module requests input, then it receives a complete JSON payload with at least 90% data coverage per day and schema conformity verified.

AI Forecast Model Training

"As a store manager, I want the system to train AI models on past heatmap trends so that I can trust its traffic surge forecasts."

Description

Implement a machine learning pipeline that ingests the aggregated historical data to train predictive models capable of forecasting foot traffic surges. This requirement involves selecting appropriate algorithms, tuning hyperparameters, and validating model accuracy. The result should be high-confidence predictions that integrate with the real-time dashboard to inform staff and promotional planning.

Acceptance Criteria

Training Data Ingestion Validation

Given aggregated historical foot traffic data spanning at least 60 days with no more than 5% missing entries, When the data ingestion pipeline runs, Then it should import 100% of valid records into the model training database within 10 minutes, logging any anomalies.

Model Hyperparameter Tuning Completion

Given a candidate set of algorithms and hyperparameter ranges, When the tuning job executes, Then it should complete grid search with cross-validation and output the top three model configurations with RMSE below the predefined threshold within two hours.

Forecast Accuracy Assessment

Given a holdout dataset of the most recent 14-day traffic, When running the predictive model, Then the forecasts should achieve at least 85% accuracy and a Mean Absolute Percentage Error (MAPE) below 10% compared to actual traffic.

Dashboard Prediction Integration

Given successful model predictions, When the API endpoint is queried by the dashboard, Then it should return prediction data in JSON format in under 200ms and correctly display the upcoming 24-hour surge predictions on the UI.

Automated Retraining Scheduling

Given new weekly traffic data arrival, When the scheduled retraining job triggers, Then the pipeline should retrain the model, replace the production model without downtime, and validate post-training accuracy against predefined benchmarks.

Forecast Visualization Dashboard

"As a store manager, I want to see a visual forecast of upcoming foot traffic surges so that I can plan staffing and displays proactively."

Description

Design and develop an interactive interface that displays predicted foot traffic trends on a timeline overlaying the store layout heatmap. Include visual indicators for expected surge intensity and time windows. The dashboard should refresh dynamically and allow users to navigate between future forecasts and historical data, enhancing decision-making with clear graphical insights.

Acceptance Criteria

Viewing Future Surge Overlays

Given the store layout and forecast data are loaded, when the user selects a future time window on the timeline, then the heatmap updates within 2 seconds to display predicted foot traffic intensity using the correct color-coded overlay as per the surge intensity legend.

Comparing Historical and Forecast Data

Given both historical and forecast datasets are available, when the user toggles between ‘Historical’ and ‘Forecast’ modes, then the dashboard displays the corresponding heatmap layers correctly labeled and the timeline slider updates its range accordingly with no data mismatch.

Dynamic Dashboard Refresh

Given new forecast data generated by the AI model, when the dashboard receives updated data, then it auto-refreshes its heatmap and indicators within 60 seconds without requiring a full page reload, preserving the current user view.

Visual Indicators for Surge Intensity

Given the color legend is visible, when the forecasted surge exceeds 20% above baseline in any heatmap cell, then that cell is rendered in the designated red hue and displays an arrow icon indicating high-intensity surge.

Time Window Navigation

Given the timeline slider control is available, when the user drags or clicks on a specific time window, then both the heatmap and the surge intensity chart update to reflect the selected window, and the chosen window segment is highlighted on the timeline.

Staff Allocation Suggestions

"As a floor supervisor, I want the system to suggest optimal staff allocations based on predicted traffic so that I can ensure efficient customer service."

Description

Create an algorithm that leverages predicted foot traffic data to generate recommended staffing levels per store zone and time period. This feature should provide managers with actionable staffing plans, indicating the number of employees to schedule in each area to maintain optimal customer service and reduce bottlenecks during anticipated high-traffic intervals.

Acceptance Criteria

Peak Hour Staff Recommendation

Given a predicted foot traffic surge exceeding threshold during a specified peak hour, when the manager requests staff allocation recommendations, then the system provides a suggested number of employees for each store zone that ensures average customer wait time remains below defined service level targets.

Zone-based Allocation Generation

Given a multi-zone store layout with individual traffic forecasts for each zone, when the staffing algorithm runs, then the system outputs a per-zone staffing plan that aligns staffing levels proportionally to forecasted traffic volumes and zone-specific service objectives.

Dynamic Adjustment on Forecast Update

Given an updated traffic forecast received within 30 minutes of a scheduled period, when the forecast data is ingested, then the staffing recommendations automatically refresh and notify the manager of any changes in required staffing levels.

Export Staffing Plan

Given a finalized staffing recommendation for a specified date range, when the manager exports the plan, then the system generates a downloadable CSV file containing zone, time period, and recommended staff count columns, matching the on-screen data.

User Overrides Handling

Given manual overrides applied by the manager to the recommended staffing levels, when the user saves the adjustments, then the system preserves the modified values and records an audit log entry describing the override reason and timestamp.

Promotional Display Recommendation

"As a marketing lead, I want the system to recommend promotional display placements based on predicted traffic hotspots so that I can maximize sales opportunities."

Description

Develop a recommendation engine that uses forecasted traffic surges and zone-specific customer interest patterns to suggest targeted product placements and promotional displays. The engine should prioritize high-visibility areas predicted to experience surges, optimizing sales impact by aligning marketing efforts with traffic forecasts.

Acceptance Criteria

Surge-Based Display Suggestions

Given a forecasted foot traffic increase of at least 15% in a specific zone When the recommendation engine runs Then it provides a ranked list of at least three product placement suggestions for that zone, prioritizing items with highest past engagement

Zone-Specific Interest Alignment

Given historical customer interest data for Zone B over the past week When the engine generates recommendations Then it suggests promotional displays featuring the top five interest-aligned products for Zone B

Visibility Prioritization Validation

Given multiple zones forecasted for upcoming surges When the engine orders display recommendations Then it allocates at least 50% of recommended displays to the zone with the highest visibility score

Forecast Accuracy Threshold

Given actual foot traffic data collected after display deployment When compared to predicted traffic Then at least 80% of recommended zones experience traffic within ±10% of forecasted values

Recommendation Delivery Timing

Given a surge forecast at least two hours in advance When recommendation processing completes Then the system sends display recommendations notification to the store manager within five minutes

StaffSync

Automatically suggests staff deployment based on live surge alerts, matching peak zones with available team members to ensure timely customer assistance and improved service coverage during high-demand periods.

Requirements

Real-time Surge Detection

"As a store manager, I want to receive real-time surge detection alerts so that I can redeploy staff promptly to high-traffic areas and improve customer service coverage."

Description

Continuously monitors foot traffic heatmaps to identify zones experiencing sudden increases in customer density. Generates alerts when predefined thresholds are exceeded, enabling timely recognition of emerging high-demand areas. Integrates seamlessly with Heatway’s existing analytics engine to leverage real-time data streams without additional hardware.

Acceptance Criteria

High-Demand Zone Alert Generation

Given a zone’s customer density rises above the predefined threshold, When the system processes incoming heatmap data in real time, Then an alert is generated and displayed to the user within 5 seconds of detection.

Custom Threshold Configuration

Given the shop owner sets a custom surge threshold for a specific zone, When the threshold is saved in the system, Then subsequent alerts only trigger when the new threshold is exceeded.

Latency Within Acceptable Range

Given continuous data streaming from Heatway’s analytics engine, When a zone exceeds the surge threshold, Then the alert is delivered with end-to-end latency below 2 seconds 95% of the time.

Seamless Integration with Analytics Engine

Given the existing analytics engine is running, When real-time surge detection is activated, Then no additional hardware is required and data ingestion continues without interruptions or manual intervention.

False Positive Minimization

Given transient spikes in customer detection, When density increases for less than 10 seconds before returning below threshold, Then no alert is sent to avoid false positives.

Staff Availability Synchronization

"As an operations manager, I want the system to know staff current availability so that suggested deployments are realistic and feasible."

Description

Imports and syncs staff schedules, on-shift statuses, and availability data from HR systems or manual inputs. Maintains an up-to-date roster of active team members and their locations within the store. Ensures deployment suggestions consider only available personnel, preventing scheduling conflicts.

Acceptance Criteria

Real-Time Schedule Import

Given an updated staff schedule in the connected HR system When the StaffSync service polls for new data Then the staff roster in StaffSync is updated to match HR records within 2 minutes

Manual Availability Update

Given a store manager manually marks a staff member as unavailable in the StaffSync interface When the change is saved Then the staff member is excluded from deployment suggestions until manually re-enabled

Shift Conflict Prevention

Given a staff member with overlapping shifts in the HR system and manual entries When the system imports data Then StaffSync flags the conflict and prevents the staff member from appearing in multiple simultaneous assignments

Location-Based Availability Tracking

Given staff check-in at different store zones via the mobile app When the system receives the location update Then the staff member’s availability status is updated in real time and reflected in deployment suggestions

HR System Sync Failure Handling

Given the connection to the HR system is interrupted When a sync attempt fails Then StaffSync logs the error, notifies the admin, and retries the sync every 5 minutes until successful

Automated Deployment Suggestions

"As a store manager, I want the system to suggest which staff to send to each surge area so that I can quickly act without manual planning."

Description

Analyzes matched surge alerts and staff availability to generate optimized deployment recommendations. Considers factors such as staff proximity, skill sets, and workload balancing. Provides clear, prioritized suggestions to managers for rapid assignment of team members to high-demand zones.

Acceptance Criteria

Surge Alert Triggers Optimization

Given a surge alert in Zone A and a list of available staff, when the system processes the alert, then it generates at least three deployment suggestions sorted by proximity and skill match, each containing staff ID, distance in meters, and matching skill rating.

Staff Proximity and Skill Matching

Given multiple available staff members with varied skill sets and locations, when recommendations are generated, then no suggested staff has a distance greater than 200 meters and each suggestion has a skill match score of at least 80%.

Workload Balancing Validation

Given current workload percentages for all staff, when generating deployment suggestions, then the system excludes any staff whose workload exceeds 80% and ensures that the variance of workload across zones after assignment does not exceed 10%.

Manager Reviews Prioritized Suggestions

Given a generated list of suggestions, when a manager opens the StaffSync UI, then the suggestions appear within 5 seconds, ordered by priority, with clear indicators for each staff member’s estimated arrival time and skill relevance.

Deployment Suggestion Reassessment

Given any change in surge alerts or staff availability, when the system detects an update within two minutes, then it automatically regenerates and re-prioritizes the deployment suggestions and updates the UI notification badge.

Notification and Alert System

"As a floor staff member, I want to receive alerts on my device so that I know when and where to assist customers during peak times."

Description

Delivers push notifications and in-app alerts to managers and designated staff devices. Alerts include surge zone details, recommended staff assignments, and expected response times. Customizable notification channels (SMS, email, mobile push) ensure timely awareness and prompt action.

Acceptance Criteria

Surge Zone Detection Notification

Given a surge zone is identified by the system, When the surge event is triggered, Then a push notification is delivered to all designated manager and staff devices within 5 seconds.

Custom Notification Channel Trigger

Given a manager has configured SMS and email as notification channels, When a surge alert is generated, Then the alert is sent via both SMS and email within the configured delivery SLA.

In-App Alert Visibility

Given a staff member opens the mobile app, When there is an active surge alert, Then the alert banner appears at the top of the dashboard displaying surge zone details and timestamp.

Recommended Staff Assignment Delivery

Given the system has computed recommended staff assignments for a detected surge, When notifications are sent, Then the message includes the names of recommended staff members and their expected response times.

Notification Failure and Retry Mechanism

Given a notification attempt fails due to network or delivery errors, When the first retry occurs after 30 seconds, Then the system retries up to three times and logs each attempt with error details.

Deployment Performance Analytics

"As a regional manager, I want to review past staff deployment performance so that I can optimize staffing strategies and resource allocation."

Description

Tracks and visualizes key metrics related to staff deployment effectiveness, including response times, coverage gaps, and customer satisfaction proxies. Provides historical reports to identify trends and inform future staffing strategies. Integrates with Heatway’s dashboard for unified insight delivery.

Acceptance Criteria

Real-time Response Time Tracking

Given a live surge alert is generated and a deployment suggestion is sent, the system records both timestamps The system calculates the response time between alert and suggestion with an accuracy of ±1 second Response times are updated in the dashboard in real time with no more than 2 seconds delay

Historical Trend Analysis Report Generation

When a user requests a weekly performance report, the system generates a report containing average response times, coverage gaps frequency, and satisfaction proxy trends for the selected period The report data is exportable in CSV and PDF formats The generated report renders in under 5 seconds for a 30-day period

Coverage Gap Visualization

Given staff deployment data over a 24-hour period, when rendered on the dashboard, coverage gaps with no staff presence for more than 10 minutes are highlighted in red The visualization allows filtering by zone, time range, and staff role Coverage gap overlays update within 3 seconds of new data arrival

Customer Satisfaction Proxy Correlation

When customer flow and staff response time metrics are available, the system calculates a satisfaction proxy score using a defined formula within 5% accuracy The proxy score updates hourly on the dashboard for the current day and past 7 days The system displays correlation coefficients between response times and satisfaction proxy with a disclaimer if data volume is below threshold (<50 events)

Dashboard Integration Consistency

When staff deployment analytics are viewed on the Heatway dashboard, the performance metrics module loads within the existing UI without layout shifts All analytics charts and tables adhere to the platform’s visual style guide and are responsive on desktop and tablet API errors or data fetch failures display a user-friendly message explaining retry options

ThresholdTailor

Enables users to define and adjust custom alert sensitivity levels per aisle, time block, or day part—ensuring notifications remain relevant to store rhythms and reducing alert fatigue while capturing meaningful traffic shifts.

Requirements

Custom Sensitivity Slider

"As a shop owner, I want a sensitivity slider for each aisle so that I can fine-tune alert triggers to match real-time traffic patterns and avoid unnecessary notifications."

Description

Implement a user-friendly slider interface allowing shop owners to set and adjust alert sensitivity levels for each aisle, enabling fine-grained control over threshold triggers. This feature integrates seamlessly with the existing traffic monitoring system, offering visual feedback as users tweak sensitivity to ensure notifications align with store dynamics and prevent excessive alerts.

Acceptance Criteria

Adjusting sensitivity for a specific aisle

Given a shop owner views the sensitivity slider for Aisle 3 When the owner moves the slider to a higher threshold Then the system updates the sensitivity value and displays the new threshold numerically above the slider

Visual feedback for threshold changes

When the sensitivity slider is moved Then a live graphical indicator updates in real time reflecting the new alert threshold level without page reload

Persisting custom sensitivity settings

Given the shop owner sets new sensitivity levels and navigates away When they return later Then the previously saved slider positions are loaded and reflect on the interface

Preventing excessive alert notifications

Given the sensitivity slider is set to low sensitivity When foot traffic spikes within the aisle Then no alert is sent if the traffic count does not exceed the configured threshold

Resetting to default sensitivity

When the shop owner clicks the reset button Then the slider returns to the default sensitivity value and the numerical indicator resets accordingly

Time Block Scheduler

"As a shop owner, I want to schedule different sensitivity levels for specific time blocks so that I can capture meaningful traffic shifts during peak and off-peak hours."

Description

Provide a scheduling module that lets users define custom time blocks (e.g., hourly intervals) for threshold settings. The scheduler should support drag-and-drop and calendar-style configuration, ensuring that sensitivity levels automatically adjust throughout the day based on predefined time segments.

Acceptance Criteria

Drag-and-Drop Time Block Creation

Given the user is on the Time Block Scheduler view, When they drag on the calendar from a start time to an end time, Then a new time block is created with the correct start and end times and default sensitivity settings.

Threshold Sensitivity Selection for a Time Block

Given a time block exists in the scheduler, When the user opens its detail panel and selects a custom sensitivity level, Then the selected sensitivity is saved and reflected in the block’s display immediately.

Automatic Threshold Switch at Block Boundaries

Given multiple time blocks with different sensitivity levels are defined, When the system time enters a new block, Then the active alert sensitivity updates automatically to the defined level without manual intervention.

Prevent Overlapping Time Blocks

Given one or more time blocks already exist, When the user attempts to create or resize a block that would overlap an existing block, Then the system blocks the action and displays an error message indicating the conflict.

Bulk Time Block Time Shift

Given several time blocks are selected, When the user drags the selection by a time interval on the calendar, Then all selected blocks shift by the same interval, preserving their durations and order.

Day-Part Differentiation

"As a shop owner, I want distinct threshold settings for morning, afternoon, and evening so that alerts stay relevant to natural variations in foot traffic."

Description

Enable users to assign unique threshold profiles to standard day parts (morning, afternoon, evening). This differentiation helps reduce alert fatigue by aligning notifications with typical customer flow trends during distinct portions of the day.

Acceptance Criteria

Creating a Morning Threshold Profile

Given a user is on the Day-Part Differentiation settings page When they select the 'Morning' segment and input custom low and high alert sensitivity values Then the system saves the morning profile, displays it in the profile list, and applies it to incoming alerts during the morning period

Modifying Afternoon Threshold Settings

Given an existing 'Afternoon' threshold profile exists When the user adjusts the sensitivity slider values and clicks 'Save' Then the system updates the profile with the new thresholds and shows a confirmation message

Assigning Evening Threshold Parameters

Given default evening thresholds are active When the user creates a new 'Evening' profile and sets distinct alert parameters Then the system stores the evening profile separately and applies it upon entering the evening day part

Validating Threshold Switching at Day-Part Boundaries

Given the system clock reaches the defined afternoon start time at 12:00 PM When the day-part boundary is crossed Then the system automatically deactivates the morning profile and activates the afternoon profile, using its thresholds for subsequent alerts

Ensuring Persistence of Custom Day-Part Thresholds Across Devices

Given a user configures custom thresholds for morning, afternoon, and evening on one device When they log in from another device within the same account Then the system synchronizes all custom day-part profiles within 5 seconds and displays identical threshold settings

Alert Frequency Control

"As a shop owner, I want to control how often I receive alerts for the same aisle so that I don’t get overwhelmed by repeated notifications."

Description

Allow users to specify minimum intervals between alerts for each aisle and time block, preventing notification overload. Configurable frequency settings ensure that once a threshold is breached and an alert sent, subsequent notifications are delayed according to user-defined rules.

Acceptance Criteria

Setting Minimum Alert Interval for Morning Aisle

Given a user sets a 30-minute minimum alert interval for Aisle 5 during the Morning time block, When the traffic threshold is breached a second time within 20 minutes, Then only the first alert is sent and subsequent alerts are suppressed until 30 minutes have passed.

Updating Existing Alert Interval Settings

Given a user updates the minimum interval from 30 to 45 minutes for Aisle 3 in the Afternoon time block, When the configuration is saved, Then all future alerts in that aisle and time block must respect the new 45-minute interval.

Enforcing Interval Across Multiple Threshold Breaches

Given a user defines a 15-minute minimum alert interval for Aisle 7 at any time of day, When the threshold is breached three times within 15 minutes, Then only one alert is generated and the next alert occurs no sooner than 15 minutes after the first.

Validating Invalid Interval Inputs

Given a user attempts to set a negative or zero-minute interval for any aisle or time block, When the user saves the setting, Then the system rejects the input and displays a validation error indicating the minimum allowable interval is one minute.

Resetting Interval at New Day Part

Given a user configures a 20-minute interval for the Evening time block, When the time block transitions from Evening to Night, Then the alert counter resets, allowing a new alert immediately if the threshold is breached in the Night block.

Preset Template Management

"As a shop owner, I want to choose from preset sensitivity templates so that I can quickly configure relevant alerts without starting from scratch."

Description

Supply a library of preconfigured sensitivity templates optimized for common store layouts and traffic patterns. Users can apply, customize, and save templates, accelerating setup and ensuring best-practice threshold configurations are easily accessible.

Acceptance Criteria

Selecting a Preconfigured Template from the Library

Given the user navigates to the Preset Template Management screen and selects a template, When the user clicks Apply, Then the system loads the template’s sensitivity settings into the aisle overview panel; The applied thresholds match the predefined values for each aisle as defined by the template.

Customizing Thresholds after Template Application

Given a template has been applied, When the user adjusts the sensitivity slider for any aisle or time block, Then the displayed thresholds update in real time and the Save option becomes enabled; The user receives a confirmation message that customization changes are pending save.

Saving a Customized Template as a New Preset

Given the user has made adjustments to an applied template, When the user selects Save As and enters a unique template name, Then the system stores the new preset in the template library with the correct thresholds; The new template appears in the library list and can be re-applied in future sessions.

Applying a Template Across Multiple Aisles in Bulk

Given the user selects multiple aisles in the map interface and chooses a template, When the user applies the template, Then each selected aisle adopts the template’s sensitivity settings simultaneously; No individual aisle retains previous custom thresholds.

Restoring Original Template Settings

Given the user has customized or applied a template, When the user clicks Restore Defaults, Then all sensitivity settings revert to the original values defined by the last applied or saved template; The interface displays a confirmation prompt before restoring, and a success message after completion.

Real-Time Feedback Loop

"As a shop owner, I want to see real-time feedback on how my threshold adjustments affect alerts so that I can immediately refine settings for optimal results."

Description

Incorporate live visualization of alert triggers and traffic heatmaps that update dynamically as users adjust thresholds. Instant feedback allows users to see the impact of their sensitivity settings in real time, facilitating on-the-fly optimization.

Acceptance Criteria

Live Aisle Sensitivity Adjustment

Given the user opens the ThresholdTailor panel for a specific aisle, when they move the sensitivity slider, then the in-store heatmap updates within 1 second to reflect the new threshold settings.

Time Block Threshold Tuning

Given the user selects a time block (e.g., 8–10 AM) and adjusts its alert threshold, when they confirm the change, then live traffic alerts and heatmap overlays immediately reflect the updated criteria for that time block.

Day Part Sensitivity Preview

Given the user toggles between day parts (morning, afternoon, evening) and modifies the custom sensitivity for each, when changes are applied, then the visual feedback panel shows updated alert trigger points and heat intensity on the map in real time.

Simultaneous Multi-Aisle Updates

Given the user updates sensitivity thresholds for multiple aisles in one session, when each threshold is changed, then the dynamic heatmap concurrently refreshes each aisle’s traffic visualization without delay or data overlap.

Rapid Threshold Cycling Under Load

Given the store experiences high foot traffic, when the user rapidly adjusts the sensitivity thresholds back and forth, then the system maintains real-time visualization accuracy with no more than 2-second refresh latency and no dropped updates.

ContextClip

Attaches a snapshot of the live heatmap segment behind each alert, complete with highlighted product categories and traffic density metrics, empowering staff with visual cues for faster decision-making on the floor.

Requirements

Snapshot Capture Functionality

"As a store associate, I want to see a snapshot of the current heatmap segment when an alert is generated so that I can quickly understand customer traffic patterns in that area and take timely action."

Description

The system must capture and generate a real-time snapshot of the specific heatmap segment at the moment an alert triggers. This snapshot should include the store layout context, timestamp, and relevant metadata to provide immediate visual context. The capture process must be optimized to occur within seconds of the alert event to maintain relevance.

Acceptance Criteria

Snapshot Generation on High Traffic Alert

Given a high-traffic alert is triggered for a specific heatmap segment When the alert event occurs Then the system captures and displays a snapshot of that segment including real-time heat density within 3 seconds

Metadata Inclusion in Snapshot

Given an alert-triggered snapshot When the snapshot is generated Then it includes the store layout context, timestamp, alert type, and relevant metadata fields in the image file

Snapshot Capture Performance

Given any alert triggers Regardless of store size When the system generates the snapshot Then the process completes in under 5 seconds 95% of the time

Snapshot Storage and Retrieval

Given snapshots are generated for alerts When a user requests the snapshot history Then the system retrieves and displays snapshots with their metadata within 2 seconds

Error Handling During Snapshot Capture

Given a failure occurs during snapshot capture When the system encounters an error Then it logs the error, retries capture up to two times, and notifies the user if unsuccessful

Product Category Highlighting

"As a store associate, I want product categories highlighted on the heatmap snapshot so that I can immediately identify which product zones are experiencing higher or lower traffic densities."

Description

The captured heatmap snapshot should overlay product category boundaries with distinct color-coded highlights for each category. The highlighting must dynamically adjust based on the store’s category mapping and be clearly labeled to help staff instantly identify which product areas are being affected.

Acceptance Criteria

Heatmap Snapshot Rendering with Category Overlays

Given a live heatmap snapshot is captured for a store layout, when displayed in ContextClip, then each product category boundary is overlaid with a distinct color matched to the store's category mapping and the category name label is displayed within the boundary.

Dynamic Category Coloring

Given the store's category mapping is updated, when a heatmap snapshot is generated, then the overlay colors adjust dynamically to reflect the new category-color assignments without requiring a page refresh.

Ensure Label Visibility Under Varying Traffic Densities

Given a heatmap snapshot with low, medium, and high traffic density areas, when the overlay is applied, then the category labels remain legible and non-overlapping and maintain contrast against the heatmap background.

Real-Time Overlay on Alert Notifications

Given an alert is triggered due to a category's traffic threshold event, when ContextClip attaches the snapshot, then the overlay includes the correct color-coded boundaries and labels of the category related to the alert.

Fallback Handling for Missing Category Mappings

Given a heatmap snapshot generated for areas with missing or undefined category mappings, when displayed, then those areas use a default highlight color and are labeled as 'Unassigned Category' to indicate missing mapping.

Traffic Density Metrics Overlay

"As a store manager, I want to view traffic density metrics overlaid on the snapshot so that I can assess customer concentration levels without switching screens."

Description

Each snapshot must display real-time traffic density metrics, both as numerical values and color gradients, directly on the image. Metrics should include visitor count, dwell time averages, and density heat levels. These overlays must be legible and positioned to avoid obscuring critical layout details.

Acceptance Criteria

Real-Time Overlay Legibility Check

Overlay text and numeric values maintain a minimum contrast ratio of 4.5:1 against the background; font size is at least 14px; overlay opacity does not exceed 50%; no overlay elements overlap highlighted product areas in the snapshot.

Accurate Numerical Metrics Display

Displayed visitor count matches backend data within ±1; average dwell time corresponds to computed dwell time within ±0.5 seconds; density heat levels correlate correctly to predefined density thresholds.

Color Gradient Representation Validation

Traffic density is represented by a continuous gradient from blue (#0000FF) for low density to red (#FF0000) for high density; gradient scale updates in real time without lag; gradient legend displays minimum and maximum scale values.

Dynamic Overlay Positioning

Overlays automatically position in an empty quadrant of the image avoiding overlap with at least 80% of product bounding boxes; repositioning occurs instantaneously when layout changes; maintains 8px padding from key layout edges.

Metric Synchronization and Refresh Timing

All overlay metrics (visitor count, dwell time, heat levels) refresh within 1 second of snapshot capture; overlays update in the same render frame; no outdated metrics are displayed after refresh.

Contextual Action Recommendations

"As a store associate, I want actionable suggestions attached to each heatmap snapshot alert so that I can respond effectively on the floor without guessing the best course of action."

Description

Attach predefined contextual action buttons or recommendations alongside each snapshot based on traffic anomalies. Recommendations might include restocking alerts, staff repositioning suggestions, or promotional display adjustments. Actions should be configurable by store managers.

Acceptance Criteria

Low Traffic Zone Restocking Recommendation

Given a heatmap snapshot showing a zone with traffic below the manager-defined threshold and a restocking action configured, When the anomaly is detected, Then the system displays a “Restock Items” button adjacent to the snapshot.

High Traffic Zone Staff Repositioning Suggestion

Given a real-time heatmap segment with traffic exceeding the high-traffic threshold and a staff repositioning action defined, When the traffic spike is detected, Then a “Reposition Staff” recommendation appears with suggested staff count and location.

Customizable Promotional Display Adjustment

Given a heatmap showing decreased engagement in a promotional display area and a promotional adjustment action configured by the manager, When engagement drops below the defined threshold, Then an “Adjust Promotion” button is shown and clicking it opens the promotional display editor.

Manager Configures Contextual Actions

Given the store manager accesses the contextual actions configuration panel, When they add, remove, or modify contextual action buttons and save changes, Then the updated actions are immediately available on incoming snapshots.

Anomaly Alert Suppression When No Actions Configured

Given an anomaly is detected but no contextual actions are configured for that anomaly type, When the snapshot alert is generated, Then no action buttons are displayed and a message “No actions available” appears.

Historical Comparison Toggle

"As a store manager, I want to compare the current heatmap snapshot with historical data of the same segment so that I can identify anomalies, trends, and the effectiveness of past interventions."

Description

Provide a toggle feature that allows users to switch between the live snapshot and historical heatmap data for the same store segment. Historical data should include comparable metrics over selectable time ranges to highlight trends and anomalies. This functionality must load quickly and synchronize metrics for easy comparison.

Acceptance Criteria

Quick Toggle Response

Given a user is viewing the live heatmap, when the user clicks the Historical Comparison Toggle, then the system loads the corresponding historical heatmap and synchronized metrics for the selected segment within 2 seconds.

Selectable Time Range Comparison

Given a user has switched to historical view, when the user selects a custom date range, then the system displays historical metrics (customer count, dwell time, heat intensity) for that range in the same format as the live view.

Visual Highlight Synchronization

Given the live heatmap highlights product categories and traffic zones, when toggling to historical view, then the same categories and zones remain highlighted using matching color codes on the historical heatmap.

Consistent Data Measurement Units

Given the user toggles between live and historical views, when metrics are displayed, then percentage changes and absolute counts use the same units and decimal precision as in the live view.

Cross-Device Functionality

Given a user on mobile or desktop, when using the Historical Comparison Toggle, then the feature loads correctly, displays synchronized metrics, and maintains responsive UI behavior across devices.

ZoneZoom

Groups adjacent aisles into logical zones and issues consolidated alerts when any zone experiences a surge, helping managers oversee broader customer movements and coordinate strategic responses across multiple areas.

Requirements

Zone Definition Editor

"As a store manager, I want to define and adjust zones by grouping aisles so that I can monitor customer movement patterns at a higher level without manually tracking individual aisles."

Description

Provides an intuitive interface for managers to group adjacent aisles into logical zones by drawing or selecting aisle boundaries. This feature enables quick creation, editing, and deletion of zones, ensuring that zones accurately reflect the store layout and can be updated as merchandise arrangements change.

Acceptance Criteria

New Zone Creation by Drawing Aisles

Given the manager opens the Zone Definition Editor, when they draw boundaries around adjacent aisles on the store map, then the selected aisles are highlighted and the “Save Zone” button is enabled, and when clicked, the new zone appears in the zone list.

Zone Editing via Aisle Selection

Given an existing zone in the editor, when the manager selects additional aisles or deselects current aisles, then the zone boundary updates dynamically and the “Save Changes” button becomes active, and upon saving, the updated zone reflects in the zone list.

Zone Deletion Confirmation

Given the manager views a zone in the editor, when they click the delete icon for that zone, then a confirmation dialog appears, and if the manager confirms, the zone is removed from the list and its aisles revert to ungrouped state.

Saving Zone Changes

Given the manager has created or modified a zone, when they click “Save” and reload the editor page, then the zone list displays all changes accurately, and the zones persist across sessions.

Invalid Zone Overlap Prevention

Given existing zones defined in the editor, when the manager attempts to create or modify a zone that overlaps with another, then the system blocks the save action and displays an error message indicating that zones cannot overlap.

Real-time Zone Monitoring

"As a store manager, I want to see heatmap updates for each zone in real time so that I can respond immediately to unexpected customer surges."

Description

Continuously aggregates foot traffic data from individual aisles into the defined zones and updates zone-level heat values in real time. This functionality ensures that managers have up-to-the-second visibility into which zones are experiencing surges, allowing them to make immediate layout or staffing adjustments.

Acceptance Criteria

Zone Data Aggregation Accuracy

Given continuous foot traffic data streams from all aisles, When the system aggregates the counts into the defined zone, Then the zone heat value equals the sum of the individual aisle counts and updates within 5 seconds.

Zone Surge Detection and Alerting

Given a zone threshold of a 20% traffic increase over the rolling 5-minute average, When the aggregated zone count exceeds this threshold, Then the system issues a consolidated surge alert to the manager within 2 seconds.

Dashboard Real-time Refresh

Given updated zone heat values from the backend, When new data arrives, Then the user interface refreshes the displayed heatmap for all zones within 3 seconds without manual page reload.

No Traffic Handling

Given no foot traffic data received for a zone over a continuous 10-second window, When absence is detected, Then the dashboard shows a ‘No Traffic’ indicator and a heat value of zero for that zone.

Network Recovery and Data Consistency

Given a temporary network interruption of up to 30 seconds, When the connection is restored, Then the system reconciles any missed aisle data, updates zone heat values without duplication or data loss, and reflects accurate counts within 5 seconds.

Consolidated Zone Surge Alerts

"As a store manager, I want to receive notifications when a zone experiences a surge so that I can allocate staff or adjust displays to better serve customers."

Description

Sends configurable alerts when any zone’s aggregated foot traffic exceeds a predefined threshold. Alerts can be delivered via push notification, SMS, or email, and include zone name, current traffic level, and time of alert to help managers coordinate responses across multiple areas efficiently.

Acceptance Criteria

Zone Surge Threshold Exceeded

Given the aggregated foot traffic for Zone A exceeds the configured threshold When the surge detection process runs Then a push notification is sent to the manager’s device within 30 seconds of detection

SMS Alert Delivery When Push Unavailable

Given the push notification service is unavailable When a zone’s foot traffic surges above threshold Then an SMS alert containing zone name, traffic level, and time is delivered to the manager’s phone within 60 seconds

Email Alert Fallback After Push and SMS Fail

Given both push and SMS alert deliveries fail When a zone’s surge is detected Then an email with the consolidated alert details is sent to the manager’s registered email address within 2 minutes

Alert Content Accuracy

Given any alert type (push, SMS, email) When an alert is generated Then the message includes the correct zone name, current aggregated traffic count, and precise timestamp in ISO 8601 format

Immediate Application of Updated Thresholds

Given a manager updates a zone’s surge threshold in settings When the update is saved Then the new threshold is applied to all subsequent traffic evaluations immediately without system restart

Zone Threshold Configuration

"As a store manager, I want to configure surge thresholds for each zone so that I only receive meaningful alerts suited to my store’s traffic patterns."

Description

Allows managers to set and customize surge thresholds for each zone, including minimum and maximum values, time windows, and notification frequency limits. This ensures that alerts are relevant and help prevent notification fatigue by only triggering when genuinely actionable conditions occur.

Acceptance Criteria

Default Zone Threshold Settings

Given the manager accesses the Zone Threshold Configuration page, when no custom values have been entered, then the system displays default minimum and maximum surge thresholds and allows saving without modification.

Custom Threshold Value Input

Given the manager enters valid minimum and maximum threshold values within the allowed range for a selected zone, when the manager saves the configuration, then the system persists the new threshold values and applies them to real-time surge detection for that zone.

Time Window Configuration

Given the manager specifies a start and end time for threshold evaluation, when the configuration is saved, then the system only evaluates surge thresholds within the defined time window for the corresponding zone.

Notification Frequency Limit Enforcement

Given the manager sets a maximum number of notifications per hour for a zone, when the surge condition is met repeatedly, then the system sends no more than the configured number of alerts within any rolling one-hour period.

Threshold Validation Error Handling

Given the manager inputs a minimum value greater than the maximum or enters negative values, when attempting to save, then the system displays clear validation errors for each invalid field and prevents the configuration from being saved until corrected.

Zone Performance Dashboard

"As a store manager, I want a dashboard that shows zone-by-zone performance over time so that I can identify layout blind spots and optimize product placement."

Description

Offers a dedicated dashboard view that displays historical and current heatmap data for each zone, including trends, peak times, and comparative performance. The dashboard provides charts and tables to help managers analyze which zones consistently underperform and identify opportunities for layout optimization.

Acceptance Criteria

Zone Trend Visualization

Given a manager selects a specific zone and a date range, When the dashboard loads, Then the time-series chart displays hourly foot traffic counts for the selected period with continuous data points and a trend line without gaps.

Peak Time Identification

Given a manager views a zone’s detailed performance, When requesting peak time analysis, Then the dashboard highlights the top three hourly intervals with the highest foot traffic and these intervals match the highest values from the stored data.

Comparative Performance Table

Given a manager selects multiple zones and a date range, When the comparison view is activated, Then a table appears listing each zone’s total visits, average visits per hour, and percentage change versus the previous period, sorted by descending total visits.

Historical Data Export

Given a manager clicks the Export Report button for a zone and date range, When the export is processed, Then a CSV file downloads containing date, hour, and foot traffic counts per hour for the selected period, formatted with headers and no missing entries.

Underperforming Zone Highlight

Given a manager views the overall zone dashboard, When zones are evaluated, Then any zone with an average hourly visit count below the defined threshold is visually flagged in red with an accompanying alert icon.

SurgeCast

Provides an interactive forecast chart displaying predicted sales spikes and foot traffic by hour, enabling shop owners to visualize upcoming peak periods and plan operations proactively.

Requirements

Predictive Traffic Modeling

"As a shop owner, I want to see predicted hourly foot traffic and sales spikes so that I can plan staffing levels and display arrangements in advance."

Description

Implement a machine-learning based forecasting engine that analyzes historical foot traffic and sales data to generate hourly foot traffic and sales spike predictions for the next 24 hours. The engine will ingest multiple data sources, apply statistical models, and continuously retrain to improve forecast accuracy. It should seamlessly integrate with Heatway’s backend pipeline and provide an API for the UI to fetch predicted values. This capability enhances shop owners’ ability to anticipate customer flow and optimize staffing and displays.

Acceptance Criteria

Hourly Forecast Data Availability

Given valid historical foot traffic and sales data, When the forecasting API is called, Then the response returns 24 hourly foot traffic and sales predictions with accurate timestamps.

Forecast Accuracy Validation

Given a historical test dataset, When predictions are generated by the engine, Then the mean absolute percentage error (MAPE) for both foot traffic and sales predictions is below 15%.

Real-Time API Integration

Given the backend pipeline is operational, When the UI fetches predictions for the next 24 hours, Then the API responds within 500ms with HTTP status 200 and a valid JSON payload.

Data Source Ingestion Robustness

Given multiple data sources with potential anomalies, When the engine ingests data containing invalid or missing records, Then it logs errors, excludes bad records from processing, and completes the ingestion without failure.

Model Retraining Pipeline

Given new foot traffic and sales data availability, When the scheduled retraining job runs, Then the model retrains within 2 hours and produces updated forecast metrics that maintain or improve current accuracy benchmarks.

Interactive Forecast Visualization

"As a shop owner, I want an interactive hourly forecast chart so that I can easily explore and understand upcoming peak times."

Description

Design and build an interactive chart component that displays the predicted foot traffic and sales spikes by hour in an intuitive, zoomable, and filterable interface. Users can hover over data points to see detailed figures, toggle between traffic and sales views, and adjust the time range. The chart must update dynamically when new forecasts are available and maintain performance on mobile devices. This feature allows shop owners to visually explore peak periods and make data-driven decisions.

Acceptance Criteria

Viewing Hourly Forecast on Desktop

Given the user is on the SurgeCast page on a desktop browser When the page loads Then the interactive chart renders showing 24-hour predicted foot traffic and sales spikes with distinct color-coded lines

Filtering Forecast by Metric

Given the chart is displayed When the user toggles from traffic view to sales view Then the chart updates to hide traffic data and display sales spike data without a full page reload

Zooming into Specific Time Range

Given the chart is visible When the user selects a time range (e.g., 2 PM to 6 PM) using the zoom controls Then the chart zooms into the selected timeframe and axes rescale accordingly

Real-time Data Update

Given new forecast data is available from the server When the data is received by the client Then the chart updates dynamically to reflect the new predictions without requiring manual refresh

Mobile Performance under Low-bandwidth

Given the user accesses the SurgeCast chart on a mobile device under weak network conditions When the chart loads or updates Then all interactions (hover, toggle, zoom) complete within 500ms and frame rate stays above 30fps

Peak Period Alerts

"As a shop owner, I want to receive alerts when a significant traffic or sales spike is forecasted so that I can take proactive measures."

Description

Implement a notification system that alerts shop owners of imminent forecasted peaks via in-app notifications and optional email or push notifications. Users can configure threshold conditions, such as predicted foot traffic exceeding a certain value or expected sales spike percentages. Alerts should include the time window, forecast values, and recommended actions. This ensures owners receive timely reminders to prepare for high-demand periods.

Acceptance Criteria

Configuring Threshold Conditions

Given a shop owner accesses the Peak Period Alerts settings When they set a foot traffic threshold of X or a sales spike percentage of Y Then the system saves the configured threshold and displays a confirmation message

Receiving In-App Alerts

Given the forecasted foot traffic exceeds the configured threshold within the next time window When the forecast is generated Then the shop owner receives an in-app notification containing the time window, forecast values, and a link to recommended actions

Receiving Email Notifications

Given the shop owner has enabled email alerts and a peak is forecasted When the forecast model predicts traffic or sales above the threshold Then an email is sent within 5 minutes containing the peak time window, forecast details, and recommended actions

Receiving Push Notifications

Given the shop owner has enabled push notifications When the forecast triggers an alert Then a mobile push notification is delivered immediately showing the time window, forecast values, and recommended steps

Alert Content Verification

Given an alert is sent via any channel When the shop owner views the alert Then it includes the accurate time window, forecast foot traffic number, sales spike percentage, and at least one actionable recommendation

Customizable Forecast Parameters

"As a shop owner, I want to customize forecast settings so that the predictions reflect my store’s unique traffic patterns."

Description

Provide a settings interface where shop owners can adjust forecasting parameters, including forecast horizon (e.g., next 6, 12, or 24 hours), preferred confidence intervals, and seasonal adjustment factors. Changes to parameters will trigger on-demand model recalculation. By offering customization, the system can cater to different business rhythms and owner preferences, enhancing accuracy and relevance.

Acceptance Criteria

Adjust Forecast Horizon Selection

Given the shop owner is on the Forecast Parameters settings page, when they select a forecast horizon option (6, 12, or 24 hours) and click save, then the system updates the model parameters and displays a success confirmation within 5 seconds.

Set Custom Confidence Interval

Given the shop owner inputs a custom confidence interval value between 50% and 99% and saves, then the system validates the value, recalculates the forecast using the new interval, and updates the forecast chart with the selected confidence band within 10 seconds.

Apply Seasonal Adjustment Factor

Given the shop owner enables the seasonal adjustment toggle and selects a seasonal factor (e.g., holiday or weekend), when saved, then the forecast model incorporates the adjustment and the chart reflects the seasonal variation.

On-Demand Model Recalculation

Given any change to forecast parameters, when the shop owner clicks "Recalculate Now", then the system queues the model recalculation job and notifies the user of progress, completing within 30 seconds and refreshing the forecast display automatically.

Validation of Parameter Limits

Given the shop owner enters out-of-range values for forecast horizon or confidence intervals, when they attempt to save, then the system displays inline error messages and prevents submission until valid values are entered.

Forecast Data Export

"As a shop owner, I want to export forecast data to CSV or PDF so that I can share and analyze trends outside the Heatway app."

Description

Enable shop owners to export forecast data and visualizations as CSV files or PDF reports. Exports should include hourly predictions, confidence levels, and chart snapshots. Users can schedule automated exports to email or download on demand. This functionality allows owners to share insights with staff or stakeholders and integrate forecast data with other business tools.

Acceptance Criteria

Ad Hoc CSV Export Request

Given the shop owner is viewing the SurgeCast forecast chart, when they click the “Export CSV” button, then the system initiates a download of a CSV file containing hourly predictions, confidence levels, and properly labeled headers.

Scheduled Email PDF Report Delivery

Given the shop owner schedules a daily PDF export for a specified email address and time, when the scheduled time occurs, then the system generates a PDF report with chart snapshots, hourly predictions, and confidence levels, and sends it as an email attachment.

Download On-Demand PDF Report

Given the shop owner is viewing the SurgeCast forecast, when they click the “Export PDF” button, then the system generates and prompts download of a PDF report that includes the forecast chart snapshot, hourly data table, and confidence levels.

Include Confidence Levels in Export

Given any export request (CSV or PDF), when the file is generated, then it includes a confidence level column for each hourly prediction formatted as a percentage with two decimal places.

Integration With External Tools via CSV

Given the exported CSV file, when the shop owner imports it into external tools (e.g., Excel, Google Sheets, BI platform), then the data imports without errors, maintains header integrity, and correctly parses hourly predictions and confidence levels.

ZoneSurge

Identifies and forecasts the specific store zones expected to experience the highest customer density, allowing owners to optimize product placement and layout in targeted areas before peak times.

Requirements

Historical Foot Traffic Aggregation

"As a store owner, I want to review past foot traffic trends by zone so that I can understand baseline customer behavior and identify zones that typically attract the most interest."

Description

Aggregate and normalize at least 30 days of anonymized foot traffic data per zone, enabling the system to learn baseline customer movement patterns across different days and times. This functionality should seamlessly integrate with the Heatway data pipeline, storing the cleaned dataset in a scalable database for efficient retrieval. It lays the groundwork for both real-time detection and predictive forecasting by ensuring high-quality historical context.

Acceptance Criteria

Baseline Data Ingestion Scenario

Given at least 30 days of anonymized foot traffic raw logs are available When the aggregation pipeline runs Then the system ingests and stores 100% of the logs into the staging area without data loss

Data Cleansing and Normalization Scenario

Given ingested raw logs containing timestamps and zone identifiers When the normalization process executes Then missing or malformed entries are corrected or removed and traffic counts are normalized according to defined business rules

Scalable Database Storage Scenario

Given cleaned and normalized historical data sets When data is written to the production database Then records are partitioned by zone and date and stored in a scalable, indexed table accessible for queries

Historical Data Retrieval Performance Scenario

Given a request for 30 days of aggregated foot traffic by zone When querying the historical database Then the system returns results for all zones within 200 milliseconds

Integration with Real-Time Pipeline Scenario

Given new real-time traffic data arriving concurrently with historical data When the forecasting module queries the data store Then it receives a unified data set combining historical and real-time records without delay

Anonymization Compliance Verification Scenario

Given raw customer data containing potential identifiers When the aggregation and normalization processes run Then all personally identifiable information is removed and audit logs confirm compliance with privacy policy

Real-Time Zone Detection

"As a shop owner, I want the app to show me in real-time which areas of my store are most crowded so that I can move staff or adjust displays on the spot."

Description

Continuously monitor incoming smartphone-generated location pings to identify zones within the store that currently have the highest concentration of customers. The system must process location data with sub-second latency, update zone occupancy counts in real-time, and provide an API endpoint for the ZoneSurge module to retrieve live density metrics. This functionality is critical for immediate in-store layout adjustments and inventory placement.

Acceptance Criteria

Peak Hour Zone Identification

Given continuous location pings during peak hours with at least 100 concurrent customers, When the system processes each batch, Then the top three highest‐density zones are updated within 500ms of data receipt and available via the API.

Post-Promotion Surge Detection

Given a promotional alert drives an influx of at least 20 new pings in a specific zone within 10 seconds, When the system receives these pings, Then it must update that zone’s occupancy count and flag it as the highest‐density zone in the API response.

High-Load API Performance

Given an incoming load of 5,000 location pings per second, When the ZoneSurge module requests live density metrics, Then the API must return the latest zone counts within 200ms, respond with HTTP 200, and include a JSON payload of all zones.

Real-Time Update Continuity

Given an uninterrupted data stream over a 12-hour store operation period, When the system is running, Then zone occupancy counts are updated every second without skipping any intervals or downtime.

Empty Store Response

Given no incoming location pings for any store zone over a continuous 5-second interval, When the API is queried, Then it must return zero occupancy for all zones.

Predictive Zone Forecasting

"As a shop owner, I want to receive predictions of which store zones will be busiest in the next hour so that I can proactively optimize product placement before peak times."

Description

Implement machine learning models that analyze historical and real-time foot traffic data to forecast zone density for the next 60-120 minutes. The forecasting engine should account for variables such as time of day, day of week, special promotions, and external factors like weather. Integration with the Heatway AI backend must allow the system to continuously retrain models with new data, improving forecast accuracy over time.

Acceptance Criteria

Peak Hour Forecast Based on Historical Data

Given historical foot traffic data for a specific zone over the past 30 days, when the system processes the data, then it generates a density forecast for the next 60 minutes with a maximum MAPE of 10%; when forecasting for 120 minutes, MAPE must not exceed 15%.

Real-Time Weather Impact Adjustment

Given real-time weather data is available, when adverse weather conditions are detected, then the forecast adjusts zone density predictions accordingly, reflecting at least a 5% change in predicted values compared to forecasts without weather data.

Promotional Event Surge Prediction

Given an active promotion is scheduled or ongoing, when promotion parameters (e.g., discount rate, duration) are input, then the model incorporates these variables to forecast a minimum 10% increase in zone density during promotion periods compared to baseline.

Continuous Model Retraining Integration

Given new foot traffic data becomes available every 15 minutes, when the retraining pipeline initiates, then the model is retrained automatically without downtime, and model accuracy improves or remains stable (no degradation beyond 2% increase in MAPE) after each retraining cycle.

60-Minute Ahead Zone Density Alert

Given density forecasts for the next 60 minutes are generated, when predicted density in any zone exceeds 80% of peak capacity, then the system triggers an alert to store owners within 30 seconds of forecast generation.

Visual Heatmap Overlay

"As a store manager, I want to see a color-coded heatmap of my store layout showing live and forecasted traffic so that I can quickly identify hotspots and cold spots."

Description

Display an interactive heatmap overlay on the store floorplan that color-codes zones based on current and predicted customer density. The UI component should support zoom, pan, and toggle between live and forecast views, and integrate seamlessly into both mobile and web dashboards. This visual representation enables quick comprehension of traffic surges and helps in making layout decisions at a glance.

Acceptance Criteria

Live Heatmap Display on Mobile Dashboard

Given the shop owner opens the mobile dashboard and selects Live Heatmap, when data is available, then the heatmap overlay renders on the current floorplan within 2 seconds. The heatmap auto-refreshes every 5 seconds with updated customer density. Zones reflect density levels with colors ranging from blue (low) to red (high) based on predefined thresholds.

Forecast Heatmap Toggle on Web Dashboard

When the shop owner clicks the Forecast toggle, then the overlay switches from live data to predicted density heatmap within 1 second. The forecast heatmap is based on machine learning predictions for the next hour and displays distinct color legends. When toggled back to Live view, the map returns to real-time data without errors.

Heatmap Zoom and Pan Interaction

Given the heatmap is displayed, when the user performs a pinch-to-zoom or mouse wheel action, then the floorplan and heatmap scale correspondingly without distortion. When the user drags the floorplan, then the view pans smoothly to reveal other zones, maintaining heatmap accuracy. Zoom and pan actions persist heatmap layer alignment and intensity values.

Heatmap Color-Coding Accuracy

For each zone on the floorplan, the displayed color matches the underlying density value as per the predefined threshold ranges. When density values change, the color transitions smoothly to the new status within 0.5 seconds. A color legend is visible and accurately maps color gradients to density ranges.

Seamless View Mode Switching

When switching between Live and Forecast views on both mobile and web, there is no flicker or data loss, and the correct heatmap renders. The toggle icon updates to reflect the current mode (Live or Forecast) immediately upon user action. Switching modes preserves the current zoom level and pan position.

Alert Notification System

"As a shop owner, I want to receive an alert when a zone is predicted to get overcrowded so that I can prepare by moving staff or restocking inventory."

Description

Notify users via push notifications or email when specific zones are forecasted to exceed a configurable density threshold or when sudden spikes occur. The notification system must allow users to set custom thresholds per zone and define quiet hours to prevent unnecessary alerts. This proactive alerting ensures shop owners are immediately informed of critical density changes without actively monitoring the dashboard.

Acceptance Criteria

Threshold-Based Alert Notification

Given a user has set a density threshold of 50 customers for Zone A, when the forecasted customer density for Zone A exceeds 50 in the next 30 minutes, then the system sends a push notification and an email within 60 seconds.

Spike-Based Alert Notification

Given real-time monitoring of Zone B, when the customer count increases by more than 30% within a rolling 10-minute window, then the system issues an immediate push notification indicating a sudden spike.

Quiet Hours Alert Suppression

Given the user has configured quiet hours from 22:00 to 06:00, when any threshold or spike condition is met during quiet hours, then no push notifications or emails are sent until quiet hours end.

Notification Channel Delivery

Given a user enables both push notifications and email alerts for Zone C, when an alert is triggered for Zone C, then the system delivers the notification via both channels and logs delivery status for each.

User Configuration Persistence

Given a user sets or updates thresholds and quiet hours in the settings, when the user logs out and logs back in, then the previously configured thresholds and quiet hours are retained and applied to future alerts.

ShiftSync

Automatically generates recommended staff schedules based on predicted spike times and zones, ensuring optimal coverage and reducing under- or over-staffing during busy periods.

Requirements

Traffic Spike Prediction

"As a store manager, I want the system to forecast peak customer times and high-traffic zones so that I can proactively staff those periods appropriately."

Description

Analyze historical foot traffic heatmap data to forecast peak customer times and high-traffic zones, enabling proactive staffing aligned with anticipated demand. This requirement ensures the system leverages Heatway’s real-time and historical data to generate accurate predictions of spike windows, informing subsequent scheduling decisions.

Acceptance Criteria

Forecast Peak Times for Weekday Mornings

Given at least 90 days of historical foot traffic data When the prediction job runs for the upcoming Monday Then the system identifies the top 3 one-hour peak windows with at least 85% accuracy compared to actual historical peaks

Identify High-Traffic Zones

Given a store floorplan with zones defined and 30 days of heatmap data When zone-level traffic prediction is executed for the next day’s spike period Then the system ranks the top 5 zones by predicted average foot traffic volume, matching historical zone rankings within a 10% variance

Generate Visual Forecast Report

Given successful prediction of peak windows and high-traffic zones When a forecast report is generated in the dashboard Then it includes a timeline chart of predicted spikes, overlayed zone heatmap for each spike window, and a summary section with recommended staffing levels

Real-Time Adjustment Alerts

Given live foot traffic counts are collected continuously When real-time traffic exceeds the predicted baseline by more than 20% for at least 10 minutes Then the system triggers an in-app and email alert recommending immediate staffing adjustments

Integration with Scheduling Module

Given generated spike predictions for time windows and zones When feeding these predictions into the ShiftSync scheduling engine Then the recommended staff schedules reflect predicted spike windows within a 15-minute margin and assign staff to top three high-traffic zones

Automated Schedule Generation

"As a store manager, I want the system to auto-generate optimal staff rosters based on forecasts and constraints so that I can save time and ensure adequate coverage."

Description

Automatically generate recommended staff schedules by combining predicted traffic spikes, labor rules, and staff availability to optimize coverage levels. The scheduling engine must balance customer demand, employee constraints, and fairness, delivering shift proposals that reduce understaffing and overstaffing.

Acceptance Criteria

Weekday Morning Spike Scheduling

Given a predicted customer traffic spike between 9:00 AM and 11:00 AM on a Tuesday, when generating the schedule, then the system allocates at least the predicted number of staff plus a 10% buffer for that time slot.

Staff Availability Compliance

Given an employee has entered unavailable time blocks for an upcoming week, when generating the schedule, then no shifts are assigned during those unavailable periods.

Fair Distribution of Weekend Shifts

Given a pool of qualified employees and recurring weekend demand, when generating schedules over a four-week horizon, then no employee is assigned more than one weekend shift difference compared to their peers.

Labor Law and Policy Adherence

Given applicable labor rules (e.g., maximum 8-hour shifts, 40-hour workweek, required rest periods), when generating the schedule, then no assignment violates these constraints.

Zone-Based Coverage Optimization

Given predicted foot-traffic peaks in specific store zones, when generating the schedule, then each peak zone has at least one qualified staff member assigned for the entire duration of the peak.

Staff Availability Management

"As a staff member, I want to submit my availability and role preferences so that the schedule respects my constraints and ensures fair shift distribution."

Description

Provide an intuitive interface for staff to submit availability, role preferences, and work constraints. This requirement ensures the scheduling algorithm respects individual schedules, labor laws, and fair distribution of shifts, integrating seamlessly with the automated scheduling engine.

Acceptance Criteria

Staff Submits Availability

Given a staff member is logged into the mobile interface, when they navigate to the availability calendar and select available and unavailable time slots, then their selections are saved and visible in the system within 5 seconds.

Role Preference Validation

Given a staff member indicates role preferences, when they assign primary and secondary roles, then the system validates and stores these preferences without conflict and displays confirmation to the user.

Labor Law Compliance Check

Given the aggregated availability and preferences, when the scheduling engine processes shifts, then it flags any schedule that violates labor laws (e.g., maximum daily hours, mandatory breaks) and prevents the schedule from being finalized until corrected.

Fair Distribution Audit

Given a completed schedule draft, when the system runs the distribution audit, then no staff member is scheduled for more than 20% over or under the average weekly hours of the team, and any imbalances are reported.

Real-time Availability Conflict Detection

Given updates to staff availability after initial scheduling, when a conflict arises, then the system immediately highlights the conflict in the schedule interface and notifies the scheduler with suggested resolution options.

Real-time Schedule Adjustment

"As a store manager, I want real-time alerts and adjustment recommendations when traffic deviates from predictions so that I can reassign staff and avoid understaffing."

Description

Continuously monitor actual foot traffic against predicted spikes and trigger schedule adjustment recommendations when deviations exceed predefined thresholds. This requirement allows dynamic reallocation of staff or shift swaps to maintain optimal coverage during unforeseen traffic fluctuations.

Acceptance Criteria

Spike Threshold Breach Detection

Given predicted foot traffic for the next 30 minutes and real-time traffic exceeds predicted values by more than 20%, when this deviation is detected, then the system generates a schedule adjustment recommendation within 2 minutes, including a suggested increase of at least one staff member.

Zone-Specific Staffing Recommendation

Given a specific store zone’s real-time foot traffic deviates by more than 15% from its prediction for at least 5 consecutive minutes, when this condition is met, then the system recommends reallocating at least one staff member to that zone and notifies relevant staff within 1 minute.

Shift Swap Execution

Given the system issues a shift swap recommendation to address unforeseen traffic fluctuations, when a staff member accepts the swap within the app, then the system updates the schedule in real-time and sends a confirmation notification to both the requesting and accepting staff members within 1 minute.

Unexpected Traffic Drop Response

Given real-time foot traffic drops by more than 25% below predicted levels for a continuous period of 10 minutes, when this sustained drop is confirmed, then the system recommends reassigning or standing down affected staff and logs the recommendation in the manager dashboard.

Adjustment Approval Process

Given a schedule adjustment recommendation is generated, when the manager reviews and approves the recommendation within the app within 5 minutes, then the system applies the change to the live schedule and sends confirmation notifications to all affected staff within 1 minute.

Staff Notification System

"As a staff member, I want to receive shift notifications and updates on my phone so that I'm always aware of my upcoming schedule."

Description

Implement automated notifications to alert staff of assigned shifts, changes, and upcoming schedules via email or mobile push. Timely communication ensures employees are informed, reducing no-shows and scheduling confusion.

Acceptance Criteria

Initial Shift Assignment Notification

Given a staff member is assigned a new shift When the shift assignment is confirmed and saved in the system Then the staff member receives both an email and a mobile push notification within 2 minutes containing shift details (date, time, and location)

Schedule Change Alert

Given a published shift is modified by a manager When the updated schedule is finalized and republished Then the affected staff member receives an immediate notification highlighting the specific changes (start/end time, location, role) via their preferred channel

Upcoming Shift Reminder

Given a staff member has a scheduled shift in 24 hours When the system clock reaches 24 hours before the shift start Then the system sends an automated reminder notification summarizing shift details and any relevant instructions

Notification Delivery Confirmation

Given a notification is sent to a staff member When the notification is delivered or fails Then the system logs the delivery status in the notification history and updates the manager dashboard with success or failure indicators

Opt-In Notification Preferences Update

Given a staff member updates their notification preferences in their profile When the preferences are saved Then subsequent notifications respect the chosen channels (email, push) and frequency settings, without sending to disabled channels

Schedule Effectiveness Reporting

"As a store manager, I want to view reports on schedule effectiveness and staff utilization so that I can refine staffing strategies over time."

Description

Generate analytics reports comparing scheduled coverage to actual foot traffic, highlighting utilization rates, coverage gaps, and scheduling efficiency. This requirement provides insights for refining forecasting models and scheduling strategies over time.

Acceptance Criteria

End-of-Day Coverage Report Generation

Given a selected date and zone, when the owner requests the end-of-day coverage report, then the system calculates scheduled coverage hours and actual foot traffic volume per hour, displays utilization rates per hour, and flags any hours where utilization rate is below 80%.

Peak Hour Scheduling vs Traffic Comparison

Given predicted peak hours and scheduled staff, when generating peak hour comparison, then the system highlights deviations greater than 10% between scheduled staff count and actual foot traffic spikes.

Real-Time Coverage Gap Identification

Given live foot traffic data and active schedules, when foot traffic exceeds scheduled capacity by 20% in any zone, then the system immediately notifies the manager and suggests reallocating staff from lower-traffic zones.

Weekly Scheduling Efficiency Trend Analysis

Given a date range spanning one week, when the owner views the scheduling efficiency trends, then the system displays utilization rates, average coverage gap duration, and trend lines showing improvements or regressions.

Forecast Refinement Feedback Loop

Given historical schedule effectiveness data and forecasting model inputs, when the owner submits adjustments based on report insights, then the system retrains the forecasting model and validates that prediction error decreases by at least 5%.

DisplayDrift

Offers AI-driven display suggestions tailored to upcoming peak traffic, recommending which products to highlight and where to position promotional fixtures to maximize visibility and sales.

Requirements

Traffic Prediction Engine

"As a shop owner, I want to receive accurate traffic forecasts so that I can prepare and optimize product displays ahead of busy periods."

Description

Develop an AI-driven forecasting module that analyzes historical foot traffic data and external factors (e.g., time of day, day of week, promotions) to predict upcoming peak periods in real time, enabling proactive display planning.

Acceptance Criteria

Weekday Morning Peak Prediction

Given the system has at least 30 days of historical foot traffic data for weekdays, When the current time is between 8:00 and 10:00 AM on a weekday, Then the Traffic Prediction Engine must generate a peak traffic prediction for the next two hours with at least 95% confidence and display it within 5 seconds.

Promotion Period Traffic Surge Prediction

Given an active promotional campaign scheduled in the system, When the promotion start date and time approaches, Then the engine forecasts traffic surges during the promotion window with an error margin of no more than 10%.

Weekend Low Traffic Forecasting

Given historical foot traffic shows lower volume on weekends, When it is Saturday at 3:00 PM, Then the engine predicts traffic volume for the next four hours within ±15% of actuals and updates predictions if deviation exceeds threshold.

External Event Impact Prediction

Given integration with external calendar events (e.g., city festivals), When an event is scheduled within a 1-mile radius, Then the engine adjusts traffic forecasts for that day by factoring in event attendance data and provides an updated prediction within 10 seconds.

Real-time Data Anomaly Handling

Given real-time foot traffic data contains anomalous spikes or dropouts, When anomalies are detected, Then the engine applies smoothing algorithms to filter out anomalies and ensures predictions remain stable, with no single anomaly shifting the forecast by more than 5%.

Display Suggestion Algorithm

"As a shop owner, I want AI-driven recommendations on which products to feature so that I can boost sales during peak traffic."

Description

Implement a machine learning algorithm that processes predicted traffic data and in-store heatmaps to generate prioritized recommendations for which products to highlight, considering product performance and seasonal trends to maximize visibility and sales.

Acceptance Criteria

Peak Traffic Display Recommendation

Given a predicted peak traffic window When the algorithm processes traffic and heatmap data Then it returns the top five product display suggestions ranked by projected visibility uplift

Seasonal Trend Adjustment

Given seasonal sales and trend data When the algorithm generates display recommendations Then at least 80% of suggested products align with current seasonal trends

Real-time Heatmap Integration

Given live in-store heatmap updates When new customer movement data is received Then the algorithm recalculates and updates display suggestions within 2 minutes

Product Performance Prioritization

Given historical product sales performance metrics When generating recommendations Then higher-performing products are prioritized such that at least 70% of suggestions are in the top 25% by sales

Recommendation Delivery Latency

Given completed recommendation processing When delivering suggestions to the shop owner interface Then the end-to-end response time does not exceed 5 seconds

Fixture Placement Optimizer

"As a shop owner, I want advice on where to position promotional fixtures so that they capture the most customer attention without causing congestion."

Description

Create a spatial optimization component that uses store layout maps and traffic flow patterns to suggest optimal positions for promotional fixtures and displays, ensuring maximum exposure to high-traffic zones without disrupting customer movement.

Acceptance Criteria

Initial Store Layout Import

Given a valid store layout map in JSON or PNG format, when the map is uploaded, then the system parses walls, entrances, and shelf coordinates without errors; and the upload completes within 10 seconds.

Peak Traffic Feature Highlight

Given historical foot traffic data for peak hours, when the optimizer runs, then it identifies the top 3 highest traffic zones and suggests fixture positions within those zones; and all suggested positions maintain at least 1 meter distance from any entrance or exit.

Cluster Traffic Zone Adjustment

Given real-time heatmap data updates, when the system recalculates fixture placement, then it adjusts suggestions to cover the top 20% of traffic clusters; and the recalculation finishes within 5 seconds.

Aisle Flow Continuity Check

Given proposed fixture positions, when simulating a customer path from entrance to exit, then no fixture encroaches into a 0.5 meter buffer of the path; and any obstructions are flagged in the report.

Multi-Fixture Optimization

Given a user-defined limit of N fixtures, when optimizing placement, then the system distributes fixtures across at least 3 distinct high-traffic zones; and generates a report confirming each fixture lies within the top 30% of traffic areas.

Product Highlight Ranking

"As a shop owner, I want a ranked list of products to highlight so that I can focus on showcasing my highest-value items effectively."

Description

Build a ranking system that evaluates products based on factors like historical sales, profit margins, and customer interest, producing a dynamic list of top candidates for display suggestions tailored to upcoming traffic peaks.

Acceptance Criteria

Peak Traffic Display Suggestion Generation

Given the system receives predicted peak traffic data When the ranking system is executed Then it must generate a ranked list of the top 10 products within 5 seconds And each list entry includes product ID, name, score, and ranking factors

High-Margin Product Prioritization

Given product profit margin data is available When computing the ranking score Then products with higher profit margins increase their overall score by at least 20% proportionally to other factors And these adjustments are reflected in the final ranking order

Customer Interest Factor Integration

Given real-time customer interest signals (e.g., clicks, dwell time) are fed into the system When updating product scores Then the system weights interest signals at 30% of the total score calculation And the final ranking shifts accordingly within one update cycle

Historical Sales Data Handling

Given three months of historical sales data for each product When calculating historical performance score Then the system normalizes sales figures and applies a decay rate to older data And includes the normalized score in the combined ranking within 5% accuracy of manual calculation

Ranking Algorithm Performance

Given a catalog of 5,000 products When the ranking algorithm runs Then it completes processing within 10 seconds using no more than 4GB of memory And system logs record execution time and resource usage

Real-Time Alert Notifications

"As a shop owner, I want to be alerted just before peak traffic so that I can implement display changes promptly."

Description

Integrate push notifications and in-app alerts that notify shop owners when high-traffic windows are imminent and provide quick-action display suggestions, enabling timely adjustments without constantly monitoring the dashboard.

Acceptance Criteria

Notification Trigger at Peak Traffic Prediction

Given the traffic prediction for a shop’s zone indicates a high-traffic window starting in 10 minutes and reaches 85% confidence, when the model triggers, then the system must send a push notification within 5 seconds to the shop owner's registered device.

Display Suggestion Quick-Action Availability

Given a high-traffic window is predicted, when the notification is delivered, then it must include at least two AI-driven display adjustment options with actionable buttons labeled 'Apply Now' and 'View Details'.

In-App Alert Reception Without App Focus

Given the shop owner has the app running in the background, when a high-traffic alert occurs, then the in-app alert banner must appear immediately upon the app returning to the foreground and the notification badge count must increment by one.

Dismissal and Snooze of Notifications

Given a notification is received, when the shop owner taps 'Snooze' on the notification, then the system must suppress further high-traffic alerts for that location for the next 30 minutes and schedule a follow-up reminder notification at the end of the snooze period.

Historical Notification Log Access

Given any time on the dashboard, when the shop owner navigates to the 'Notifications' log, then the system must display the last 7 days of sent alerts with timestamps, predicted traffic intensity, and any applied display suggestions.

Dashboard Integration and Visualization

"As a shop owner, I want to view AI-driven display suggestions and placement maps in my dashboard so that I can easily understand and act on them."

Description

Design and develop user interface components within the Heatway dashboard to visualize traffic predictions, AI suggestions, and fixture placement maps, providing an intuitive experience for reviewing and applying recommendations.

Acceptance Criteria

Traffic Prediction Visualization Load

Given the dashboard is opened and the 'Predictions' tab is selected When the system retrieves AI-generated traffic predictions Then a heatmap overlay displaying predicted foot traffic intensity appears on the store map And a corresponding legend indicates color gradients with accurate numerical ranges.

AI Suggestion Panel Accessibility

Given the user views the dashboard When AI-driven display suggestions are available Then a collapsible 'Suggestions' panel appears adjacent to the store map And each suggestion includes product name, recommended display location, and expected visibility uplift percentage.

Fixture Placement Overlay Interaction

Given the user activates a fixture suggestion When they hover over a suggested fixture icon on the map Then a tooltip displays fixture details including dimensions, product focus, and suggested orientation And clicking the icon opens a preview mode for placement adjustment.

User Application of Recommendations

Given the user reviews AI suggestions and fixture placements When the user clicks 'Apply Recommendation' Then the current layout updates to reflect the selected suggestions And the dashboard confirms application with a success message and updates analytics to show expected impact.

Responsive Dashboard Layout

Given the dashboard is accessed on desktop, tablet, or mobile When screen resolution changes Then all visualization components (heatmaps, panels, maps) reflow appropriately And no element overlaps or becomes inaccessible at any supported resolution.

PromoPulse

Aligns marketing promotions with forecasted high-traffic periods by recommending ideal timing and placement of offers, helping owners boost conversion rates and capitalize on expected customer surges.

Requirements

Traffic Forecast Engine

"As a shop owner, I want the system to forecast upcoming high-traffic periods so that I can plan promotions when customer presence is highest."

Description

Implement a forecasting engine that analyzes historical foot traffic data and external factors (e.g., time of day, day of week, holidays) to predict high-traffic periods for each store zone. This component should provide accurate hourly and daily forecasts and integrate seamlessly with the PromoPulse recommendation module.

Acceptance Criteria

Forecast Generation for a New Store Zone Setup

Given a store with at least 30 days of historical foot traffic data, when the system initializes forecasts, then it generates and stores hourly and daily forecasts for each defined zone with no missing data points.

Hourly Forecast Update Delivery

Given the latest 24-hour foot traffic data, when the system processes updates at the start of each hour, then it refreshes the hourly forecasts for the next 24 hours within 5 minutes of data arrival.

Holiday Traffic Prediction Accuracy

Given holiday dates in the calendar, when generating forecasts for those days, then predicted traffic volumes deviate by no more than 10% from actual recorded volumes based on previous holiday data.

PromoPulse Integration Response

Given hourly and daily forecast outputs, when queried by the PromoPulse module, then the system responds with recommendations within 2 seconds including forecasted peak periods tagged for promotional campaigns.

Forecast Accuracy Validation

Given a rolling window of one week of forecasted vs. actual traffic, when computing accuracy metrics, then the mean absolute percentage error (MAPE) for hourly forecasts is below 15% and daily forecasts below 10%.

Promotion Timing Recommender

"As a shop owner, I want recommendations on the best times to run promotions so that I can maximize conversion during peak customer flow."

Description

Develop a recommendation engine that uses traffic forecasts to suggest optimal promotion start and end times. The engine should rank timing options by anticipated foot traffic uplift and enable users to view and select recommended slots directly within the PromoPulse interface.

Acceptance Criteria

Generating Ranked Promotion Time Slot Recommendations

Given forecasted foot traffic data for the upcoming week, when the user requests promotion timing recommendations, then the system shall return a minimum of three distinct time slots ranked by anticipated uplift percentage in descending order.

Displaying Recommendations in PromoPulse Interface

Given a set of ranked promotion time slots, when the user opens the PromoPulse Promotion Timing Recommender panel, then the interface shall display each recommended slot’s start and end time, expected traffic uplift percentage, and rank position.

User Selection and Scheduling of Recommended Slot

Given displayed recommendations, when the user selects a specific time slot and confirms the choice, then the system shall schedule the promotion for that slot, update the campaign calendar accordingly, and show a confirmation message with the scheduled times.

Accuracy Verification of Uplift Predictions

Given historical traffic and past promotion performance data, when the recommendation engine is validated against a held-out test set, then the predicted uplift for each time slot shall be within ±5% of the actual measured uplift.

Handling Insufficient Traffic Data for Recommendations

Given a time period with fewer than seven days of traffic data, when the user requests promotion timing recommendations, then the system shall display a warning about low data reliability and provide default peak-hour slots based on the past four weeks of aggregate data.

Location-based Offer Placement

"As a shop owner, I want guidance on where to place promotional displays in my store so that I can capitalize on areas with the highest customer footfall."

Description

Create a module that identifies in-store zones with forecasted high traffic and recommends specific placement locations for promotional materials. Recommendations should account for store layout, product categories, and blind spots to enhance visibility and engagement.

Acceptance Criteria

Identifying High-Traffic Zones Before Deployment

Given forecasted foot traffic data for the upcoming hour; When the store owner requests high-traffic analysis; Then the module must identify and return at least three zones where projected foot traffic exceeds 20% above the store average.

Generating Placement Recommendations

Given identified high-traffic zones and store layout data; When the owner selects a product category; Then the module recommends at least three promotion placements within those zones, prioritizing areas adjacent to the selected product shelves.

Ensuring Category-Relevant Placement Suggestions

Given multiple product categories in the store; When placement recommendations are generated; Then suggestions must align promotion materials with the selected category’s shelf locations and avoid placing conflicting category promotions in the same zone.

Accounting for Physical Store Blind Spots

Given known blind spots with low visibility; When recommending placement locations; Then the module must flag at least two blind spots per zone and suggest materials be positioned to mitigate those blind spots by at least one foot closer to main traffic paths.

Updating Recommendations on Real-Time Forecast Changes

Given a 15-minute update interval; When new foot traffic forecasts are available; Then the system automatically refreshes zone identifications and placement recommendations within 2 minutes, notifying the owner of changes.

Real-time Alert Notifications

"As a shop manager, I want to receive immediate alerts before customer surges so that I can prepare and activate promotions in time."

Description

Implement a notification system that sends real-time alerts to shop owners or managers when a predicted traffic surge is imminent. Alerts should be deliverable via mobile push notifications or email and include suggested actions (e.g., activate current promotion, adjust staffing).

Acceptance Criteria

Surge Alert Delivery

Given a predicted traffic surge within 15 minutes When the system forecasts a surge above the configured threshold Then a push notification or email is sent to the shop owner within 60 seconds

Actionable Suggestion Inclusion

Given a surge alert is generated When the notification is composed Then it includes at least one suggested action (e.g., activate promotion, adjust staffing)

Notification Timing Precision

Given multiple surge predictions occur When alerts are dispatched Then notifications are delivered in chronological order without delay exceeding 60 seconds per alert

Multi-Channel Reliability

Given the owner has both push and email enabled When a surge alert is triggered Then both channels receive identical alert content and arrive within 60 seconds of each other

Alert Acknowledgement Tracking

Given an alert is sent When the owner taps 'Acknowledge' in the push notification or clicks 'Acknowledge' link in the email Then the system logs the acknowledgement timestamp and stops sending repeat reminders for that event

Performance Analytics Dashboard

"As a data-driven shop owner, I want to analyze how my promotions performed against forecasted traffic so that I can refine future marketing strategies."

Description

Build an interactive dashboard that visualizes the relationship between traffic forecasts, promotion timings, and actual customer engagement metrics. The dashboard should allow filtering by date range, promotion type, and store zone to evaluate promotion effectiveness and inform future campaigns.

Acceptance Criteria

Date Range Filter Application

Given the user selects a start and end date for analysis, when the filter is applied, then all visualizations update to display only data within the specified date range.

Promotion Type Selection

Given the user selects one or multiple promotion types from the filter menu, when applied, then the dashboard reflects metrics exclusively for the chosen promotion types.

Store Zone Selection

Given the user chooses a specific store zone on the filter panel, when the filter is applied, then charts and tables update to show traffic and engagement data for that zone only.

Forecast vs Actual Visualization

Given the dashboard loads traffic forecast and actual engagement data, when viewed, then a combined line chart displays both forecasted traffic and actual customer counts over the same time periods with an identifying legend.

Promotion Effectiveness Summary View

Given the dashboard displays filtered data, when the user views the summary section, then it calculates and displays conversion rates for each promotion and ranks promotions by highest to lowest effectiveness.

FlowForesight

Projects dynamic customer pathways on virtual floorplans to visualize how fixture changes influence foot traffic patterns, helping store owners anticipate blind spots and high-engagement zones before implementation.

Requirements

Floorplan Import & Configuration

"As a store owner, I want to import and configure my store’s floorplan so that I can generate accurate virtual layouts for traffic projections."

Description

Allow users to upload store floorplans in common formats (JPEG, PNG, SVG, PDF) and configure scale, orientation, and zone definitions. The system must support auto-detection of walls and display dimension tools for accurate mapping. This feature ensures that pathway projections align with real-world store layouts and provides a foundation for accurate simulations.

Acceptance Criteria

Uploading Floorplan in Supported Formats

Given a user uploads a floorplan file in JPEG, PNG, SVG, or PDF format When the upload completes Then the system accepts the file without error and displays it in the canvas

Configuring Scale and Orientation

Given a floorplan is displayed When the user specifies the real-world width and height Then the system scales the image accordingly and preserves aspect ratio

Defining Custom Zones

Given a scaled floorplan When the user draws a polygon on the canvas Then the system saves a zone with a unique identifier and displays its computed area

Auto-Detection of Walls Accuracy

Given a uploaded floorplan When the auto-detect walls feature runs Then detected walls align within 5 pixels of actual wall positions on at least 80% of straight edges verified by test suite

Using Dimension Tools for Verification

Given dimension tools are enabled When the user measures two points on the floorplan Then the system displays the correct real-world distance within ±1% error margin

Customer Pathway Simulation Engine

"As a store owner, I want the system to simulate customer movement patterns so that I can predict traffic flow across different store zones."

Description

Develop a simulation engine that processes historical foot traffic data and projects dynamic customer pathways on the virtual floorplan. The engine should model customer movement probabilities, dwell times, and pathfinding around obstacles and fixtures, generating realistic trajectories.

Acceptance Criteria

Historical Data Ingestion and Simulation Accuracy

Given the simulation engine has ingested at least 30 days of foot traffic data When a simulation is run Then the projected pathways must match historical movement distributions within a 5% variance

Obstacle-Aware Path Projection

Given a virtual floorplan with defined fixtures and obstacles When customer pathways are simulated Then no trajectory may intersect with obstacles and all paths must navigate around fixtures according to defined pathfinding rules

Dwell Time Modeling

Given historical dwell time statistics for each zone When simulating customer movement Then the engine must assign dwell times per visit that follow the historical mean and standard deviation within a 10% tolerance

Path Variability and Probability Distribution

Given multiple simulation runs over the same dataset When comparing results Then the distribution of unique routes taken by customers must reflect the modeled movement probabilities and show at least 80% route variability coverage

Performance Under Load

Given a virtual floorplan with up to 100 active simulated customers When running a full-day simulation Then the engine must complete processing within 60 seconds without errors and maintain memory usage under 2 GB

Fixture Adjustment & Impact Modeling

"As a store owner, I want to adjust fixture placements and see how those changes affect customer pathways so that I can optimize product placement before physically rearranging my store."

Description

Enable users to add, remove, or reposition fixtures on the virtual floorplan and automatically recalculate projected pathways. The tool must update simulation parameters in real time, reflecting how layout changes influence traffic distribution and potential bottlenecks.

Acceptance Criteria

Add Fixture Recalculation

Given a user adds a new fixture to the virtual floorplan, When the fixture placement is confirmed, Then the projected pathways update within 2 seconds and the heatmap reflects the added fixture’s impact on traffic distribution.

Remove Fixture Recalculation

Given a user removes an existing fixture from the floorplan, When the removal is saved, Then the tool recalculates and displays updated traffic pathways within 2 seconds, showing traffic redistribution around the removed fixture.

Reposition Fixture Impact

Given a user repositions a fixture on the virtual floorplan, When the new location is applied, Then the simulation updates in real time and highlights changes in customer flow and engagement zones.

Real-Time Simulation Update

Given any layout change (add, remove, reposition), When the change is submitted, Then the system refreshes simulation parameters live, maintaining interactive performance under 3 seconds for floorplans up to 100 fixtures.

Bottleneck Identification

Given the updated traffic simulation, When bottlenecks emerge, Then the system identifies and marks areas with traffic density above 80% capacity, alerting the user through visual indicators on the floorplan.

Heatmap Overlay Visualization

"As a store owner, I want to view heatmap overlays of projected traffic so that I can identify high-engagement areas and blind spots in my store."

Description

Provide interactive heatmap overlays on the virtual floorplan to visualize density of projected foot traffic. Users should be able to adjust color scales, opacity, and thresholds to highlight high-engagement zones and blind spots, supporting data-driven layout decisions.

Acceptance Criteria

Adjust Heatmap Color Scale

Given a loaded virtual floorplan with heatmap overlay, when the user selects the 'Color Scale' option and chooses a predefined or custom gradient, then the overlay updates to reflect the chosen color scale within 2 seconds and accurately maps traffic density values to the new colors.

Modify Overlay Opacity

Given a visible heatmap overlay on the virtual floorplan, when the user adjusts the opacity slider to any value between 0% and 100%, then the overlay transparency changes in real-time and underlying floorplan details remain legible at opacities above 20%.

Set Traffic Density Threshold

Given the default threshold setting, when the user inputs a minimum traffic density percentage threshold and applies it, then only zones meeting or exceeding the threshold are highlighted while zones below the threshold are visually de-emphasized, with changes reflected within 2 seconds.

Reset to Default Overlay Settings

Given modified overlay settings for color scale, opacity, and threshold, when the user clicks 'Reset to Default', then all settings revert to the system defaults and the heatmap overlay refreshes automatically to the default visualization.

Export Heatmap Visualization

Given a configured heatmap overlay, when the user selects 'Export' and chooses a file format (PNG or PDF), then the system generates a downloadable file reflecting the current overlay settings within 5 seconds, with a minimum resolution of 300 DPI.

Scenario Comparison Dashboard

"As a store owner, I want to compare different layout scenarios side-by-side so that I can choose the best configuration for maximizing traffic engagement."

Description

Implement a dashboard that allows side-by-side comparison of multiple layout scenarios, displaying key metrics such as projected traffic volume, average dwell time, and zone penetration. The dashboard should support exporting scenario reports for stakeholder review.

Acceptance Criteria

Comparing Two Layout Scenarios

Given two saved layout scenarios exist When the user selects both scenarios for comparison and clicks 'Compare' Then the dashboard displays side-by-side visualizations of projected traffic volume, average dwell time, and zone penetration for each scenario

Multiple Scenario Selection

Given at least three saved layout scenarios exist When the user selects up to four scenarios and clicks 'Compare' Then the dashboard displays each selected scenario as its own column with aligned metrics for easy side-by-side comparison

Scenario Report Export

Given one or more scenarios are displayed in the comparison view When the user clicks the 'Export Report' button Then a downloadable PDF is generated containing scenario names, charts of projected traffic volume, average dwell time, and zone penetration, along with summary tables for stakeholder review

Real-Time Data Refresh in Comparison

Given underlying foot traffic data is updated When the user refreshes the comparison dashboard Then the displayed metrics for all compared scenarios update to reflect the latest data without needing to reselect scenarios

Responsive Comparison Layout

Given the user views the comparison dashboard on various device screen sizes When the screen is resized or viewed on a different device Then the comparison columns, charts, and labels adjust responsively to remain legible and accessible

ScenarioShowdown

Enables side-by-side comparison of multiple layout designs, highlighting projected traffic differentials and sales impact to empower data-driven decisions and optimize store configurations quickly.

Requirements

Import Layout Designs

"As a shop owner, I want to import different store layout designs so that I can compare multiple configurations side-by-side and identify optimal layouts quickly."

Description

Allow users to upload or select multiple store layout designs for side-by-side comparison. The feature supports common file formats such as PNG and SVG as well as in-app generated layout snapshots, enabling seamless integration with existing Heatway tools. Users can assign custom labels to each scenario, maintain version control, and effortlessly manage multiple designs within the interface. This capability ensures that shop owners can prepare diverse configurations and visualize them concurrently without manual setup.

Acceptance Criteria

Uploading Multiple Layout Files

Given a user selects multiple valid layout files (PNG, SVG) and clicks 'Upload', when the upload completes, then each file appears in the design list with correct filename, format icon, and a generated thumbnail within 5 seconds.

Labeling and Versioning Designs

Given a user has an imported or in-app generated design, when the user enters or edits a custom label and version number and saves, then the label and version are persistently displayed alongside the design and searchable via the interface.

Integrating In-App Snapshots

Given a user captures a layout snapshot within the application, when importing the snapshot, then it is added to the design gallery with the same functionality as uploaded files, including thumbnail preview, labeling, and version control.

Handling Unsupported File Formats

Given a user attempts to upload a file with an unsupported format (e.g., PDF, TIFF), when the upload is initiated, then the system rejects the file, displays a clear error message listing supported formats (PNG, SVG), and does not add the file to the design list.

Managing Large Design Collections

Given a user has more than 10 imported layouts, when viewing the design gallery, then the interface provides pagination or infinite scroll, sorts designs by upload date by default, and loads each new page or batch in under 2 seconds.

Scenario Comparison Engine

"As a shop owner, I want to compare traffic and sales projections across multiple layout scenarios in real time so that I can make data-driven configuration decisions swiftly."

Description

Implement a core engine that aligns and renders multiple layout scenarios in a synchronized view, calculating projected foot traffic differentials and sales impacts in real time. The engine should use Heatway’s traffic data models and statistical algorithms to compute comparative metrics, updating instantaneously as users adjust parameters. This backend capability underpins rapid, data-driven decision-making and integrates seamlessly with both the visualization layer and data services.

Acceptance Criteria

Synchronized Layout Alignment

Given two or more layout scenarios are selected in the Scenario Comparison Engine When the engine loads the scenarios Then the engine displays all layouts side-by-side with identical scale, orientation, and coordinate alignment

Real-Time Traffic Differential Calculation

Given baseline and comparison traffic datasets are available When a scenario parameter (date range, visitor segmentation) is modified Then the engine updates and displays projected foot traffic differentials and percentage changes within 1 second

Interactive Parameter Adjustment

Given the user adjusts traffic model parameters (e.g., average dwell time, conversion rate) When the user confirms the parameter change Then the engine recalculates comparative metrics automatically without a full page reload and displays a processing indicator if calculations exceed 200ms

Visualization Layer Integration

Given the Scenario Comparison Engine produces comparison metrics When the frontend visualization component requests data via the API endpoint Then the engine returns a JSON payload containing scenario IDs, layout coordinates, and metrics conforming to the agreed schema

Data Service Resilience

Given the traffic data model service is unresponsive or returns an error When the engine attempts to fetch required datasets Then the engine retries the request up to 3 times, logs each failure, and after the final attempt surfaces a user-friendly error message

Visualize Traffic Differentials

"As a shop owner, I want to see heatmap comparisons that highlight traffic differences so that I can pinpoint areas of high and low customer engagement across layouts."

Description

Provide clear, color-coded heatmap overlays that highlight foot traffic variations between selected layout scenarios. The visualization should support multiple layers, opacity control, and tooltips showing percentage differences at specific points. Interactive legends and thresholds enable users to focus on significant traffic shifts, facilitating rapid identification of high- and low-engagement areas across scenarios.

Acceptance Criteria

Side-by-Side Heatmap Overlay Display

Given two layout scenarios selected, when the user enables overlay mode, then the system displays both heatmaps concurrently with each scenario using a distinct, color-coded layer.

Opacity Adjustment for Layer Comparison

Given an overlay of multiple heatmaps, when the user drags the opacity slider for any layer, then the corresponding heatmap’s transparency updates in real time reflecting the slider value between 0% and 100%.

Tooltip Percentage Difference Display

Given the heatmap overlay is active, when the user hovers over any point on the map, then a tooltip appears showing the exact percentage difference in foot traffic between the selected scenarios at that specific location.

Interactive Legend Filtering by Thresholds

Given predefined traffic variation thresholds (e.g., ±10%, ±25%), when the user selects a threshold in the interactive legend, then the heatmap highlights only areas where the traffic differential meets or exceeds the chosen threshold while dimming other areas.

Multiple Layer Toggle Management

Given more than two layout scenarios loaded, when the user toggles visibility for individual layers via checkboxes in the legend, then only the checked scenarios’ heatmap layers are displayed on the map.

Project Sales Impact

"As a shop owner, I want to view projected sales changes for different layouts so that I can optimize product placements to increase revenue."

Description

Calculate and display projected sales changes based on traffic differentials and historical conversion rates for each scenario. The feature should generate estimated revenue uplifts or declines for specific zones, summarize totals, and provide visual indicators of profitability shifts. Integration with historical sales data ensures realistic projections, aiding in strategic decisions for product placement and promotional planning.

Acceptance Criteria

Layout Comparison Sales Projection

Given two layout scenarios with defined traffic differentials and historical conversion rates, when the user initiates the sales impact calculation, then the system displays the projected sales change for each scenario in both absolute currency value and percentage.

Zone-Level Revenue Forecast

Given a selected store zone with identified traffic increase or decrease, when the projection is executed, then the system calculates and displays the estimated revenue uplift or decline for that zone based on its historical conversion rate.

Aggregate Store Sales Summary

Given multiple zones across the store, when the projection completes, then the system summarizes and presents the total projected store-wide revenue change, clearly distinguishing total uplifts and declines.

Visual Profitability Indicators

Given the projected revenue outcomes for each zone, when the results are shown, then the system provides visual indicators (green for uplift, red for decline) on the heatmap overlay or bar chart for immediate readability.

Historical Data-Driven Estimation

Given integration with historical sales data over a user-selected date range, when projections are generated, then the estimated sales values must reflect conversion rates within ±5% of the historical averages for accuracy validation.

Filter and Select Metrics

"As a shop owner, I want to filter scenarios by time and product categories so that I can compare layouts under specific customer behavior patterns."

Description

Enable users to apply filters and select specific metrics such as time of day, day of week, traffic thresholds, and product categories for scenario comparisons. The interface should allow dynamic adjustment of parameters, instantly reflecting changes in the comparison view. This ensures customization of analysis to match business objectives and focus on relevant customer behavior segments.

Acceptance Criteria

Adjust Time of Day Filter

- The time slider control is present and adjustable between 00:00 and 23:59 - When the user updates the start and end times, the comparison view refreshes within 200ms - Only data points within the selected time range are displayed; others are excluded - The visual heatmap and metrics reflect the filtered time range accurately

Select Day of Week Metric

- A dropdown menu lists all days of the week - When the user selects one or more days, the comparison view updates within 200ms - Only traffic data from the selected days is included in the analysis - Unselected days are excluded from metrics and visualizations

Set Traffic Thresholds

- Input fields allow the user to define minimum and maximum traffic thresholds - Threshold inputs are validated to be numeric and within the actual data range - Applying thresholds filters out all data points outside the specified range - An error message displays if thresholds are invalid or min≥max

Choose Product Category

- A multi-select list shows all available product categories - Users can select and deselect categories freely - Only data for selected categories appears in the comparison view - Deselected categories’ data is excluded from metrics and visualizations

Combined Filter Application

- The interface allows simultaneous application of time, day, threshold, and category filters - When all filters are applied, the comparison view updates within 300ms - A summary label displays all active filters - Using the clear filters action resets the view to show the full dataset

Export and Share Reports

"As a shop owner, I want to export comparison reports so that I can share insights with my team and refer to them in future planning."

Description

Allow users to export side-by-side comparison results and visualizations as PDF or interactive web links. Reports should include heatmaps, traffic differential statistics, sales impact summaries, and user-defined notes. Integration with email and cloud storage services enables seamless sharing with stakeholders and easy inclusion in strategic presentations.

Acceptance Criteria

Export Side-by-Side Comparison as PDF Report

Given the user has selected two layout designs for comparison When the user opts to export as PDF Then the system generates a PDF within 120 seconds containing side-by-side heatmaps, traffic differential statistics, sales impact summaries, and all user-defined notes formatted correctly, and prompts the user to download the file

Generate Interactive Web Link for Report Sharing

Given the user has chosen to share the report as an interactive link When the user confirms the action Then the system generates a unique URL valid for 30 days that provides an interactive view of heatmaps, traffic differentials, sales impact summaries, and user notes, and displays the link with copy-to-clipboard functionality

Email Report Directly to Stakeholders

Given the user has entered valid stakeholder email addresses When the user sends the report Then the system emails a PDF attachment and the interactive link to all addresses within 60 seconds and displays a success confirmation message

Save Report to Cloud Storage Integration

Given the user has connected their Google Drive or Dropbox account When the user selects 'Save to Cloud' Then the system uploads the PDF report and a .link file containing the interactive report URL to the specified cloud folder, enforces overwrite protection, and displays a success notification

Include and Persist User-Defined Notes

Given the user has added custom notes in the report editor When any export or share action is performed Then both the generated PDF and the interactive report include the notes in the designated section, preserving text formatting and any embedded hyperlinks

AI Placement Coach

Leverages machine learning to recommend ideal product and fixture positions based on simulated traffic data, ensuring maximum exposure for key items and streamlining the planning process.

Requirements

Simulated Traffic Modeling

"As an independent shop owner, I want to simulate foot traffic patterns so that I can test different store layouts virtually and avoid costly physical rearrangements."

Description

Develop a backend engine that generates realistic in-store foot traffic simulations based on historical data and customizable parameters. This engine will feed simulated movement patterns into the AI Placement Coach to evaluate layout scenarios before physical implementation, reducing trial-and-error on the shop floor.

Acceptance Criteria

Historical Data Accuracy Verification

Given the engine ingests historical foot traffic data When data ingestion completes Then system reports ≥95% data completeness with no missing timestamps within a 24-hour period

Custom Parameter Simulation Execution

Given shop owner inputs custom parameters for peak hours and customer count When simulation runs Then engine generates a traffic heatmap within 10 seconds reflecting parameters within ±5% accuracy

Integration with AI Placement Coach

Given simulated traffic output When consumed by AI Placement Coach Then layout recommendations are generated within 5 seconds with ≥80% relevance score

Scalability under High Load

Given 1 million simulated customer paths When running parallel simulations Then backend maintains response times <2 minutes and error rate <1%

Simulation Data Export Capability

Given simulation completion When user requests export Then engine provides CSV and JSON files containing timestamped foot traffic coordinates and all input parameter metadata

Adaptive Placement Recommendations

"As a shop owner, I want personalized placement suggestions so that I can maximize exposure of key products and increase sales without manual guesswork."

Description

Implement a machine learning algorithm that analyzes both simulated and real-time traffic heatmaps to recommend optimal product and fixture placements. Recommendations should prioritize high-value items, factor in seasonal variations, and allow tuning based on shop owner preferences for product promotion.

Acceptance Criteria

Real-time and Simulated Data Analysis

Given both simulated and real-time heatmaps are available, When the algorithm generates placement recommendations, Then it processes both data sources concurrently and returns a unified recommendation list within 60 seconds, ensuring at least 75% of recommended positions correspond to observed high-traffic zones.

Seasonal Variation Adjustment

Given the seasonal context is set (e.g., "Holiday Sales"), When generating placement recommendations, Then seasonal modifiers are applied to item weighting factors and recommendations differ by at least 10% in priority ranking compared to non-seasonal conditions.

High-Value Item Prioritization

Given a list of items tagged as high-value by the shop owner, When generating placement recommendations, Then at least 4 of the top 5 recommended fixture positions are assigned to those high-value items.

Owner Preference Tuning

Given the shop owner adjusts the promotion priority slider for a specific product by 20%, When recommendations are regenerated, Then the relative ranking of that product increases by at least 20% compared to its position in the default recommendation within 30 seconds.

Scalability for Large Inventory

Given a product catalog of over 500 SKUs, When the algorithm processes heatmap data and item metadata, Then it completes recommendation generation within 2 minutes with a processing error rate below 1%.

Visual Layout Editor

"As a retail manager, I want a visual layout tool so that I can easily adjust product placements and immediately see predicted customer flow changes."

Description

Create an interactive interface where shop owners can drag and drop product icons and fixtures onto a floor plan. The editor integrates with the AI Placement Coach to display real-time impact visualizations of placement changes on simulated traffic heatmaps.

Acceptance Criteria

Dragging and Dropping a Product Icon onto the Floor Plan

Given a shop owner has a floor plan loaded, when they drag a product icon from the toolbar onto the plan and release it, then the icon snaps to grid, displays its name and SKU, and is saved at the dropped coordinates.

Real-Time Heatmap Update after Placement Change

Given a product or fixture is moved on the floor plan, when the drag operation completes, then the overlay heatmap recalculates within two seconds and displays the new traffic intensity for the affected area.

Undoing and Redoing a Placement Action

Given a placement action has been performed, when the shop owner clicks 'Undo', then the last change is reverted on the layout and heatmap, and when 'Redo' is clicked, the change is reapplied exactly as before.

Integrating AI Placement Recommendations

Given the AI Placement Coach has generated recommendations, when the shop owner selects 'Apply Recommendation' for a suggested item, then the item moves to the recommended position and the simulated heatmap updates to reflect predicted traffic changes.

Saving and Exporting Layout Configurations

Given a layout is finalized, when the shop owner clicks 'Save' or 'Export', then the system stores the configuration with a timestamp and allows download of a PDF and JSON file containing layout and heatmap data.

Placement Performance Analytics

"As a store owner, I want analytics on placement performance so that I can measure the impact of layout changes and justify decisions with data."

Description

Build a dashboard that tracks key performance indicators (KPIs) for recommended placements, including conversion uplift, dwell time changes, and heatmap shifts. Provide comparative metrics between baseline layouts and AI-suggested layouts to validate recommendation effectiveness.

Acceptance Criteria

Comparative Conversion Rate Analysis

Given baseline and AI-suggested layout conversion data is loaded When the user selects the “Conversion Uplift” metric Then the dashboard displays the percentage change in conversion rate between layouts with time-period filters

Dwell Time Monitoring

Given heatmap-derived dwell time data for both baseline and AI layouts When the user selects the “Dwell Time Changes” metric Then the dashboard shows average dwell time per zone and flags zones with a ±10% change

Heatmap Shift Visualization

Given heatmaps for baseline and AI suggested layouts When the user toggles between layouts Then the system overlays the heatmaps and highlights zones with ≥15% foot traffic intensity shifts

Real-time KPI Update

Given live customer movement data streaming When new data arrives Then the dashboard updates conversion, dwell time, and heatmap shift metrics within 5 minutes without manual refresh

Historical Performance Export

Given comparative layout metrics are visible When the user clicks “Export” Then the system generates a CSV report containing timestamps and values for conversion uplift, dwell time changes, and heatmap shift percentages

Report Export Functionality

"As a regional manager, I want to export placement reports so that I can share insights with my team and implement best practices across multiple locations."

Description

Enable shop owners to export layout plans, recommendation summaries, and performance analytics into shareable PDF and CSV formats. Exports should include annotated floor plans, heatmap snapshots, and KPI tables for easy stakeholder review.

Acceptance Criteria

Generate PDF Export with Annotated Floor Plan

Given a finalized layout plan and annotations are visible on the interface When the shop owner clicks 'Export PDF' Then a downloadable PDF is generated containing the complete layout plan with all annotations and proper formatting

Generate CSV Export of Key Performance Indicators

Given the shop owner has accessed the performance analytics dashboard When the shop owner clicks 'Export CSV' Then a CSV file is downloaded containing a complete KPI table with correct column headers, accurate data rows, and comma-separated values

Include Heatmap Snapshots in PDF Export

Given heatmap visualizations for selected timeframes are displayed When exporting to PDF Then the exported PDF includes high-resolution heatmap snapshots with legends and timestamps for each included snapshot

Export Recommendation Summaries

Given the AI Placement Coach has generated placement recommendations When exporting reports in PDF or CSV Then the export includes a summary section listing each product, recommended position, and associated confidence score

Validate Export File Integrity

Given an export (PDF or CSV) has completed When the shop owner opens the exported file Then the file opens without errors, all images and tables render correctly, and the file metadata reflects the correct export date and user ID

Ensure Export Performance within Threshold

Given a report with up to 5 floor plans, heatmap snapshots, and recommendation summaries When initiating an export Then the system completes the export process and provides the download link within 10 seconds

3D StoreView

Transforms flat floorplans into immersive 3D walkthroughs, allowing users to virtually navigate redesigned spaces and assess visibility, flow comfort, and customer experience from every angle.

Requirements

Floorplan Import and Conversion

"As a shop owner, I want to upload my existing store floorplans so that the system can generate an accurate 3D model without manual 3D design work."

Description

Enable users to upload 2D floorplans in common formats (e.g., JPEG, PNG, PDF) and automatically convert them into accurate 3D wireframe models. The conversion process should handle scale, dimensions, and basic structural elements (walls, doors, windows) to generate a reliable foundation for the 3D environment.

Acceptance Criteria

High-resolution JPEG Floorplan Upload

Given a 2D floorplan in JPEG format under 10MB When the user selects the file and initiates upload Then the system validates file type and size and begins conversion within 2 seconds And upon completion, displays a 3D wireframe representation preserving the original aspect ratio within 30 seconds

Multi-page PDF Floorplan Import

Given a multi-page PDF containing floorplan diagrams When the user uploads the PDF file Then the system parses all pages, allows the user to select which pages to convert, and successfully generates separate 3D wireframes for each selected page

Floorplan Scale and Dimension Validation

Given a floorplan image containing embedded scale markers or dimension annotations When the conversion process is initiated Then the system reads the scale metadata or prompts the user for scale input And applies real-world measurements correctly within a 5% accuracy tolerance in the resulting 3D model

Recognition of Basic Structural Elements

Given the uploaded floorplan includes walls, doors, and windows represented by lines or annotations When the conversion algorithm processes the image Then it correctly identifies at least 95% of walls, doors, and windows And accurately represents each identified element in the generated 3D wireframe

Unsupported Format and Error Handling

Given a user attempts to upload an unsupported or corrupted file format When the upload is initiated Then the system rejects the file and displays a clear error message “Unsupported format” within 2 seconds And logs the error event for user troubleshooting

3D Environment Generation

"As a store manager, I want a realistic 3D version of my store layout so that I can virtually inspect fixture placement and layout flow."

Description

Transform the converted wireframe into a fully rendered 3D environment with realistic textures, materials, and basic lighting. The environment should reflect store fixtures, shelving units, and walls, providing an immersive basis for walkthroughs and assessments.

Acceptance Criteria

Initial 3D Environment Rendering Preview

Given a converted wireframe floorplan uploaded by the user, when the 3D Environment Generation is triggered, then the system produces a fully rendered 3D environment showing walls, shelving, and fixtures within 30 seconds.

Material and Texture Application Accuracy

Given predefined material mappings for store elements, when textures are applied in the environment, then each element displays the correct material with a minimum resolution of 1024x1024 pixels and without visible seams or stretching artifacts.

Basic Lighting Implementation

Given default lighting configuration, when the environment loads, then ambient and directional lights should illuminate all surfaces, producing realistic shadows and no areas of complete darkness.

Geometric Alignment Verification

Given the original dimensions from the wireframe, when the 3D environment is generated, then all fixtures and walls align within a tolerance of 1 cm of their specified positions.

Rendering Performance Under Load

Given a complex store layout with at least 50 distinct assets, when viewing the generated environment on a standard device, then the system maintains a minimum of 30 frames per second during navigation.

Navigation and Interaction Controls

"As a retailer, I want to navigate the 3D store environment like a customer so that I can evaluate sight lines, flow comfort, and product visibility from different perspectives."

Description

Provide intuitive 3D navigation controls (walk, rotate, zoom, pan) and viewpoint presets (eye-level, bird’s-eye) to allow users to explore the virtual store. Include collision detection to prevent walking through solid objects and smooth camera transitions for a comfortable user experience.

Acceptance Criteria

User Navigates Through Store Using Walk Mode

Given the 3D model is loaded, when the user engages the walk control, then pressing forward/backward moves the camera at a controlled speed of 1.5 m/s, and releasing the control stops movement immediately without drift.

User Rotates Viewpoint to Inspect Shelves

Given the 3D StoreView is active, when the user uses rotate controls, then the viewpoint rotates horizontally or vertically at a maximum speed of 90°/s, and rotation stops precisely when input ends.

User Zooms and Pans to Focus on Store Sections

Given the scene is displayed, when the user scrolls or pinches, then the camera zooms in/out between 0.5× and 3× the default FOV, and when dragging, the camera pans within store boundaries without exceeding floorplan limits.

User Selects Viewpoint Presets

Given viewpoint presets are available, when the user selects 'Eye-Level' or 'Bird’s-Eye', then the camera moves to the predefined height (1.6 m or 5 m) and orientation within 1 s without abrupt jumps.

User Encounters Physical Boundaries within the Store

Given collision detection is enabled, when the camera approaches a solid object, then further movement into the object is prevented by a 0.2 m buffer, ensuring no clipping or penetration.

User Experiences Smooth Camera Transitions

When switching between navigation modes or presets, then camera movements interpolate over 0.5 s using an ease-in-out curve, and the frame rate remains ≥30 fps throughout.

Heatmap Overlay Integration

"As a shop owner, I want to see foot traffic heatmaps within the 3D model so that I can identify high-visibility zones and optimize product placement accordingly."

Description

Overlay real-time foot traffic heatmap data onto the 3D walkthrough, visualizing customer movement patterns on store surfaces. The heatmap should be toggleable and adjustable in intensity thresholds, enabling users to correlate high-traffic areas with product placements.

Acceptance Criteria

Toggle Heatmap Visibility During Walkthrough

Given the user has the 3D walkthrough open, when the user toggles the heatmap visibility control, then the heatmap overlay appears or disappears on all visible store surfaces within 2 seconds.

Adjust Heatmap Intensity Threshold

Given the heatmap overlay is visible, when the user adjusts the intensity threshold slider, then the heatmap colors update in real time to reflect the new threshold with at least three distinct intensity levels.

Real-Time Heatmap Update During Navigation

Given the heatmap data stream is active, when new foot traffic data is received, then the overlay updates to reflect the latest data within 5 seconds without requiring a manual refresh.

Overlay Accuracy on 3D Surfaces

Given a known high-traffic zone on the floorplan, when displayed in the 3D walkthrough, then the heatmap hotspots align within 10 centimeters of their mapped positions on the 3D model’s surfaces.

Performance Under Large Data Loads

Given the store has over 10,000 foot traffic data points, when the heatmap is rendered, then the frame rate remains at or above 30 FPS and the heatmap loads within 3 seconds.

Performance and Cross-Platform Optimization

"As a business user, I want the 3D walkthrough to perform smoothly on my device so that I can explore the store model without lag or glitches."

Description

Ensure smooth rendering and interaction of the 3D walkthrough on a range of devices (smartphones, tablets, desktops) by implementing level-of-detail techniques, asset streaming, and GPU acceleration. Target a consistent 30+ FPS performance on mid-tier hardware.

Acceptance Criteria

Mid-Tier Smartphone Rendering

Given a mid-tier smartphone with GPU equivalent to A12 Bionic, when the user navigates continuously through the 3D StoreView for 60 seconds, then the average frame rate remains at or above 30 FPS.

Tablet Asset Streaming

When loading a 3D StoreView scene containing 50 MB of assets on a Snapdragon 660–class tablet, then initial low-resolution models render within 2 seconds and high-resolution assets stream progressively without frame rate dropping below 25 FPS.

Desktop GPU Acceleration Enablement

Given a desktop environment with a supported GPU, when the 3D walkthrough initializes, then GPU acceleration is automatically enabled, verified by GPU utilization over 20% and CPU utilization under 50% during navigation.

LOD Transitions

When the user approaches or moves away from major scene objects during the walkthrough, then level-of-detail transitions occur seamlessly without visible popping and the frame rate stays at or above 30 FPS.

Cross-Platform Feature Consistency

When launching 3D StoreView on smartphone, tablet, and desktop, then core features (camera controls, navigation, annotation overlays) are accessible within two taps or clicks and operate without functional errors.

SplitScreen Analyzer

Displays original and proposed layouts simultaneously with synchronized heatmap overlays, making it easy to spot improvements, quantify traffic shifts, and validate design hypotheses in real time.

Requirements

Dual Heatmap Synchronization

"As an independent shop owner, I want to view original and proposed layout heatmaps side by side with synchronized interactions so that I can instantly compare traffic patterns and ensure accurate analysis."

Description

Enable simultaneous display of original and proposed layout heatmaps with synchronized interactions, ensuring any pan, zoom, or time-slider adjustments in one view are immediately reflected in the other. This functionality integrates with the existing mapping and rendering engine, leveraging real-time event listeners and data synchronization logic to provide low-latency updates. The dual synchronization offers a seamless comparison experience, allowing users to pinpoint layout performance differences and validate design hypotheses through direct visual correlation.

Acceptance Criteria

Pan Synchronization

Given two heatmap views are displayed side-by-side When the user pans the original layout heatmap Then the proposed layout heatmap must pan by the identical offset within 100ms

Zoom Synchronization

Given the user applies a zoom action on the proposed layout heatmap When zooming in or out Then the original layout heatmap must mirror the zoom level and focal point within 100ms

Time-Slider Synchronization

Given the user adjusts the time-slider on either heatmap When the time range is changed Then the other heatmap’s data and visual overlay must update to the same time range instantly

Initial Load State Synchronization

Given the SplitScreen Analyzer feature is opened When both heatmaps are initially rendered Then both views must load with the same center coordinates, zoom level, and default time range

Performance and Latency

Given interactive events occur (pan, zoom, time-slider) When multiple rapid interactions are made Then synchronization updates must complete within 200ms per event without UI freezing

Real-time Overlay Rendering

"As a store manager, I want heatmap overlays to update in real time so that I can monitor customer movement changes immediately and make timely layout adjustments."

Description

Continuously capture customer movement data and render heatmap overlays for both the original and proposed layouts in real time. This requirement involves integrating a streaming data API, optimizing the data processing pipeline for minimal latency, and ensuring smooth rendering at high frame rates. By maintaining up-to-date overlays without manual refresh, users can monitor live foot traffic shifts and respond promptly to emerging patterns.

Acceptance Criteria

Initial Overlay Load

Given the user opens the SplitScreen Analyzer, When the Real-time Overlay Rendering feature initializes, Then both original and proposed layout heatmap overlays display within 2 seconds using the latest customer movement data.

Peak Customer Flow Rendering

Given the customer movement data stream spikes to over 100 events per second, When the system processes incoming data, Then the heatmap overlays update continuously without dropping below 24 frames per second.

Network Disruption Recovery

Given a temporary network interruption occurs, When the connection is restored, Then the overlays resume real-time updates and catch up to live data within 5 seconds without manual refresh.

Layout Toggle Consistency

Given the user toggles between original and proposed layout views, When the toggle action is performed, Then the heatmap overlays transition seamlessly without misalignment or interruption of the data stream.

Frame Rate Stability

Given the application runs on a standard smartphone configuration, When the Real-time Overlay Rendering operates continuously for 10 minutes, Then it maintains a minimum of 30 frames per second without crashes or memory leaks.

Traffic Shift Analytics

"As a shop owner, I want to see quantitative metrics on foot traffic shifts so that I can objectively evaluate the effectiveness of proposed layout changes."

Description

Calculate and display quantitative metrics indicating foot traffic shifts between the original and proposed layouts, including zone-based percentage change, peak area comparisons, and visual indicators for increases or decreases. The analytics module integrates with the split-screen views and database, allowing on-demand metric computation and export. These insights provide objective measures of layout performance, helping users make data-driven decisions.

Acceptance Criteria

Zone-Based Traffic Percentage Change Calculation

Given an original and proposed layout with defined zones, when the user requests zone percentage changes, then the system calculates and displays the percentage difference for each zone rounded to one decimal place.

Peak Area Traffic Comparison Display

Given heatmap data for both layouts, when the user views peak area analytics, then the system identifies the top three highest-traffic zones in each layout and displays their counts side by side with percentage differences.

On-Demand Analytics Computation

Given stored layout and heatmap data, when the user clicks the ‘Compute Traffic Shift’ button, then all relevant metrics are generated within five seconds and rendered without requiring a page reload.

Visual Indicators for Traffic Shifts

Given calculated zone metrics, when increases or decreases are detected, then green upward arrows appear for positive shifts and red downward arrows for negative shifts next to each zone’s percentage change.

Export Traffic Shift Report

Given computed traffic shift metrics, when the user selects ‘Export Report’, then a CSV file named ‘Traffic_Shift_Report_{timestamp}.csv’ is downloaded containing zone IDs, original counts, proposed counts, and percentage changes.

Hypothesis Validation Dashboard

"As a UX researcher, I want to set design hypotheses and track their validation so that I can systematically test layout changes and document results."

Description

Offer a dedicated dashboard for defining, tracking, and validating design hypotheses. Users can mark specific zones, set evaluation periods, and view validation results through graphical summaries and traffic metrics. This dashboard integrates with both the synchronization engine and analytics layer, enabling structured experiment management and clear documentation of layout change outcomes.

Acceptance Criteria

Hypothesis Creation Scenario

Given a user on the Hypothesis Validation Dashboard When the user enters a hypothesis title, selects map zones, sets start and end dates, and defines validation metrics Then the hypothesis is saved and listed in the dashboard with status “Pending”

Hypothesis Tracking Scenario

Given an active hypothesis within its evaluation period When the dashboard loads Then real-time foot traffic data for each defined zone refreshes at least every five minutes and displays current versus baseline values

Validation Results Display Scenario

Given an evaluation period has ended When the user opens the hypothesis details Then the dashboard presents graphical summaries comparing pre- and post-change metrics and displays a pass/fail indication based on predefined success criteria

Experiment Documentation Export Scenario

Given a completed hypothesis validation When the user clicks “Export Report” Then a downloadable PDF is generated containing hypothesis details, zone maps, metrics charts, and validation outcome

Layout Sync Integration Scenario

Given a hypothesis references zones updated via SplitScreen Analyzer When the user applies layout changes Then the synchronization engine triggers new data collection for those zones and dashboard metrics reflect updated traffic for the next evaluation cycle

Comparison Report Export

"As a marketing analyst, I want to export comparison reports so that I can share insights with stakeholders and archive layout tests."

Description

Provide functionality to export side-by-side heatmap comparisons, traffic shift metrics, and user annotations into shareable PDF or high-resolution image formats. Users can select specific date ranges, layout versions, and include custom notes or highlights. This feature integrates with the reporting module and ensures consistent branding and formatting for stakeholder presentations.

Acceptance Criteria

Date Range and Layout Version Selection

Given the user opens the Comparison Report Export dialog When the user selects a valid start date, end date, and layout version Then the Export button is enabled And the selected parameters are correctly displayed in the export summary And if the end date precedes the start date, an inline validation error appears and Export remains disabled

PDF Export with User Annotations

Given the user adds custom notes and highlights in the export dialog When the user chooses PDF as the export format and clicks Export Then a PDF file is generated containing side-by-side heatmaps, traffic shift metrics, and all annotations And the file name follows the pattern "ComparisonReport_{startDate}_{endDate}.pdf"

High-Resolution Image Export

Given the user selects Image (PNG) as the export format When the user clicks Export Then a high-resolution PNG file is generated with a minimum resolution of 300 DPI And the image includes side-by-side heatmaps, traffic shift metrics, and user annotations

Consistent Branding and Formatting

Given the user exports a report in any supported format When the report is generated Then the company logo, colors, fonts, and header/footer layouts match the established branding guidelines And margins and page orientation adhere to predefined template settings

Successful Report Generation and Download

Given the user completes all required selections and clicks Export When the export process finishes Then the system displays a success notification with a Download link And clicking the Download link saves the file to the user’s default download location

Interactive Split View Controls

"As a user, I want to adjust the split view orientation and overlay opacity so that I can focus on specific areas and better visualize comparisons."

Description

Implement user interface controls for adjusting the split view, including toggling orientation (vertical/horizontal), controlling overlay opacity levels, and synchronized zoom/pan functionality. Incorporate a time-range slider to animate traffic patterns over a chosen period. These controls should be intuitive, responsive, and seamlessly integrated into the SplitScreen Analyzer UI for enhanced user interaction.

Acceptance Criteria

Toggle Orientation Control

Given the user is in the SplitScreen Analyzer, When the user clicks the orientation toggle button, Then the display switches between vertical and horizontal split layouts within 300ms, and both original and proposed heatmap overlays remain synchronized.

Overlay Opacity Adjustment

Given the user accesses the opacity slider, When the user drags the slider to any value between 0% and 100%, Then the heatmap overlays update in real time to reflect the selected opacity without performance lag.

Synchronized Zoom and Pan Interaction

Given the user performs a zoom or pan gesture on either the original or proposed layout, When the action completes, Then the other layout mirrors the zoom/pan operation within 200ms, maintaining alignment of heatmap data.

Time-Range Slider Animation

Given the user opens the time-range slider, When the user selects a start and end time and clicks play, Then the heatmap overlays animate through the selected period at a smooth frame rate of at least 24 FPS and allow pause, rewind, and fast-forward controls.

UI Responsiveness on Different Screen Sizes

Given the SplitScreen Analyzer is loaded on devices with viewports ranging from 320px to 1920px width, When the user interacts with any control, Then all UI elements remain fully visible, functional, and responsive without overlapping or truncation.

Conversion Correlator

Automatically links foot traffic heatmaps with POS transactions to identify which zones deliver the highest conversion rates, enabling shop owners to focus resources on proven high-performing areas for maximum ROI.

Requirements

Data Ingestion Module

"As a data engineer, I want Heatway to automatically gather and store foot traffic and transaction data so that I can ensure accurate and timely inputs for conversion analysis."

Description

Develop a robust ingestion pipeline that collects real-time foot traffic heatmap data from smartphones and POS transaction logs, normalizes and stores them in a unified data warehouse. This module should ensure data consistency, handle data bursts, support incremental updates, and provide error handling and retry mechanisms. It should integrate with existing Heatway services and be scalable to support multiple stores simultaneously.

Acceptance Criteria

Ingesting Real-Time Heatmap Data from Smartphones

Given the pipeline is subscribed to the smartphone SDK event stream When a heatmap event is emitted Then the event must be persisted to the staging database within 1 second without loss Given 100 events per second are generated during peak traffic When events exceed the baseline rate Then the system must buffer and enqueue all events without dropping any

Integrating Incremental POS Transaction Updates

Given new POS transactions are recorded When the ingestion module polls or receives a transaction batch Then each transaction must be appended incrementally to the transaction log table with correct timestamps and store identifiers Given a gap in transaction sequence numbers When the module detects missing entries Then it must re-request the missing batch and reconcile before further processing

Normalizing and Storing Unified Data Sets

Given raw heatmap and POS data in staging When the normalization job runs Then all records must conform to the unified schema and be ingested into the warehouse’s unified tables Given inconsistent field formats in incoming payloads When normalization encounters a mismatch Then records must be flagged for review and rejected entries logged separately

Handling Data Bursts During Peak Hours

Given a sudden spike in incoming events during peak shopping hours When event rate doubles above average Then the pipeline must auto-scale horizontally to process events within SLA without queuing delays Given sustained high throughput for 5 minutes When resource limits are reached Then the system must trigger alerting and continue processing at degraded but acceptable performance levels

Automatic Error Handling and Retry on Failure

Given a network or downstream database error When an ingestion attempt fails Then the system must retry up to 3 times with exponential backoff before flagging an error Given a persistent failure after retries When the retry threshold is exceeded Then the event must be moved to a dead-letter queue and a notification sent to the operations team

Scaling to Support Multiple Stores Simultaneously

Given data streams from 10 stores When all stores send simultaneous heatmap events Then the ingestion module must maintain per-store isolation and process events under 2 seconds latency each Given the onboarding of an 11th store When capacity thresholds are exceeded Then the system must auto-provision additional resources and rebalance load without manual intervention

POS Transaction Integration

"As a shop owner, I want Heatway to connect with my existing POS system so that my transaction data automatically feeds into the conversion correlator without manual uploads."

Description

Implement secure and flexible connectors to integrate popular POS systems (e.g., Square, Shopify POS, Clover) with Heatway’s backend. This requirement involves designing OAuth-based authentication, scheduled or real-time data synchronization, field mapping for transaction metadata, and mechanisms to resolve data schema mismatches. It should also support adding new POS providers through a plug-in architecture.

Acceptance Criteria

OAuth Authentication Setup

Given a shop owner selects Square as their POS provider When they initiate the connection process Then the system redirects them to Square’s OAuth consent screen and requests authorization And upon granting permission, the system receives and stores a valid access token securely

Real-Time Transaction Synchronization

Given the OAuth token for Shopify POS is valid When a sale occurs in the Shopify POS Then the transaction appears in Heatway’s database within 30 seconds with all relevant metadata fields

Scheduled Bulk Import of Historical Data

Given a shop owner connects Clover after hours When the system runs its nightly import job at 2:00 AM Then all transactions from the previous day are fetched, de-duplicated, and stored without errors

Transaction Metadata Field Mapping

Given a transaction record from any supported POS provider When the data reaches Heatway’s backend Then required fields (timestamp, total amount, item list) are mapped to the internal schema correctly And any unmapped fields are logged for review

Plug-in Architecture for New POS Providers

Given a developer adds a new POS connector using the provided plug-in template When they deploy the connector to a staging environment Then Heatway detects the new connector, loads it dynamically, and can authenticate and sync transactions through it

Correlation Engine

"As a shop owner, I want Heatway to calculate which areas of my store drive the most sales so that I can focus on high-performing zones."

Description

Build the core algorithmic engine that links foot traffic heatmap zones with corresponding POS transactions to calculate zone-specific conversion rates. The engine should process time-window alignment, location-based matching, and transaction tagging. It should also enable configurable timeframes, support batch and streaming modes, and provide APIs for querying correlation results.

Acceptance Criteria

Time Window Alignment

Given foot traffic heatmap zone data and POS transactions with timestamps within a 5-minute window, when the engine processes alignment, then all transactions are correctly associated with their corresponding zone events within the time window with 100% accuracy.

Location-Based Matching

Given distinct store zone coordinates and POS terminal locations, when the engine matches transactions to zones, then each transaction is tagged with the correct zone identifier with at least 99% accuracy in a test dataset.

Configurable Timeframe Filtering

Given an API request specifying a custom timeframe, when the engine processes the request, then correlation results include only data from the specified timeframe and exclude any outside the range.

Batch Processing Throughput

Given a 24-hour dataset of heatmap and POS transactions, when running in batch mode, then the engine completes processing within 2 minutes and outputs a JSON correlation report.

Streaming Mode Latency

Given real-time streams of heatmap and POS data, when operating in streaming mode, then correlation results are available via API within 10 seconds of data ingestion, maintaining end-to-end latency under 15 seconds.

Correlation Query API Response

Given a valid API call with zone ID and timeframe parameters, when the API is invoked, then it returns a JSON payload containing zone_id, timeframe_start, timeframe_end, visitor_count, transaction_count, and conversion_rate.

Zone Performance Dashboard

"As a shop owner, I want to view a visual report of zone-level conversion rates so that I can quickly identify and optimize my store layout for better sales."

Description

Design and implement an interactive dashboard that visualizes conversion rates per heatmap zone, allows filtering by time range or product category, and highlights top-performing and underperforming areas. The dashboard should offer drill-down capabilities, exportable reports (CSV, PDF), and support mobile and desktop views, integrating seamlessly with the existing Heatway UI.

Acceptance Criteria

Desktop Zone Conversion View

Given the user is viewing the Zone Performance Dashboard on a desktop browser, when they click on any heatmap zone, then the dashboard displays the zone's conversion rate as a percentage and the raw number of POS transactions for the selected time range.

Mobile Time Range Filtering

Given the user is on a mobile device viewing the dashboard, when they apply a time range filter, then the heatmap and conversion metrics update in under 2 seconds to reflect data from the specified period.

Drill-Down Insights

Given the user selects 'View Details' on any zone card, then the system navigates to a detailed view showing hourly conversion trends, associated product categories, and a mini heatmap for the selected zone.

Export Reports

Given the user selects 'Export' and chooses CSV or PDF, then the system generates and downloads a report that includes zone identifiers, conversion rates, transaction counts, and applied filters, formatted correctly and with a timestamp.

Performance Highlights

Given the dashboard loads, then the top 3 and bottom 3 performing zones are highlighted visually (green for top, red for bottom) with corresponding conversion rate annotations.

Actionable Recommendations & Alerts

"As a shop owner, I want to receive alerts and insights based on conversion analytics so that I can take timely actions to improve my store layout and drive sales."

Description

Develop a recommendation engine that analyzes zone conversion trends and generates actionable insights, such as suggesting layout changes or promotional opportunities. Implement alert notifications (email, in-app) to inform shop owners when conversion rates in critical zones drop below a threshold or when new high-performing patterns emerge.

Acceptance Criteria

Layout Change Recommendation Trigger

Given a specific zone’s conversion rate drops by more than 10% compared to its 30-day moving average, when analysis completes, then the system generates a layout change recommendation including zone ID, suggested action, and sends it via in-app notification and email within 5 minutes.

Promotional Opportunity Suggestion

Given a zone where conversion rate exceeds its historical average by at least 20% but foot traffic remains below the global store median, when a trend is detected over three consecutive business days, then the engine suggests a targeted promotional campaign outlining expected uplift metrics and delivers the recommendation within 10 minutes.

Low Conversion Rate Alert

Given a configurable conversion threshold for each zone, when any zone’s rate falls below this threshold for two consecutive traffic peaks, then the system dispatches an alert via email and in-app message containing zone name, current rate, threshold value, and timestamp within 2 minutes.

High-Performing Zone Alert

Given a zone’s recent conversion rate surpasses its previous peak by at least 15% during peak hours for three consecutive sessions, when this pattern is confirmed, then the system sends an alert with summary performance metrics (zone ID, new rate, percentage increase) to the shop owner via email and in-app notification within 5 minutes.

Recommendation Accuracy Validation

Given at least ten recommendations generated over a 7-day pilot period, when compared against actual zone performance changes, then at least 75% of recommendations must correspond to an observed conversion improvement of at least 5%, validated through POS and traffic data correlation reports.

Revenue Radar

Overlays real-time sales data onto live heatmaps, highlighting revenue hotspots in vibrant color gradients so merchants can instantly spot and capitalize on their most lucrative store zones.

Requirements

Real-Time Revenue Overlay

"As a shop owner, I want to see live sales revenue overlaid on the store heatmap so that I can quickly identify and capitalize on my most profitable areas."

Description

Enable overlaying live sales data onto the heatmap interface, using dynamic color gradients to indicate revenue intensity in each store zone. This feature integrates POS transactions via API in real time, highlighting high-earning areas and offering shop owners immediate insights to rearrange products or promote items. It ensures minimal latency, intuitive visualization, and seamless performance on smartphones without additional hardware.

Acceptance Criteria

Live POS Transaction Reflects on Heatmap

Given a new sale is recorded in the POS system, When the transaction data is received via the API, Then the corresponding store zone on the heatmap updates with the correct revenue value within 2 seconds

Accurate Revenue Gradient Display

Given the heatmap legend shows revenue ranges, When revenue data is overlaid, Then each zone’s color matches the defined gradient scale and legend indicates ranges correctly

Low-Latency Heatmap Refresh During Sales Surge

Given a surge of multiple transactions, When 20 or more sales occur within one minute, Then the heatmap refreshes and displays all updates within 3 seconds without missing transactions

Seamless Smartphone Heatmap Interaction

Given the feature is accessed on a smartphone over 4G or Wi-Fi, When the user pans, zooms, or taps zones, Then interactions remain responsive with no UI lag and memory usage stays below 200MB

Graceful Degradation on POS API Unavailability

Given the POS API is unreachable or returns an error, When attempting to fetch revenue data, Then the app displays the last cached heatmap with a warning indicator and retries fetching every 30 seconds

Customizable Color Gradients

"As a user, I want to customize the heatmap’s revenue color gradients so that I can tailor visual cues to my brand preferences and sales goals."

Description

Allow users to define custom color schemes and threshold values for revenue intensity, offering flexibility to adjust visualizations based on individual preferences or specific sales targets. This feature includes preset themes, slider controls for gradient ranges, and real-time previews. It enhances usability by catering to diverse user needs and ensures accessibility for color-blind users through adjustable palettes.

Acceptance Criteria

Preset Theme Selection

Given the user selects a preset color theme from the dropdown, when the selection is confirmed, then the heatmap’s color gradient updates to match the chosen theme within 1 second and the selection persists after page reload

Custom Gradient Slider Adjustment

Given the user adjusts the low and high revenue threshold sliders, when the sliders are moved, then the gradient range updates in real time, slider values cannot cross over, and both values remain within the allowed min/max bounds

Real-Time Preview Updates

Given the user changes any color or threshold setting, when the change is applied, then the preview pane refreshes immediately to reflect the updated gradient without requiring a page reload

Threshold Value Configuration

Given the user inputs numeric threshold values manually, when the values are entered and saved, then the system validates they are positive integers within the allowed range, applies them to the gradient, and displays a success message

Color-Blind Accessibility Mode

Given the user enables color-blind mode in settings, when the toggle is switched on, then the heatmap switches to a color-blind–safe palette from the preset list and a sample preview demonstrates the new palette

Historical Revenue Playback

"As a store manager, I want to replay past revenue heatmaps over time so that I can understand sales trends and optimize future store layouts."

Description

Provide a historical playback mode that replays revenue heatmap data over selectable time periods, allowing shop owners to analyze sales trends and patterns across days, weeks, or months. This feature integrates archived POS data with timeline controls, supports speed adjustment, and overlays key event annotations (e.g., promotions). It empowers data-driven layout decisions and aids in measuring the impact of marketing activities.

Acceptance Criteria

Time Period Selection

Given the shop owner opens the Historical Revenue Playback feature, when they select a valid start and end date within the available data range, then the heatmap displays revenue data accurately for all timestamps in that period.

Playback Speed Adjustment

Given the revenue heatmap playback is active, when the shop owner selects a playback speed option (e.g., 0.5x, 1x, 2x), then the replay speed adjusts accordingly without display lag or skipped data points.

Event Annotation Display

Given key events are predefined, when the playback reaches each event timestamp, then the system displays corresponding annotations on both the timeline control and heatmap overlay.

Archived POS Data Integration

When the shop owner initiates playback for a historical period, then the system retrieves and overlays the archived POS revenue data precisely aligned with the heatmap’s time intervals.

Data Accuracy Validation

Given the playback output for a selected period, when compared to raw POS reports for the same timestamps, then the total revenue figures per interval match within a 1% margin of error.

Revenue Spike Alerts

"As a small business owner, I want to receive alerts when a particular area’s revenue suddenly spikes or drops so that I can investigate and respond immediately."

Description

Implement alert notifications for sudden spikes or drops in revenue within specific store zones, triggered by customizable thresholds. Alerts can be delivered via in-app banners, push notifications, or email, containing details about the location, magnitude, and time of the event. This feature helps shop owners respond promptly to unexpected sales surges or declines, enhancing operational agility and promotional responsiveness.

Acceptance Criteria

Sudden Revenue Spike Alert in Primary Entrance Zone

Given the shop owner sets a spike threshold of 15% over 10 minutes for the Primary Entrance zone, When revenue increases by 15% or more within 10 minutes, Then the system triggers an alert delivered via in-app banner, push notification, and email containing the zone name, percentage increase, and timestamp.

Revenue Drop Alert in Clearance Section

Given the shop owner configures a drop threshold of 20% over 30 minutes for the Clearance Section, When revenue decreases by 20% or more within 30 minutes, Then the system sends a notification through the chosen channels with details on the zone, percentage decrease, and time period.

Custom Threshold Email Notification

Given the shop owner defines a custom threshold of 10 sales per hour in the Electronics Display zone, When sales fall below 10 in one hour, Then an email is sent with the zone, actual sales count, threshold value, and alert timestamp.

Push Notification Delivery Verification

Given a revenue spike or drop event meeting configured thresholds, When the system processes the alert, Then a push notification is received on the merchant’s device within 30 seconds of the event with accurate event details.

In-App Banner Alert Details Accuracy

Given that the alert is triggered for any zone, When the in-app banner is displayed, Then it shows the correct zone name, exact magnitude of change (in percentage or units), time of event, and includes a link to view the detailed heatmap.

Revenue Data Export and Reporting

"As a shop owner, I want to export revenue heatmap data to CSV and PDF so that I can perform detailed offline analyses and share reports with my team."

Description

Enable exporting revenue heatmap data and related analytics into CSV, PDF, or dashboard-compatible formats for offline analysis and reporting. This feature includes customizable report templates, date range filters, and automated scheduling for regular data exports. It facilitates comprehensive record-keeping, performance reviews, and sharing insights with stakeholders.

Acceptance Criteria

Manual CSV Export

Given a user selects 'Export' and chooses 'CSV' format When the system processes the request Then a CSV file containing revenue heatmap data for the user-selected date range is downloaded; And the filename includes the date range and timestamp; And the downloaded file opens successfully in standard spreadsheet software

Custom Date Range Filtered Export

Given a user inputs a valid start and end date When the export is initiated Then only revenue heatmap data within the specified date range is included; And the system validates date inputs preventing end dates before start dates; And the exported file metadata reflects the applied date filter

PDF Report Customization

Given a user applies a custom report template with company logo, header, and selected color scheme When the PDF export is generated Then the output PDF includes the user’s logo and header in the correct position; And the chosen color scheme is applied to heatmap gradients and charts; And all data sections (heatmap, summary table, key metrics) follow the template layout

Automated Scheduled Reports

Given a user schedules a recurring export (daily, weekly, or monthly) When the schedule time arrives Then the system automatically generates the report in the selected format (CSV or PDF) and emails it to the specified recipients; And a notification is logged confirming successful delivery; And failures trigger an error alert to the user

Dashboard-Compatible Data Export

Given a user selects 'Dashboard-Compatible' format and chooses the target platform When the export runs Then data is packaged in JSON or the required schema for that platform; And the exported file validates against the platform’s import schema; And the data fields include standardized keys for date, location, revenue, and heatmap intensity

Profit Pathways

Visualizes common customer routes annotated with average spend values, allowing owners to redesign layouts based on the most profitable trajectories and guide shoppers through high-value product sequences.

Requirements

Path Data Acquisition

"As a shop owner, I want to capture customer foot-traffic paths in real-time so that I can visualize movement patterns and pinpoint high-traffic areas."

Description

Enable real-time tracking and recording of customer movement paths within the store using smartphone sensors and geofencing overlays of the shop layout. This module collects, filters, and normalizes foot-traffic data to ensure high positional accuracy, then integrates seamlessly with Heatway’s analytics backend. The outcome is a continuous stream of path sequences that serve as the foundational dataset for profit pathway analysis.

Acceptance Criteria

Real-Time Path Capture Startup

Given the smartphone app is opened and the store layout is loaded, when a customer enters the geofence, then the system begins capturing location data at least once per second with positional accuracy within 2 meters.

Location Data Noise Filtering

Given raw sensor data containing jitter, when the data processing module runs, then all location points deviating more than 5 meters from the expected path are filtered out and the remaining path is smoothed.

Coordinate Normalization Accuracy

Given filtered position points, when they are normalized to the shop floor plan coordinate system, then the transformed points align within a ±1-pixel error margin of their true positions on the map.

Backend API Data Ingestion

Given normalized path data, when it is sent to the analytics backend API, then the API responds with status 200 OK within 500 milliseconds and confirms successful data ingestion.

High-Volume Streaming Reliability

Given up to 100 simultaneous customer paths being tracked, when data streams run continuously for one hour, then packet loss does not exceed 1% and no stream interruptions occur.

Spend Annotation Processor

"As a shop owner, I want to view average spend values along customer routes so that I can identify the most profitable pathways."

Description

Calculate and attach average spend values to each segment of captured customer routes by aggregating transaction data from integrated POS systems. This processor matches time-stamped purchases with corresponding foot-traffic segments, computes segment-level spend metrics, and outputs enriched path data. The result is a dataset that highlights the monetary value of each route, enabling data-driven layout decisions.

Acceptance Criteria

Match POS Transactions to Foot Traffic Segments

Given time-stamped foot-traffic segments and POS transactions, When processing transactions, Then each transaction must be matched to the correct segment within a 5-second time tolerance and recorded with the segment ID.

Calculate Average Spend per Segment

Given matched transactions for each segment, When computing segment metrics, Then the processor calculates average spend as total spend divided by transaction count, with results accurate to two decimal places.

Handle Missing or Delayed POS Data

Given transactions arriving after the initial processing window, When delayed transactions are ingested, Then they are flagged, reconciled in the subsequent run, and included in the segment-level spend without duplicates.

Enrich Path Data Export

Given the enriched path dataset, When exporting to the visualization module, Then each exported record includes segment ID, average spend, transaction count, and timestamp in JSON format.

Process High Volume Data Streams

Given up to 1,000 concurrent foot-traffic segments and 5,000 POS transactions per minute, When running the processor, Then all segments are annotated and output within 2 minutes, with a data drop rate below 1%.

Profit Pathways Visualization

"As a shop owner, I want to see profit-annotated customer routes on a store map so that I can easily identify which paths generate the highest revenue."

Description

Render annotated customer routes on an interactive store map, using color gradients and arrow overlays to indicate the frequency and average spend of each pathway. The visualization supports filtering by date, time of day, and spend thresholds, and provides zoom and hover interactions for detailed inspection. This UI empowers owners to quickly discern high-value trajectories and optimize merchandising accordingly.

Acceptance Criteria

Date Range Filtering

Given the user selects a start and end date for analysis, when the user applies the date filter, then only customer routes occurring within the specified date range are rendered on the map and all associated frequency and spend metrics update accordingly.

Time of Day Filtering

Given the user chooses one or more time-of-day segments (e.g., morning, afternoon, evening), when the user applies the time-of-day filter, then the heatmap displays only the customer pathways that occurred during the selected time intervals and updates average spend annotations.

Spend Threshold Filtering

Given the user sets minimum and maximum average spend values, when the user applies the spend threshold filter, then only routes with average spend within the defined range are visible on the interactive map.

Interactive Zoom and Hover Inspection

Given the map is displayed with multiple pathways, when the user zooms in or out, then the map and overlays rescale correctly without loss of clarity; and when the user hovers over any route segment, then a tooltip appears displaying that route’s frequency count, average spend, and path ID.

Color Gradient and Arrow Overlay Accuracy

Given the computed frequency and average spend data for each route, then the visualization uses a continuous color gradient that accurately reflects route frequency based on the legend, and arrow thickness or size scales proportionally to the average spend values for each pathway.

Layout Optimization Recommendations

"As a shop owner, I want personalized layout recommendations so that I can redesign my store to guide customers through the most profitable paths."

Description

Analyze profit-annotated routes to generate actionable layout adjustment suggestions, such as relocating high-margin products to high-spend pathways or adding promotional displays along underperforming routes. The engine uses rule-based and machine learning algorithms to propose modifications aimed at guiding shoppers through high-value product sequences and balancing traffic flow.

Acceptance Criteria

Identifying High-Margin Product Overlaps

Given the profit pathways analysis identifies high-margin products outside the top two profit pathways, when the layout optimization engine runs, then it suggests relocating at least three high-margin products into those pathways.

Underperforming Route Promotional Display Suggestion

Given at least one customer route is underperforming by more than 10% of average spend, when the recommendations are generated, then the system proposes at least two promotional display placements along that route specifying product and location.

Balancing Traffic Flow Recommendations

Given a pathway’s traffic density exceeds the defined threshold, when layout adjustments are calculated, then the engine recommends redistributing at least 20% of display space to adjacent underutilized routes.

Actionable Recommendation Report Generation

Given the layout optimization process completes, when the shop owner requests the report, then the system generates a PDF with at least five actionable layout suggestions and includes annotated visual overlays on the heatmap.

Machine Learning Model Validation

Given historical in-store data from the past 30 days, when the recommendation engine runs, then predicted spend uplift is at least 5% with a 95% confidence interval.

Route Export and Reporting

"As a shop owner, I want to export profit pathway reports so that I can share insights with my team and track changes over time."

Description

Provide functionality to export annotated routes and summary reports in PDF and CSV formats. Reports include visual snapshots of profit pathways, key metrics such as average spend per path, and recommended layout changes. This feature facilitates offline analysis, stakeholder sharing, and record-keeping of traffic and revenue insights.

Acceptance Criteria

PDF Export Generation for Profit Pathways Report

Given the shop owner views the Profit Pathways screen with annotated routes and metrics, When they select 'Export PDF', Then the system generates a PDF containing visual snapshots of the profit pathways, a summary table of average spend per path, and recommended layout changes, and initiates a download within 10 seconds.

CSV Export of Annotated Routes Data

Given the shop owner views the Profit Pathways data, When they click 'Export CSV', Then the system produces a CSV file with columns for route ID, sequence of coordinates, timestamps, and average spend per path, and triggers a file download within 5 seconds.

Inclusion of Recommended Layout Changes in Reports

Given a Profit Pathways report is generated (PDF or CSV), When the export completes, Then the report includes a dedicated section listing at least three recommended layout changes ranked by potential revenue uplift.

Print-Ready Formatting of PDF Reports

Given the shop owner selects 'Export PDF', When the system generates the report, Then the PDF is formatted for A4 paper with consistent headers (including report title and date), footnotes explaining metrics, and legible visuals at 300 DPI resolution.

Report Generation Performance Under Peak Data Load

Given a dataset of up to 100 annotated routes, When the shop owner requests export (PDF or CSV), Then the system completes file generation and download initiation within 30 seconds without errors.

POS Pulse

Sends instant alerts when busy sections show unexpectedly low sales, prompting timely interventions—such as staff engagement or targeted promotions—to boost conversion and minimize missed revenue opportunities.

Requirements

Real-Time Sales-Traffic Correlation

"As a shop owner, I want to see live correlation between foot traffic and sales so that I can identify underperforming areas immediately."

Description

Implement a system that continuously collects foot traffic data and point-of-sale (POS) transactions, aligns timestamps, and computes real-time metrics comparing customer presence against actual sales. The module should visualize these correlations in the POS Pulse dashboard, highlighting sections where traffic significantly exceeds sales to trigger alerts and guide immediate action.

Acceptance Criteria

High-Traffic Low-Sales Detection

Given continuous foot traffic and POS data streams, When a section’s traffic-to-sales ratio exceeds 1.2 for at least 5 consecutive minutes, Then the system generates an alert and displays it in the POS Pulse dashboard within 30 seconds.

Data Timestamp Alignment Verification

Given incoming foot traffic and POS transaction data, When records are ingested, Then timestamps are aligned within a tolerance of 1 second and any discrepancies greater than 2 seconds are logged for review.

Real-Time Alert Triggering

Given alert thresholds are configured for traffic-sales deviations, When a threshold breach is detected, Then the system sends notifications to staff via SMS and in-app within 60 seconds.

Dashboard Visualization Accuracy

Given real-time correlation metrics computed, When rendered on the POS Pulse dashboard, Then the heatmap overlay accurately reflects up-to-the-minute traffic and sales data with less than 5% variance.

Performance Under Peak Load

Given simultaneous updates from 100 sections per minute, When processing loads peak, Then correlation computations and dashboard refreshes complete within 2 seconds without errors or data loss.

Configurable Alert Thresholds

"As a shop owner, I want to set my own alert thresholds so that I receive meaningful notifications aligned with my business goals."

Description

Provide an interface for shop owners to define and adjust custom thresholds for metrics such as traffic-to-sales ratio, absolute traffic counts, and time-based windows. These thresholds will determine when POS Pulse sends alerts for low conversion rates, ensuring notifications are tailored to each store’s unique performance benchmarks.

Acceptance Criteria

Initial Threshold Configuration

Given the shop owner is on the Configurable Alert Thresholds page for the first time, When they enter a valid traffic-to-sales ratio threshold of 5%, an absolute traffic count threshold of 100 customers, and a time-based window of 30 minutes, and click ‘Save’, Then the system stores these values, displays a confirmation message, and updates the thresholds table accordingly.

Threshold Modification

Given existing custom thresholds are displayed, When the shop owner changes the traffic-to-sales ratio to 7%, adjusts the traffic count to 150 customers, updates the time window to 45 minutes, and clicks ‘Save’, Then the system overwrites the previous thresholds, shows a success notification, and reflects the new values in the settings list.

Threshold Boundary Validation

Given the shop owner enters threshold values outside allowed ranges (e.g., negative numbers or exceeding maximum limits), When they attempt to save, Then the system displays clear validation error messages next to each invalid field and prevents saving until all inputs are within the valid ranges.

Alert Generation on Threshold Breach

Given custom thresholds are configured and active, When the traffic-to-sales ratio falls below the defined threshold within any 20-minute window, Then the system generates an alert within 1 minute, sends a push notification to the owner’s device, and logs the alert event in the Alerts History.

Threshold Persistence After Logout

Given the shop owner has saved custom thresholds, When they log out and log back in, Then the previously configured thresholds are automatically loaded and displayed on the Configurable Alert Thresholds page without requiring re-entry.

Multi-Channel Notification Delivery

"As a store manager, I want to receive alerts on my preferred channels so that I never miss critical notifications even when I’m away from the app."

Description

Develop a notification engine that can dispatch POS Pulse alerts via multiple channels including in-app push notifications, SMS, and email. The system should support user preferences for channel selection, quiet hours, and escalation rules, ensuring timely delivery and minimizing missed alerts.

Acceptance Criteria

In-App Push Notification During Operating Hours

Given a POS Pulse alert triggers for an area with low sales and the user has enabled in-app notifications and it is outside their quiet hours preferences, when the system processes the alert, then the user receives a push notification within 5 seconds with the correct section name, time, and suggested action.

SMS Alert Delivery for Offline Users

Given a POS Pulse alert triggers and the user has disabled in-app notifications or is offline on the app and has SMS enabled, when the alert is generated, then an SMS message is sent to the user's registered phone number within 60 seconds containing the alert details and a link to view more information.

Email Notification Fallback After Push Failure

Given a POS Pulse alert triggers and the system fails to deliver the in-app push notification after two retry attempts, when the escalation rule is applied, then an email is sent to the user's registered email address within 2 minutes containing the same alert content and a visible subject line indicating importance.

Quiet Hours Suppression and Deferred Delivery

Given it is within the user's configured quiet hours and a POS Pulse alert is generated, when the system processes the alert, then the notification is queued and no push, SMS, or email is sent until quiet hours end, at which point all queued notifications are delivered in chronological order.

Channel Preference Order Execution

Given a POS Pulse alert triggers and the user has set a channel preference order (e.g., push, SMS, email), when delivering notifications, then the system attempts delivery in the specified order, stopping at the first successful channel and logging the result.

Escalation After Multiple Missed Deliveries

Given a POS Pulse alert triggers and the initial notification channel fails or is not acknowledged within 5 minutes, when retry rules exhaust the configured number of retries, then the system escalates to the next channel in the escalation sequence and logs both the failures and the successful delivery.

Staff Assignment Recommendations

"As a store manager, I want recommendations on which staff member to assign when an alert triggers so that I can quickly deploy personnel to boost sales."

Description

Integrate with employee scheduling data to suggest optimal staff redeployment when an alert is triggered. The system should analyze current shift rosters, proximity of staff, and workload balance, then recommend which available team member can address the busy-but-low-sales section to improve customer engagement.

Acceptance Criteria

Busy Section Alert Triggers Staff Recommendation

Given a busy-but-low-sales section alert is received, When the system processes the alert, Then at least one staff assignment recommendation is displayed within 5 seconds

Recommendation Matches Staff Proximity

Given the pool of on-shift employees, When generating recommendations for a busy section, Then only staff located within 30 meters of that section are suggested

Balanced Workload Consideration

Given multiple eligible staff, When recommending assignments, Then the system prioritizes the employee with the lowest number of recent recommendations, provided the difference in workload exceeds 10%

Schedule Compliance Verification

Given staff scheduling data, When producing recommendations, Then only employees with active shifts at the current time are included

Override of Staff Recommendation

Given a displayed staff recommendation, When the shop owner overrides the suggestion, Then the system logs the override event and excludes the same staff member from suggestions for that section for the next 10 minutes

Historical Trends Analysis

"As a shop owner, I want to review historical alert data and intervention outcomes so that I can understand trends and refine my response strategies."

Description

Extend the POS Pulse dashboard to include historical analysis of alert events, conversion rates, and staff interventions. The feature should allow users to filter by date range, section, and alert type, generating reports that reveal patterns and measure the impact of past interventions.

Acceptance Criteria

Filter by Date Range

Given a user accesses the Historical Trends Analysis dashboard When they select a valid start and end date Then the system displays alert events, conversion rates, and staff interventions exclusively within the chosen period

Filter by Section

Given a user opens the Historical Trends Analysis dashboard When they choose a specific store section from the section dropdown Then the displayed data is limited to that section’s historical alert events, conversion rates, and interventions

Filter by Alert Type

Given a user is on the Historical Trends Analysis dashboard When they select one or more alert types (e.g., low sales in busy section) Then the report shows only events matching the selected alert types

Generate Trend Report

Given filter criteria have been applied When the user clicks the 'Generate Report' button Then the system produces a downloadable report (PDF or CSV) summarizing historical trends and patterns

Analyze Intervention Impact

Given a user reviews a generated report When they compare conversion rates before and after staff interventions Then the system highlights statistically significant changes and displays a summary of the intervention’s effectiveness

ValueView

Transforms traditional heatmaps into conversion-focused maps by weighting foot traffic with actual sales figures, giving a clear, unified view of where visitor interest truly translates into revenue.

Requirements

Sales Data Integration

"As a shop owner, I want the system to automatically import my sales transactions so that foot traffic data can be weighted by actual revenue in real time."

Description

Develop a module to connect and ingest point-of-sale transactions from various registers and e-commerce systems, normalize and timestamp sales data to align with foot traffic events, ensuring accurate mapping of sales to visitor movements within the store.

Acceptance Criteria

Sales Data Source Authentication

Given valid register or e-commerce credentials When the integration module attempts to connect Then it successfully authenticates and retrieves the sales data schema

Sales Data Ingestion and Normalization

When raw sales transactions are ingested Then all records are normalized to a common schema with standardized fields (e.g., product ID, quantity, price, currency) with 100% field mapping

Timestamp Alignment with Foot Traffic Data

Given normalized sales records with timestamps When aligning with foot traffic events Then at least 99% of sales records fall within a 5-second window of corresponding foot traffic timestamps

Data Mapping Accuracy Check

When mapping sales records to foot traffic zones Then the system correctly links at least 95% of sales to the correct store zones based on foot traffic event data

Error Handling for Data Discrepancies

When encountering malformed or duplicate sales records Then the system logs the error, retries ingestion up to three times, and flags unresolvable records in the dashboard

Weighted Heatmap Algorithm

"As a shop owner, I want the heatmap to reflect where my sales are happening so that I can see which areas are truly driving revenue."

Description

Implement an algorithm that combines visitor dwell time and count with corresponding sales figures, calculating a conversion score per location to generate a heatmap where color intensity reflects revenue impact rather than raw traffic.

Acceptance Criteria

Real-Time Conversion Heatmap Generation

Given visitor count, dwell time, and sales figures are available for each zone When the algorithm executes Then it calculates a conversion score for each zone using sales-weighted dwell metrics And generates a heatmap within 3 seconds where color intensity reflects normalized revenue impact

Historical Data Consistency Check

Given one week of recorded foot traffic, dwell time, and sales data When the algorithm processes historical data Then the resulting conversion scores for each zone match manual calculations within a 2% margin of error

Peak Hour Performance Stress Test

Given a simulated burst of 10,000 visitor events and associated sales transactions When the algorithm runs under load Then it completes heatmap generation within 5 seconds without errors or timeouts

Outlier Sales Impact Mitigation

Given a zone records an exceptionally high-value sale When conversion scores are computed Then the algorithm caps the zone’s conversion score at 1.5 times the median zone score to prevent skewing

User Interface Integration

Given the weighted heatmap data from the algorithm When loaded into the Heatway UI Then the overlay displays correctly with a legend scaled to revenue impact And the user can toggle between raw traffic and revenue-weighted views

Real-time Visualization

"As a shop owner, I want to see the conversion heatmap update in real time so that I can make immediate adjustments to displays."

Description

Create a real-time rendering engine that updates the conversion-focused heatmap on the shop owner’s device with minimal latency, handling streaming foot traffic and sales data concurrently to provide up-to-the-second insights.

Acceptance Criteria

Initial Heatmap Load

Given the shop owner opens the Real-time Visualization screen, when the app establishes a connection to the data streams, then the initial conversion-focused heatmap must render within 2 seconds using the latest 1-minute aggregated data.

Continuous Data Streaming

Given the app has been running for at least 10 minutes, when foot traffic and sales events arrive continuously at up to 100 events per second, then the heatmap must update at least once per second without dropping updates or accumulating a backlog exceeding 5 seconds.

Concurrent Data Handling

Given simultaneous streams of foot traffic and sales data, when both event types arrive within the same time window, then the rendering engine must correctly weight and display conversion data without data loss or misalignment in the heatmap.

Latency Threshold Compliance

Given peak system load conditions, when processing and rendering each heatmap frame, then the end-to-end latency from data event arrival to visual display must not exceed 500 milliseconds.

Device Resource Monitoring

Given the app runs on a standard smartphone for 30 minutes, when the Real-time Visualization engine is active continuously, then CPU usage must remain below 70% and memory usage below 200 MB, with no app crashes or perceptible UI lag.

Interactive Dashboard Controls

"As a shop owner, I want to filter conversion heatmaps by time and product category so that I can analyze revenue patterns for different promotions."

Description

Design interactive UI controls allowing users to filter heatmap views by time range, product category, or sales thresholds, including zoom, pan, and overlay toggles for detailed analysis of specific areas or periods.

Acceptance Criteria

Time Range Filtering Scenario

Given a user selects a start and end time on the time-range slider when the user clicks 'Apply' then the heatmap updates to display data exclusively within the selected time range

Product Category Filtering Scenario

Given a user chooses one or more product categories from the filter dropdown when the user applies the filter then the heatmap reflects only foot traffic associated with the selected categories

Sales Threshold Filtering Scenario

Given a user defines a minimum and/or maximum sales threshold when the user applies the filter then the heatmap highlights only areas where weighted traffic exceeds the specified sales thresholds

Zoom and Pan Interaction Scenario

Given a user uses zoom controls or drags the map area when the interaction is complete then the heatmap view scales and repositions correctly, maintaining data fidelity and control responsiveness

Overlay Toggle Scenario

Given a user toggles overlay layers (e.g., product markers, sales bubbles) on or off when the toggle state changes then the corresponding overlays appear or disappear immediately without affecting other map elements

Exportable Conversion Reports

"As a shop owner, I want to export conversion heatmap reports so that I can share performance insights with my team."

Description

Provide functionality to generate and download PDF and CSV reports summarizing conversion heatmap data, including key metrics, trend graphs, and recommended action points to facilitate sharing insights with stakeholders.

Acceptance Criteria

Generate PDF Conversion Report

When the user selects the “Export as PDF” option, then a PDF file is generated containing the conversion summary, weighted foot traffic metrics, trend graphs, and three recommended action points, and the file download begins automatically.

Download CSV Conversion Data

When the user clicks “Download CSV,” then a CSV file is provided containing detailed conversion data for each tracked zone, including timestamp, visit count, sales figures, and conversion rate, and the file size does not exceed 10MB.

Report Data Accuracy

The metrics displayed in the exported PDF and CSV reports must match the on-screen ValueView dashboard metrics within a 1% variance for all key figures (visits, sales, conversion rate) when tested with sample data.

Embed Trend Graphs

The exported PDF report must include trend graphs for foot traffic and sales conversion over the selected date range, with clear axis labels, legends, and data points that match the dashboard visuals.

Include Recommended Action Points

Each exported report must include at least three dynamically generated, context-specific recommendations based on observed conversion patterns and blind-spot analysis from the ValueView data.

Performance Monitoring

"As a developer, I want system monitoring in place so that we can quickly identify and resolve performance issues."

Description

Implement monitoring and logging for data processing pipelines and visualization performance to detect bottlenecks, ensure system reliability, and enable troubleshooting of conversion map generation processes.

Acceptance Criteria

Real-time Latency Monitoring Alerts

Given the data pipeline processes incoming records, when processing latency exceeds 2 seconds for 5 consecutive records, then an alert is sent to the DevOps team.

Dashboard Rendering Performance Logging

Given the conversion map is rendered for a user, when rendering time exceeds 3 seconds, then a log entry with timestamp, user ID, and rendering duration is recorded in the monitoring system.

Data Ingestion Error Rate Tracking

Given the data ingestion pipeline is running, when the error rate (failed records/total records) exceeds 0.5% over any 10-minute window, then a critical alert is generated and logged to the monitoring dashboard.

Resource Utilization Metrics Collection

Given batch jobs are executing in the pipeline, when CPU or memory utilization exceeds 80% for 5 continuous minutes, then utilization metrics are logged every minute and an alert is raised.

Historical Performance Reporting

Given performance logs are stored, when a user requests a report for a date range, then the system returns average latency and throughput per hour within 2 minutes of the request.

Basket Blend

Correlates product movement patterns with basket contents by syncing item-level POS data, revealing which displays drive upsells and cross-sells so store owners can optimize product adjacencies.

Requirements

POS Data Sync Integration

"As a shop owner, I want my POS system to sync basket contents with foot traffic data so that I can understand which displays lead to sales."

Description

Integrate the Heatway platform with item-level POS systems to automatically and securely import basket contents data. This integration enables the correlation of in-store customer movement patterns captured via smartphone heatmapping with actual purchase details, providing actionable insights on which product adjacencies drive upsells and cross-sells. The functionality should handle various POS vendors, support real-time or scheduled batch syncs, and include error handling and data reconciliation to ensure accuracy.

Acceptance Criteria

Real-time POS Data Sync

Given a configured POS integration When a sale transaction occurs Then within 5 seconds Heatway imports the basket contents matching the transaction ID and displays the items list accurately in the UI

Scheduled Batch POS Data Sync

Given a batch sync schedule set When the scheduled time arrives Then Heatway initiates a data pull from the POS system updates all transactions from the last 24 hours without duplicate records and completes within 15 minutes

Cross-vendor POS Integration

Given integrations for POS vendors A, B, and C When each is configured Then Heatway successfully authenticates fetches basket data and normalizes it into the standard data model with 99.9% field mapping accuracy

Data Discrepancy Reconciliation

Given imported basket data and transaction logs differ When the reconciliation process runs Then Heatway identifies mismatches logs them and flags them for manual review with reconciliation reports accessible in the dashboard

Secure Data Transfer and Storage

Given POS sync in progress When data is transmitted Then it is encrypted in transit via TLS 1.2+ and at rest using AES-256 and only authorized roles can view raw POS data

Basket-Heatmap Correlation Engine

"As a shop owner, I want the system to correlate where customers move in my store with the items they purchase so that I can pinpoint which displays drive additional sales."

Description

Develop a correlation engine that links foot traffic heatmap zones with basket item data to identify patterns of upsells and cross-sells. The engine analyzes customer paths, dwell times, and purchase combinations to uncover which product placements and adjacencies result in increased basket value. It should support configurable correlation thresholds and present confidence scores for each identified association.

Acceptance Criteria

Configurable Correlation Threshold Setup

Given the shop owner selects a correlation threshold of 20%, When the engine executes the analysis, Then only product associations with a confidence score >= 20% are returned in the results.

Heatmap Zone and Basket Data Integration

Given valid foot traffic heatmap zones and synchronized item-level POS basket data, When the correlation engine processes the input files, Then each heatmap zone record is correctly linked to the corresponding basket items and timestamped without data loss.

Upsell and Cross-sell Pattern Detection

Given a historical dataset containing known upsell and cross-sell patterns, When the engine runs its pattern analysis, Then it must identify all predefined product combinations with uplift ≥ 15% and assign confidence scores within ±5% of expected values.

Real-time Analysis Performance

Given a live data feed of at least 1,000 events per minute, When the correlation engine ingests and processes the stream, Then updated correlation results are produced and available in the UI within 5 seconds for each new data batch.

Visual Confidence Score Display

Given calculated product associations with confidence scores, When results are rendered in the user interface, Then each association displays its confidence score as a percentage and the list is sorted in descending order of confidence.

Upsell & Cross-sell Visualization Dashboard

"As a shop owner, I want a visual dashboard showing which products frequently sell together in high-traffic areas so that I can optimize my store layout."

Description

Create an interactive dashboard within Heatway that visualizes key upsell and cross-sell opportunities derived from the Basket Blend feature. The dashboard should display heatmaps overlaid with product correlation metrics, highlight top-performing product pairs, and allow filtering by time range, product category, and traffic intensity. Users can drill down into specific zones and view details on basket compositions tied to those areas.

Acceptance Criteria

Filter by Time Range

Given the user selects a time range on the dashboard, when the selection is applied, then the heatmap updates to display foot traffic and upsell/cross-sell correlations only for the chosen period

Highlight Top-Performing Product Pairs

Given the basket blend data is loaded, when the dashboard renders, then the top 5 product pairs by upsell and cross-sell correlation are listed with correlation scores and sales lift percentages

Drill-Down into Specific Zones

Given the heatmap is visible, when the user clicks on a specific zone, then detailed basket composition data for that zone is displayed, including product names, quantities, and correlation metrics

Overlay Product Correlation Metrics on Heatmap

Given the base foot traffic heatmap is displayed, when product correlation overlay is enabled, then correlation values for each zone are shown using color-coded markers and tooltips with exact metrics

Filter by Product Category and Traffic Intensity

Given multiple filters are available, when the user applies product category and traffic intensity filters, then the heatmap and top-performing product pair lists update to reflect only data matching those filters

Real-time Insights Refresh

"As a shop owner, I want the heatmap and sales correlations to refresh automatically within minutes so that I can respond quickly to customer behavior."

Description

Implement a real-time data refresh mechanism that updates heatmap and basket blend insights within five minutes of new POS or movement data being available. This requires efficient data pipelines, incremental synchronization, and in-memory processing to ensure shop owners can make timely display adjustments. The system should notify users when new insights are ready via in-app alerts.

Acceptance Criteria

POS Data Sync Trigger

Given new POS transaction data is ingested, When the data pipeline runs, Then the basket blend insights table is updated with new transaction details within 5 minutes.

Movement Data Ingestion

Given new customer movement events are received, When the ingestion service processes the events, Then the heatmap dataset is refreshed within 5 minutes of receipt.

In-App Alert Delivery

Given new insights are available, When the refresh process completes, Then an in-app notification is delivered to the user within 1 minute.

Heatmap Dashboard Refresh

Given a user is viewing the heatmap dashboard, When new movement or POS data has been processed, Then the heatmap visualization automatically refreshes in the UI without manual reload.

Basket Blend Insights Update

Given the basket blend insight view is open, When new POS transactions are synchronized, Then the basket blend report updates in real-time with no stale records and displays the latest cross-sell metrics.

Data Privacy & Security Compliance

"As a compliance officer, I want customer movement and purchase data to be anonymized and encrypted so that we adhere to privacy regulations and protect personal information."

Description

Establish data privacy and security measures to ensure all POS and movement data is anonymized, encrypted in transit and at rest, and stored in compliance with relevant regulations (e.g., GDPR, CCPA). This includes role-based access controls, audit logging, and data retention policies to protect customer information while providing shop owners with aggregated insights.

Acceptance Criteria

Anonymizing POS and Movement Data

Given raw POS and movement data is ingested, When data processing is executed, Then all PII fields (e.g., names, email addresses, credit card numbers) are removed or replaced with pseudonymous identifiers before storage.

Encrypting Data in Transit

Given data is transmitted between clients, servers, and third-party integrations, When communication channels are established, Then all data in transit must be encrypted using TLS 1.2 or higher.

Encrypting Data at Rest

Given data stored in databases, file systems, and backups, Then the system must apply AES-256 encryption to all sensitive data at rest, and secure encryption keys using a hardware security module (HSM).

Implementing Role-Based Access Controls

Given user roles defined in the system (e.g., shop owner, admin, analyst), When users attempt to access data or functionality, Then access controls must enforce least privilege, granting permissions only to authorized roles with valid credentials.

Maintaining Audit Logs

Given all user and system actions affecting privacy and security, Then audit logs must capture the timestamp, user identity, action type, affected resources, and source IP; logs must be tamper-evident and retained for at least 12 months.

Enforcing Data Retention Policies

Given data retention policies configured per regulation (e.g., GDPR, CCPA), When data reaches its end-of-life period, Then the system automatically archives or permanently deletes the data, and logs the deletion event with context.

Insights Export & Sharing

"As a shop owner, I want to export detailed reports on product interaction and sales correlations so that I can share insights with my team and suppliers."

Description

Enable shop owners to export Basket Blend insights and visualizations in multiple formats (PDF, CSV) for offline analysis and team collaboration. The export feature should include summaries of top correlations, heatmap snapshots, and recommended product adjacency actions. Exports can be scheduled or generated on-demand and shared via email or link.

Acceptance Criteria

Export Formats Availability

Given the shop owner navigates to the Insights Export page When they open the format selection dropdown Then PDF and CSV options must be visible and selectable

On-Demand Export Generation

Given the shop owner selects a format and clicks “Export Now” When the export request is submitted Then a file is generated within 30 seconds containing top correlations summary, heatmap snapshots, and recommended adjacency actions

Scheduled Export Configuration

Given the shop owner navigates to the Schedule Exports section When they set frequency, time, and format Then the system must save settings and enqueue automatic exports at configured intervals

Email Sharing of Exports

Given the shop owner selects email sharing When they enter a valid email address and click “Send” Then an export file (or link) is emailed to recipients within 1 minute

Secure Link Sharing

Given the shop owner generates a shareable link When the link is created Then it must be unique, expire after configured days, and grant view-only access to the exported report

Loyalty Lens

Profiles repeat visitors by tracking visit frequency and engagement, assigning loyalty scores to highlight high-value guests and inform targeted strategies.

Requirements

Unique Visitor Identification

"As a shop owner, I want to uniquely identify returning visitors so that I can track their visit history and behavior over time."

Description

Implement a mechanism to assign and persist a unique identifier for each visitor based on smartphone sensor data, allowing the system to recognize returning guests without capturing personal PII. This functionality is critical for building individual visitor profiles, enabling long-term tracking of visit history and behavioral patterns within the store. It must integrate with existing foot‐traffic data capture, ensure data privacy compliance, and support seamless retrieval of past interaction records.

Acceptance Criteria

Initial Visitor ID Assignment

Given a new smartphone enters the store with no existing identifier, when the system captures sensor data, then it generates a unique ID, stores it in the visitor database, and confirms persistence without errors.

Returning Visitor Recognition

Given a smartphone with an existing unique ID re-enters the store, when sensor data is matched to stored signatures, then the system retrieves the visitor’s ID, links the new visit to the existing profile, and logs the visit timestamp.

Offline Data Caching and Sync

Given network connectivity is unavailable, when visitor data is captured, then the system caches the unique ID and visit details locally, and upon reconnection, syncs without creating duplicate visitor records.

Cross-Session Profile Retrieval

Given a request for a visitor’s history, when querying the database with a valid unique ID, then the system returns all past visit records and behavioral data associated with that ID.

Data Privacy Compliance

Given unique identifiers are generated, when storing visitor data, then the system ensures no personal PII is captured, applies encryption at rest, and enforces GDPR-compliant retention policies.

Visit Frequency Tracking

"As a shop owner, I want the system to count how often each visitor returns so that I can measure customer loyalty and identify frequent shoppers."

Description

Develop a component that automatically counts and records each visit per identified visitor, updating a visit frequency tally in real time. This requirement ensures the system can differentiate between new and repeat guests, offering insights into customer retention and loyalty trends. It should leverage the unique visitor identifiers and integrate with the data analytics pipeline to store and retrieve frequency metrics efficiently.

Acceptance Criteria

New Visitor Identification

Given a visitor’s device identifier is not found in the database When the visitor enters the store Then a new visitor record is created with visit count initialized to 1

Repeat Visitor Session Detection

Given a visitor’s device identifier matches an existing record When the visitor enters the store Then the visit frequency tally for that visitor is incremented by 1 in real time

Real-Time Visit Count Update

Given a visitor session is active When the visitor crosses the store threshold Then the system updates the visit count on the dashboard within 5 seconds

Frequency Tally Persistence

Given the visit frequency tally is updated When the visitor session ends Then the updated count is stored persistently in the analytics database without data loss

Analytics Pipeline Integration

Given visit frequency records exist When analytics jobs run Then all visit frequency data is correctly ingested into the data pipeline and available for reporting

Loyalty Scoring Algorithm

"As a shop owner, I want visitors to be assigned a loyalty score based on their visit patterns and engagement so that I can prioritize high‐value customers."

Description

Design and implement an algorithm that combines visit frequency, duration of stay, and in‐store engagement metrics into a composite loyalty score for each visitor profile. The scoring model should be configurable via weighted parameters, allowing product owners to adjust emphasis on different engagement factors. Results must be normalized to a standard scale and updated dynamically as new data arrives.

Acceptance Criteria

Configurable Weight Parameters

Given an administrator sets custom weights for visit frequency, duration, and engagement When the loyalty scoring algorithm runs Then the computed loyalty scores must proportionally reflect the configured weight distribution within a tolerance of ±1%.

Dynamic Score Normalization

Given raw loyalty scores across diverse ranges When applying normalization Then all scores must map to the standardized 0 to 100 scale, preserving the relative rank ordering.

Real-time Data Update

Given new visit data is ingested When data arrives within one minute Then the loyalty score for the affected visitor profile updates automatically without manual intervention.

Accuracy of Composite Loyalty Score

Given historical data and manual calculations for sample visitor profiles When comparing algorithm-generated scores Then the discrepancy must not exceed 2% against the manual benchmark.

Scalability with High Visitor Volume

Given simultaneous data ingestion for up to 10,000 visitors per minute When the algorithm processes the data Then it completes scoring within 5 seconds on average without errors or significant performance degradation.

Loyalty Analytics Dashboard

"As a shop owner, I want a dashboard that displays customer loyalty scores and profiles so that I can quickly understand my most valuable customers."

Description

Build an interactive dashboard within the Heatway app that visualizes visitor loyalty scores, visit trends, and top‐ranked guest profiles. Features include sortable tables, trend graphs over time, and filters for date ranges or score thresholds. The dashboard should present real‐time updates, support drill‐down into individual profiles, and align with the app’s existing UI/UX standards for consistency.

Acceptance Criteria

Viewing Loyalty Scores Trend Over Time

Given the user navigates to the Loyalty Analytics Dashboard, When they select a custom date range, Then the trend graph updates to display loyalty scores aggregated by day/week/month within that range without errors.

Sorting Guest Profiles by Loyalty Score

Given the Loyalty Scores table is displayed, When the user clicks the 'Loyalty Score' column header, Then the table sorts guest profiles in ascending or descending order based on their loyalty scores and indicates the sort direction visually.

Filtering Profiles by Score Threshold

Given the Loyalty Scores table is displayed, When the user applies a loyalty score filter with a minimum threshold, Then only profiles with scores equal to or above the threshold are shown in the table.

Real-Time Data Update Display

Given the dashboard is open, When a new visit is logged, Then the loyalty scores, trend graphs, and tables update within 30 seconds to reflect the latest data without requiring manual refresh.

Drilling Down into Individual Guest Profiles

Given a guest profile row in the table, When the user clicks the profile, Then a detail panel opens showing visit history, loyalty score changes, and engagement metrics for that individual.

Consistent UI/UX with Existing App Standards

Given the Loyalty Analytics Dashboard is loaded, When the user interacts with any UI element, Then fonts, colors, spacing, and component styles match the app's design system and no horizontal scrolling is required on standard mobile view.

Segment Export for Marketing

"As a shop owner, I want to export lists of high‐loyalty customers so that I can run targeted marketing campaigns."

Description

Enable the export of visitor segments based on loyalty score ranges or engagement criteria into common formats (CSV, JSON) or direct integration with email and SMS marketing platforms via API connectors. This requirement ensures shop owners can seamlessly leverage high‐loyalty customer lists for targeted outreach campaigns, driving personalized promotions and improving ROI on marketing efforts.

Acceptance Criteria

Export High-Loyalty Customers to CSV

Given a shop owner selects loyalty score range 80–100 When they click 'Export CSV' Then a file named 'segment_80_100.csv' is generated And the file contains only visitor records with loyalty_score ≥80 and ≤100 And each record includes customer_id, name, email, phone, loyalty_score, last_visit_date And the file is UTF-8 encoded, comma-separated with a header row

Export Low-Engagement Visitors to JSON

Given a shop owner selects engagement criteria of fewer than 2 visits When they click 'Export JSON' Then a file named 'segment_low_engagement.json' is generated And the file contains a valid JSON array of visitor objects And each object includes id, name, visit_count, last_visit_date, loyalty_score And the JSON passes schema validation with correct field types

Integrate with Email Marketing via API

Given the shop owner has configured valid API credentials for the email marketing platform When they choose 'Send to Email Platform' Then the system issues a POST to /api/segments on the email platform endpoint And the request payload includes segment name, description, and list of subscriber emails And the platform responds with HTTP 200 OK and a confirmation ID And the confirmation ID is recorded in the system log

Integrate with SMS Marketing Provider via API

Given the shop owner has set up and validated SMS provider credentials When they trigger 'Send to SMS Platform' Then the system issues a POST to /sms/segments endpoint And the payload includes segment name and list of phone numbers in E.164 format And the request is authenticated via OAuth2 and returns HTTP 201 Created And the response segment_id is stored in the system

Schedule Weekly Segment Exports

Given the shop owner configures a weekly export schedule for Mondays at 08:00 AM When the scheduled time arrives Then the system automatically exports the high-loyalty segment as a CSV file And emails the file as an attachment to the shop owner's registered email address And a log entry is created with timestamp and export status 'Success'

PromoPulse VIP

Delivers personalized promotions to VIP customers based on their in-store movement patterns and past purchases, boosting conversion rates and nurturing loyalty.

Requirements

VIP Customer Identification

"As a shop owner, I want the system to identify VIP customers as they enter or move within the store so that I can target them with personalized offers and improve engagement."

Description

The system must accurately identify VIP customers in real-time by integrating purchase history data with in-store proximity sensors and smartphone identifiers. This ensures that high-value customers are recognized immediately upon entry or movement within the store, allowing for timely and personalized engagement without requiring additional hardware investments.

Acceptance Criteria

VIP Customer Entry Detection

Given a customer’s smartphone identifier is registered as VIP in purchase history When the smartphone connects to in-store proximity sensors Then the system flags the customer as VIP within 3 seconds

VIP In-Store Movement Recognition

Given a flagged VIP moves between defined store zones When the proximity sensor detects the smartphone identifier in each zone Then the system logs the movement path with timestamps for each zone entry

VIP Profile Data Retrieval

Given a VIP customer is identified in real-time When the system queries the purchase history database Then the system retrieves and displays the customer’s last five purchases within 2 seconds

VIP Promotion Notification Trigger

Given a VIP customer enters a promotional zone When the system confirms VIP status and zone entry Then an appropriate personalized promotion is pushed to the smartphone within 5 seconds

VIP Identification Accuracy Reporting

Given the system processes real-time identification events When the system cross-references detected identifiers with VIP records Then system reports an accuracy rate of at least 99% over 1,000 entry events

Movement Pattern Analysis

"As a shop owner, I want to see real-time movement heatmaps and dwell times of VIP customers so that I can understand their behavior and tailor promotions accordingly."

Description

The feature analyzes VIP customers’ in-store movement patterns and dwell times using real-time heatmap data. It identifies areas where VIPs spend the most time and highlights zones with low engagement. This insight allows shop owners to refine product placement and target promotions dynamically based on actual customer behavior.

Acceptance Criteria

Identification of High Engagement Zones

Given real-time heatmap data for a specific VIP session, when the analysis runs, then the system identifies and lists the top three store zones where the VIP’s dwell time exceeds 120 seconds.

Detection of Low Engagement Areas

Given VIP session heatmap data, when the analysis completes, then the system flags any zone where the VIP’s average dwell time is below 30 seconds during the session.

Personalized Promotion Suggestion Generation

Given the identified high and low engagement zones for a VIP session, when the analysis results are displayed, then the system provides at least two personalized promotion suggestions per high-engagement zone based on the customer’s past purchase history.

Real-Time Heatmap Update

Given incoming movement data, when a VIP customer remains in the store, then the heatmap and dwell time metrics update on the dashboard within 30 seconds of data capture.

Heatmap Data Export

Given a selected VIP session and analysis results, when the shop owner requests data export, then the system generates and downloads a CSV file containing zone identifiers, dwell times, and engagement flags for all zones.

Personalized Promotion Algorithm

"As a VIP customer, I want to receive offers that match my shopping patterns and past purchases so that I get relevant and appealing promotions."

Description

Develop an intelligent algorithm that combines VIP customers’ past purchase history and current movement patterns to generate tailored promotional offers. The algorithm weighs product affinity, seasonal trends, and real-time engagement metrics to ensure that offers are both relevant and timely, boosting likelihood of conversion.

Acceptance Criteria

VIP Customer Profile Recognition

Given a VIP customer enters the store and enables Bluetooth/Wi-Fi, When the system detects their device signal, Then it must retrieve and load the customer's past purchase history within 1 second with at least 99% matching accuracy.

Real-time Movement Tracking

Given a recognized VIP customer is moving through the store, When they enter a new store zone, Then the system logs the zone entry timestamp and updates the movement heatmap in real-time within 500 milliseconds.

Promotion Generation After Dwell Time

Given a VIP customer dwells in a product zone for more than 60 seconds, When the dwell threshold is reached, Then the algorithm generates at least one tailored promotion based on product affinity score, seasonal trends, and current engagement context within 2 seconds.

Seasonal Trend Integration

Given the current date falls within a defined seasonal promotion period, When generating promotions for a VIP customer, Then the algorithm weights seasonal trend factors by a minimum of 20% influence on the final offer selection.

Promotion Delivery Confirmation

Given a promotion is generated for a VIP customer, When the system pushes the offer to the customer's mobile device, Then the customer receives and views the promotion within 3 seconds and the system logs the delivery status as "Delivered and Viewed".

Promotion Delivery Mechanism

"As a VIP customer, I want to receive timely push notifications with personalized offers when I am in an area of interest so that I don't miss relevant promotions."

Description

Implement an in-app push notification system that delivers personalized promotions to VIP customers’ smartphones based on their current location in the store. Notifications should trigger when customers enter designated promotional zones, ensuring messages are contextually relevant and delivered at optimal moments to maximize conversion.

Acceptance Criteria

Entry Into Promotional Zone Triggers Notification

Given a VIP customer with location tracking enabled enters a promotional zone When the system detects the customer’s device in the zone Then it sends a push notification within 5 seconds containing the correct promotion for that zone

No Notification for Non-VIP Customers

Given a non-VIP customer enters a promotional zone When the system detects their device in the zone Then no push notification is sent

Notification Suppressed if Push Permissions Disabled

Given a VIP customer who has disabled push notifications enters a promotional zone When the system detects their device in the zone Then the system does not send a push notification and logs the event with reason 'Push Disabled'

Rate Limiting of Promotional Notifications

Given a VIP customer enters multiple promotional zones within 10 minutes When additional triggers occur Then the system ensures only one notification is sent per customer per 10-minute interval

Notification Interaction Opens Promotion Detail

Given a VIP customer receives a push notification about a promotion When the customer taps the notification Then the app opens directly to the corresponding promotion detail page

Campaign Management Dashboard

"As a shop owner, I want to configure and schedule VIP promotion campaigns from a dashboard so that I can manage offers and control timing easily."

Description

Provide shop owners with an intuitive dashboard to create, configure, and schedule VIP promotion campaigns. The dashboard should allow easy selection of target products, offer parameters, time windows, and customer segments, alongside visual controls for campaign activation and real-time monitoring of campaign status.

Acceptance Criteria

Valid Campaign Creation

Given the shop owner has entered a campaign name, selected target products, set offer parameters (discount type and value), chosen a valid start and end time, and defined customer segments; When the owner clicks 'Save' and 'Activate'; Then the system shall persist the campaign configuration, display it in the campaign list with status 'Scheduled', and confirm creation with a success message.

Campaign Activation Scheduling

Given a campaign with a future start time; When the scheduled start time is reached; Then the system shall automatically change the campaign status to 'Active', trigger the promotion distribution to VIP customers per segment, and log the activation timestamp.

Real-time Metrics Update

Given an active campaign; When the shop owner views or refreshes the dashboard; Then the system shall display live updates for VIP reach count, offer redemptions, and remaining budget, refreshing automatically every 5 seconds without a full page reload.

Campaign Editing

Given an existing campaign in 'Scheduled' or 'Active' status; When the shop owner modifies offer parameters or schedule and clicks 'Save'; Then the system shall validate the changes, apply them without losing prior configuration, update the campaign status accordingly, and record the change in the campaign history log.

Validation of Campaign Inputs

Given any attempt to save a campaign with missing or invalid inputs (e.g., no target products, end time before start time, or undefined customer segments); When the owner clicks 'Save'; Then the system shall display clear, field-specific error messages and prevent saving until all inputs are valid.

Performance Tracking and Reporting

"As a shop owner, I want to view reports on promotion performance and customer redemption metrics so that I can assess ROI and optimize future campaigns."

Description

Build analytics tools that track campaign metrics such as push notification open rates, offer redemption rates, and incremental sales attributed to VIP promotions. Reports should be available in the dashboard, enabling shop owners to evaluate ROI, compare campaign performance, and derive insights for continuous improvement.

Acceptance Criteria

Viewing Campaign Metrics Dashboard

Given a shop owner selects a VIP promotion campaign on the dashboard When the dashboard loads Then detailed metrics including push notification open rates, offer redemption rates, and incremental sales are displayed within 5 seconds

Push Notification Open Rate Tracking

Given a push notification is sent to VIP customers When 24 hours have passed Then the dashboard shows the percentage of notifications opened out of total sent with a timestamp and device breakdown

Offer Redemption Rate Reporting

Given an offer promotion is active When customers redeem the offer Then the dashboard updates in real-time to display the total redemptions and redemption rate as a percentage of notifications opened

Incremental Sales Attribution

Given VIP promotions run over a defined period When sales data is collected Then the system attributes incremental sales by comparing pre- and post-campaign revenue and displays the difference in currency and percentage

Campaign Performance Comparison

Given multiple VIP campaigns exist When the shop owner selects two or more campaigns to compare Then the dashboard displays side-by-side metrics for open rates, redemption rates, and incremental sales with highlighting for top-performing metrics

Report Data Export

Given the shop owner views any campaign report When they choose the export option Then the system generates a CSV file containing all displayed metrics within 5 seconds and prompts a download

VIP PathView

Generates detailed visualizations of VIP visitor pathways, revealing their favored routes and product zones to optimize store layouts for top-tier customers.

Requirements

VIP CRM Integration

"As a shop owner, I want the system to automatically recognize my VIP customers from our existing CRM so that I can accurately track their in-store movement without manual input."

Description

Implement seamless integration with the shop owner’s CRM or loyalty system to automatically identify and tag VIP customers by their linked identifiers (e.g., phone numbers, loyalty card IDs) when they enter the store. This integration ensures accurate and real-time recognition of VIPs without requiring manual check-ins, enabling the system to capture their movement patterns and personalize insights based on their status.

Acceptance Criteria

VIP Customer Entry Recognition

Given a customer’s phone number or loyalty card ID matches a VIP record in the CRM When they enter the store and connect via Heatway Then the system automatically tags them as a VIP in real time and logs their entry event with a VIP badge in the PathView dashboard

VIP Path Visualization Activation

Given a tagged VIP is moving through defined store zones When their location updates are received Then the system generates and displays the VIP PathView heatmap within two minutes of their entry

VIP Exit Detection and CRM Sync

Given a tagged VIP’s session ends when they exit the store When the system detects loss of signal or session timeout Then the system logs the exit timestamp and movement summary back to the CRM within one minute

Handling Multiple VIP Identifiers

Given a customer presents multiple linked VIP identifiers (phone number, loyalty card ID) When either identifier is detected by Heatway Then the system resolves both to a single CRM VIP profile without creating duplicates

CRM Unavailable Fallback Mode

Given the CRM service becomes unreachable When a VIP enters the store Then Heatway queues their identification request locally and synchronizes all queued VIP events to the CRM within five minutes of reconnection

Real-Time Path Data Capture

"As a store manager, I want VIP location data captured and updated in real time so that I can immediately see where top customers are moving and adjust the store layout on the fly."

Description

Develop a robust data ingestion pipeline that collects and streams smartphone location pings of identified VIPs in real time. Ensure low-latency processing, data deduplication, and privacy compliance. This capability is critical to generate up-to-the-second path visualizations and enable timely decision-making.

Acceptance Criteria

Real-Time Data Ingestion

Given VIP devices are present in the store, when their location pings are sent, then the data ingestion pipeline captures and records each ping within 1 second of receipt.

Low-Latency Streaming Verification

Given the ingestion pipeline consumes pings, when streaming data to the visualization engine, then end-to-end latency does not exceed 2 seconds for 99% of pings.

Data Deduplication Accuracy

Given duplicate location pings from the same device within 500ms, when processed, then duplicates are identified and only one record is stored in the database.

Privacy Compliance Enforcement

Given incoming location data includes personal identifiers, when data is ingested, then all personal identifiers are anonymized according to GDPR standards and no raw personal data is stored.

System Scalability Under Load

Given 1000 VIP pings per second, when the pipeline is under this load, then it processes at least 95% of pings within the latency SLA without data loss.

VIP Path Heatmap Overlay

"As a shop owner, I want a visual heatmap of VIP pathways so that I can quickly understand their movement patterns and optimize product placement accordingly."

Description

Create a dynamic heatmap visualization layer that plots the aggregated paths of VIP visitors over the store blueprint. Use color gradients and weighted paths to highlight high-traffic routes and dwell zones. This feature allows shop owners to intuitively identify popular walkways and product areas favored by VIPs.

Acceptance Criteria

Generating Heatmap Overlay on Store Blueprint Load

Given a store blueprint is successfully uploaded, When the VIP Path Heatmap Overlay feature is activated for a selected date range, Then the system must display an aggregated heatmap layer of VIP visitor paths over the blueprint within 3 seconds.

Real-time VIP Path Tracking and Overlay Update

Given VIP visitors enter the store and move through zones, When new location data is received, Then the overlay must update the heatmap visualization in real time (within 5 seconds) reflecting current VIP routes.

Accurate Color Gradient Representation for Traffic Density

Given aggregated VIP path frequency data across the store, When rendering the heatmap overlay, Then high-traffic routes must be displayed in red and low-traffic routes in blue with a continuous gradient scale accurately reflecting relative densities.

Detection and Highlighting of High-Dwell Zones

Given VIP visitor dwell times are recorded per zone, When a zone’s average dwell time exceeds the configured threshold, Then that zone must be highlighted with a distinctive border and shaded area on the overlay.

Exporting and Sharing Heatmap Overlays

Given a heatmap overlay is generated, When the shop owner selects export, Then the system must provide a downloadable PNG file and a shareable URL link for the current overlay configuration.

Custom Zone Analysis and Filtering

"As a shop owner, I want to filter VIP path data by custom zones so that I can analyze performance of key product areas and make targeted layout adjustments."

Description

Enable owners to define and name custom product zones on the store map and filter the VIP path visualizations by these zones. Provide metrics such as average dwell time, entry and exit counts, and most-traversed routes per zone. This empowers owners to focus analysis on specific display areas and actionable insights to boost sales.

Acceptance Criteria

Define and Name a Custom Zone

Given the shop owner is on the store map customization page When they draw a new polygon or select a rectangular area on the map and enter a unique zone name Then the new zone is saved and visible in the zone list with the specified name.

Filter VIP Paths by Custom Zone

Given one or more custom zones exist When the shop owner selects a zone filter in the VIP PathView settings Then only VIP visitor paths that intersect or traverse the selected zone are displayed on the heatmap.

Display Zone Metrics

Given a selected custom zone When the shop owner views the zone analytics panel Then the dashboard shows average dwell time, total entry count, and total exit count for that zone, with values updated within 5 seconds of data refresh.

Highlight Most-Traversed Routes in Zone

Given VIP paths filtered by a selected zone When the shop owner toggles 'Show Top Routes' Then the system highlights the top three most-traversed routes through that zone, ranked by frequency, and displays their percentage usage.

Persist Custom Zones Across Sessions

Given a shop owner creates or edits a custom zone When the shop owner refreshes the page or logs out and back in Then all custom zones and their names remain saved and available without data loss.

Exportable VIP Path Reports

"As a business analyst, I want to export VIP path reports so that I can share performance insights with my team and track changes over time."

Description

Offer the ability to export detailed VIP path analytics and visualizations as PDF or CSV reports. Include summary metrics, heatmap snapshots, and zone-specific statistics. This functionality supports sharing insights with stakeholders and maintaining offline records for trend analysis.

Acceptance Criteria

Generate VIP Path PDF Report

Given a completed VIP PathView analysis, when the user selects "Export as PDF," then the system generates and downloads a PDF containing summary metrics, heatmap snapshots, and zone-specific statistics formatted and paginated correctly.

Generate VIP Path CSV Report

Given a completed VIP PathView analysis, when the user selects "Export as CSV," then the system generates and downloads a CSV file with headers and rows detailing path ID, timestamp, zone name, duration in zone, and summary metrics.

Include Summary Metrics in Reports

The exported report includes a summary section listing total VIP visits, average session duration, top three visited zones, and overall path completion rate displayed prominently at the beginning of the document.

Include Heatmap Snapshots

The exported report contains high-resolution heatmap images overlaid on the store layout, annotated with timestamps and zone labels, ensuring clarity and legibility in both PDF and referenced image links in CSV.

Offline Stakeholder Sharing

When the exported PDF/CSV report is opened on a device without Heatway installed, all data, images, and formatting are intact and viewable without missing assets or errors.

Real-Time VIP Path Alerts

"As a shop owner, I want to receive real-time alerts when VIPs enter low-traffic areas so that I can intervene to guide them toward high-value products."

Description

Implement configurable alerts that notify shop owners via push notification or email when VIPs deviate into underperforming zones or when abnormal dwell patterns occur. These alerts help owners proactively engage with VIPs or rearrange displays to capture attention.

Acceptance Criteria

VIP Enters Underperforming Zone

Given a tracked VIP enters a zone with sales performance below the configured threshold, when the VIP’s location is detected in that zone, then a push notification is sent to the shop owner’s mobile device within 5 seconds containing the VIP ID and zone name.

VIP Abnormal Dwell Time Exceeds Threshold

Given a tracked VIP remains in any single zone longer than the configured dwell time threshold, when the threshold is exceeded, then an email alert is generated and sent to the shop owner within 1 minute detailing the zone and dwell duration.

VIP Path Deviation Alert

Given a tracked VIP deviates from their historical path by entering an unfrequented zone, when this deviation is confirmed over two consecutive visits, then a push notification is sent summarizing the deviation and suggesting display optimization.

VIP Re-Entry into Previously Underperforming Zone

Given a tracked VIP re-enters a zone previously flagged as underperforming during the same visit session, when the VIP re-enters that zone, then an email alert is generated within 2 minutes highlighting repeat interest in that zone.

Multiple Abnormal Patterns in Single Session

Given a tracked VIP exhibits two or more abnormal patterns (e.g., extended dwell time, zone deviation) within a single session, when the second pattern is detected, then a consolidated push notification summarizing all detected anomalies for that VIP is sent within 5 minutes.

Elite Arrival Alerts

Sends real-time notifications to staff when a VIP enters the store, including customer profile snippets and tailored greeting or offer suggestions for exceptional service.

Requirements

VIP Detection Trigger

"As a store associate, I want to be notified instantly when a VIP customer walks in so that I can greet them personally and provide exceptional service."

Description

Implement a real-time VIP identification module that uses the smartphone’s camera and motion sensors to detect when a Registered VIP customer enters the store. The module must continuously scan entrypoints, recognize VIP profiles against a secure database, and trigger downstream processes within one second of detection. This requirement ensures the system can reliably and promptly identify high-value customers without additional hardware investments.

Acceptance Criteria

Main Entrance VIP Entry Detection

Given the smartphone camera is monitoring the main entrance and the VIP is registered in the database, When the VIP crosses the entrance threshold, Then the system must identify the VIP within 1 second and trigger the Elite Arrival Alert notification to staff with the customer's profile snippet and greeting suggestion.

Concurrent Multi-Customer Entry

Given multiple individuals enter the store simultaneously, including at least one registered VIP, When the VIP appears in the camera frame of the entrypoint, Then the system must accurately identify the VIP within 1 second without false positives or misidentifications of non-VIP customers.

Low-Light Environment Detection

Given store lighting conditions drop below standard illumination levels, When a registered VIP enters under suboptimal lighting, Then the system must still correctly identify the VIP within 1 second with at least 95% confidence and send the notification.

Motion Sensor Activation Synchronization

Given the motion sensor detects movement at the entrypoint, When the sensor triggers the camera activation, Then the system must synchronize sensor data with the video feed and identify any registered VIP within 1 second of entry.

Offline to Online Transition Detection

Given a temporary network outage occurs during a VIP entry, When the system reconnects to the network, Then it must retroactively process any buffered detection events and send the Elite Arrival Alert for the VIP within 1 second of reconnection.

Notification Delivery System

"As a store manager, I want the system to send immediate alerts to my team’s smartphones when a VIP arrives so that we can coordinate and respond quickly."

Description

Develop a robust, low-latency push-notification infrastructure that delivers VIP arrival alerts to all relevant staff mobile devices. The system must support group and individual targeting, handle network interruptions gracefully, and guarantee delivery within two seconds of the trigger event. This ensures staff receive timely alerts regardless of device state or connectivity.

Acceptance Criteria

Instant VIP Entry Notification

Given a VIP customer enters the store, When the system detects the entry, Then a push notification must be delivered to all relevant staff devices within two seconds of the trigger event.

Resilient Delivery During Network Interruptions

Given intermittent network connectivity on one or more staff devices, When a VIP arrival notification is triggered, Then the system must queue the notification and automatically retry delivery until the device reconnects, ensuring delivery within ten seconds of reconnection.

Group Targeting Accuracy

Given a VIP arrival event with staff groups configured, When the notification is sent, Then only devices belonging to the predefined ‘floor staff’ group receive the notification and no other groups are targeted.

Offline Device Handling

Given an individual staff device is offline at the moment of notification, When the device reconnects within two minutes, Then the system must deliver the missed VIP arrival notification immediately upon reconnection.

High-volume Notification Performance

Given one hundred VIP arrival events within a rolling one-minute window, When notifications are processed, Then the system must maintain an average delivery latency of two seconds or less with a failure rate below 0.1%.

Customer Profile Retrieval

"As a sales associate, I want to view a VIP’s basic profile information immediately upon their arrival so that I can tailor my greeting and service to their preferences."

Description

Build an API service that retrieves essential VIP customer profile snippets—such as name, photo, purchase history, and personal preferences—from the CRM database in real time. The service should return data in under 300ms and include configurable privacy filters to display only approved information. This requirement enables staff to access relevant context instantly.

Acceptance Criteria

VIP Profile Data Fetch on Customer Entry

Given a VIP customer enters the store and their device is detected, when the Elite Arrival Alerts service invokes the Customer Profile Retrieval API, then the API must return the customer's name, photo URL, last five purchases, and personal preferences within 300 milliseconds with an HTTP 200 response.

Privacy Filter Enforcement

Given privacy filters are configured to hide purchase history, when the API is called for a valid VIP customer, then the response must exclude the purchase history field while including all other approved fields and return HTTP 200.

CRM Service Unavailability Handling

Given the CRM database is unavailable or the API call to CRM exceeds 300 milliseconds, when the Customer Profile Retrieval API is invoked, then it must return an HTTP 504 timeout with an error code 'CRM_TIMEOUT' and no profile data.

High Concurrency Performance

When 100 concurrent valid API requests are made for different VIP customers, then at least 95% of responses must complete within 300 milliseconds with HTTP 200, and error rate must remain below 1%.

Response Schema Compliance

Given any successful API response, when validating against the defined OpenAPI JSON schema, then the response must conform exactly to the schema with no missing required fields or unauthorized additional fields.

Greeting and Offer Suggestion Engine

"As a store associate, I want automated greeting and offer suggestions for VIPs so that I can engage them with relevant promotions and personalized messages."

Description

Integrate a recommendation engine that generates personalized greeting scripts and suggested offers based on the VIP’s past purchases, preferences, and current promotions. The engine must produce at least three actionable suggestions within 500ms of profile retrieval. This enhances staff-customer interactions and drives upsell opportunities.

Acceptance Criteria

VIP Profile Retrieval Speed

Given a VIP entry is detected, when the system retrieves the VIP’s past purchase and preference data, then it must produce at least three personalized greeting scripts and offer suggestions within 500ms.

Personalized Greeting Accuracy

Given a VIP’s historical purchase and preference data, when the greeting engine generates greeting scripts, then at least 80% of generated scripts must reference the VIP’s top two preferences or past purchases correctly.

Relevant Offer Suggestions

Given current in-store promotions and a VIP’s purchase history, when suggestions are generated, then at least one suggested offer must align with an active promotion and two must align with the VIP’s past purchases.

Promotion Integration

Given there are active promotions, when the suggestion engine runs, then all returned offers must reflect current valid promotions without expired or invalid offers.

System Failure Handling

Given the profile retrieval service is unavailable, when the engine attempts to generate suggestions, then the system gracefully degrades by providing a generic greeting and a default promotional offer, and logs the error.

Real-time Notification UI

"As a sales associate, I want an intuitive alert interface that highlights VIP details and recommended actions so that I can respond efficiently."

Description

Design and implement a user interface component in the staff mobile app that displays incoming VIP alerts. The UI must clearly show the VIP’s photo, name, suggested greeting, and offer, with buttons for acknowledgement, snooze, or dismissal. It should be responsive, accessible, and support dark mode. This requirement ensures alerts are actionable and user-friendly.

Acceptance Criteria

VIP Alert Display on Idle Screen

Given the app is in any idle state, when a VIP enters the store, then a notification card appears immediately at the top of the screen showing the VIP’s photo, name, suggested greeting, and offer.

Acknowledgement Button Interaction

Given a VIP alert is displayed, when staff taps ‘Acknowledge’, then the alert is marked as read, removed from the active list, and a confirmation message displays.

Snooze Functionality on Alert

Given a VIP alert is displayed, when staff taps ‘Snooze’ with a duration selection, then the alert hides and reappears after the specified duration.

Dismiss Alert Action

Given a VIP alert is displayed, when staff taps ‘Dismiss’, then the alert is permanently cleared from the notification list and logged in the activity history.

Dark Mode Responsiveness

Given the app is in dark mode, when a VIP alert is displayed, then all text, icons, and backgrounds adhere to the dark mode color palette with a minimum 4.5:1 contrast ratio.

Accessibility Compliance for Screen Readers

Given a VIP alert is displayed, then all alert elements have appropriate ARIA labels and can be navigated via screen reader and keyboard.

Notification Logging and Analytics

"As a store manager, I want to track VIP alert metrics and staff response performance so that I can evaluate service quality and improve team training."

Description

Create a logging and analytics system to record all VIP arrival alerts, staff acknowledgments, response times, and outcomes (e.g., promotions accepted). The system should generate reports and dashboards for store managers to measure engagement effectiveness and identify training needs. Data retention policies and compliance with privacy regulations must be enforced.

Acceptance Criteria

VIP Arrival Logged Correctly

Given a VIP enters the store, When the arrival alert is triggered, Then a log entry must be created with timestamp, VIP ID, and alert details; And the log entry must be stored in the analytics database within 1 second.

Staff Acknowledgment Recorded

Given a staff member acknowledges a VIP alert, When the acknowledgment action is completed, Then the system must record staff ID, acknowledgment timestamp, and associated VIP alert ID; And the acknowledgment must be visible in real-time on the manager dashboard.

Response Time Captured

Given a VIP alert has been sent, When staff acknowledgment occurs, Then the system must calculate response time as the difference between alert timestamp and acknowledgment timestamp; And the response time metric must be accurate within 100ms.

Promotion Outcome Tracked

Given a promotion suggestion is sent with the VIP alert, When the VIP accepts or declines the promotion, Then the outcome must be recorded with VIP ID, promotion ID, acceptance status, and timestamp; And the outcome record must be included in the analytics reports.

Analytics Dashboard Generation

Given at least one week of logged alert data, When the manager views the analytics dashboard, Then charts for alert counts, average response times, acknowledgment rates, and promotion outcomes must be displayed; And reports must be downloadable in PDF and CSV format.

Reward Routes

Maps custom in-store journeys for VIPs, guiding them through high-margin and relevant products based on their known preferences to maximize upselling opportunities.

Requirements

VIP Profile Integration

"As a store manager, I want to import VIP customer profiles and preferences so that Reward Routes can tailor personalized in-store journeys to each VIP’s interests."

Description

Enable importing, managing, and syncing VIP customer profiles—including preferences, purchase history, and behavior data—so that the system can generate personalized in-store journeys tailored to each VIP’s known interests.

Acceptance Criteria

CSV VIP Profile Import Scenario

Given an admin has a valid CSV file with VIP profiles including name, email, preferences, and purchase history When the admin uploads the file via the VIP import interface Then all valid profiles are imported into the system, duplicate records are identified and flagged, and any errors are summarized in a report

Real-Time Profile Sync Scenario

Given a VIP profile is updated in the external CRM system When the CRM sends a webhook notification to Heatway Then the corresponding VIP profile in Heatway is updated within 5 seconds and data consistency is verified

VIP Preference Update Scenario

Given a VIP’s preference is modified in the admin dashboard When the admin clicks Save Preferences Then the updated preference values are persisted to the database and immediately available for reward route generation

Purchase History Sync Scenario

Given new VIP transactions are recorded in the connected POS system When the hourly batch sync job executes Then Heatway imports the latest transactions, updates the VIP’s purchase history, and reflects these changes in the next route recommendation

Profile Data Privacy Consent Scenario

Given VIP profiles include a consent flag for data usage When profiles are imported or synced Then only profiles with valid consent flags are stored in Heatway, and records lacking consent are rejected with an appropriate log entry

Route Designer Interface

"As a store manager, I want a visual route designer so that I can easily create and adjust Reward Routes to highlight targeted products."

Description

Provide an intuitive drag-and-drop interface for store managers to design, customize, and visualize in-store Reward Routes, allowing placement of waypoints at high-margin or relevant products and adjustment of path flow to optimize customer engagement.

Acceptance Criteria

Adding a New Waypoint

Given the Route Designer Interface is open When the user drags a product icon onto the canvas Then a waypoint is created at the drop location with appropriate product metadata

Reordering Waypoints

Given at least two waypoints exist on the canvas When the user drags one waypoint before or after another Then the waypoint sequence updates and the visual path reconnects accordingly

Adjusting Route Flow

Given a sequence of waypoints displayed When the user adjusts the connecting arrows to change direction or path Then the route preview updates to reflect the new flow with correct distance indicators

Saving a Custom Route

Given a fully configured route with at least one waypoint When the user clicks the Save button and provides a route name Then the route is persisted and appears in the Route Library with correct metadata

Handling Invalid Placement

Given the user attempts to drop a waypoint outside the store boundary When the drop action occurs Then the interface displays an error message and prevents placement

Dynamic Route Optimization

"As a store manager, I want the system to dynamically optimize VIP routes based on current store traffic so that customers enjoy a smooth experience and discover key products."

Description

Implement algorithms that adjust predefined Reward Routes in real time based on live foot traffic heatmap data and store conditions, ensuring VIP customers avoid congested areas and follow the most efficient path.

Acceptance Criteria

Initial Route Adjustment in Smooth Traffic

Given a VIP customer starts their journey and live heatmap shows evenly distributed foot traffic, when the dynamic optimization algorithm runs, then the predefined Reward Route remains unchanged and is confirmed as optimal within 5 seconds.

Route Recalibration During Sudden Congestion

Given a VIP customer is midway through a Reward Route and heatmap data indicates a new congestion zone ahead, when the algorithm detects a 20% increase in foot traffic density, then an alternative route is generated that avoids the congested area and updates within 3 seconds.

Customer Wait Time Minimization

Given a VIP customer arrives at an intersection of routes, when multiple route options are available, then the algorithm calculates and selects the path with the shortest estimated traversal time based on live heatmap speeds and store layout, showing reroute within 2 seconds.

Real-Time Rerouting During Store Event

Given a flash sale event area suddenly draws high foot traffic, when live data shows a 50% spike in density around the flash sale zone, then the algorithm dynamically reroutes VIP customers away from the event area to maintain an average walking speed of at least 1.2 m/s.

Edge Case: Heatmap Data Latency Handling

Given transient delays in heatmap data feed exceeding 1 second, when the algorithm does not receive fresh data, then it continues to guide the VIP customer along the last known optimal route and triggers a data freshness alert to the system within 1 second.

Real-time Navigation Guidance

"As a VIP customer, I want real-time navigation guidance on my phone so that I can easily follow the recommended route and discover highlighted products."

Description

Deliver turn-by-turn, in-store navigation instructions on VIP customers’ smartphones, overlaid on the heatmap view, to guide them along their personalized Reward Route and draw attention to featured products.

Acceptance Criteria

VIP Route Initiation

Given a VIP customer launches the Reward Routes feature and enters the store When they tap 'Start Navigation' Then the turn-by-turn navigation overlay appears on the heatmap view within 2 seconds and displays the first direction arrow

Overlay Position Accuracy

Given the VIP customer is walking along the route When the smartphone’s location updates are received Then the overlay arrow and path on the heatmap realign within 500ms to reflect the correct direction and position

Weak Connectivity Navigation Continuity

Given the customer’s device experiences signal loss or weak connectivity When the connection drops below threshold Then the system displays the last-known instruction, queues upcoming steps locally, and seamlessly resumes real-time guidance upon reconnection without errors

Route Performance Analytics

"As a store manager, I want analytics on Reward Route performance so that I can measure engagement and improve future route designs."

Description

Offer a dashboard with metrics on Reward Route engagement, completion rates, time spent at waypoints, and upsell conversion, enabling store managers to evaluate route effectiveness and refine strategies.

Acceptance Criteria

Dashboard Data Loading Successfully

Given the store manager navigates to the Route Performance Analytics dashboard and selects a date range, When the dashboard loads, Then all key metrics (engagement, completion rate, time on waypoints, upsell conversion) are displayed within 5 seconds without errors.

Viewing Engagement Metrics

The dashboard displays the total number of route engagements and unique VIP starts; metrics match database records for the selected period.

Analyzing Route Completion Rates

The dashboard shows completion rate per route calculated as completed routes divided by started routes, with values accurate to within ±1%.

Reviewing Time Spent at Waypoints

The dashboard lists average and median time spent at each waypoint in a sortable table, with values matching raw location logs within ±5 seconds.

Monitoring Upsell Conversion

The dashboard displays upsell conversion rate defined as upsell purchases divided by completed routes; clicking the metric drills down to individual upsell transactions.

Product Ideas

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

Blindspot Beacon

Highlights under-visited shelves with glowing overlays, guiding owners to reposition products and boost unseen sales.

Idea

PathPulse Alerts

Sends instant alerts when foot traffic surges in any aisle, enabling staff to engage customers at peak interest zones.

Idea

Sale Spike Predictor

Forecasts busiest store hours and hot zones using past heatmaps, helping owners schedule staff and tweak displays proactively.

Idea

Layout Lab

Simulates new fixture placements on virtual floorplans, showing projected traffic shifts before real-world changes.

Idea

Heatway Harmony

Syncs heatmaps with POS data to link movement patterns with sales conversions, revealing high-value zones instantly.

Idea

VIP Viewfinder

Identifies repeat visitors by their phone trajectories, prompting personalized offers that increase loyalty and spend.

Idea

Press Coverage

Imagined press coverage for this groundbreaking product concept.

Heatway Launches Instant Smartphone Heatmap Analytics to Revolutionize Retail Floor Insights

Imagined Press Article

SAN FRANCISCO, CA – 2025-06-28 – Heatway today announced the official launch of its groundbreaking smartphone-based foot traffic heatmap platform for independent retailers. By transforming any modern mobile device into a powerful in-store analytics tool, Heatway empowers shop owners to visualize customer movement patterns in real time, identify blind spots in product displays, and make data-driven layout decisions that drive sales without the need for expensive hardware or complex installations. In a retail landscape where every square foot of floor space represents potential revenue, Heatway’s intuitive Blindspot Meter delivers a continuous visibility score for each shelf segment, immediately highlighting under-visited zones. The platform’s Beacon Alerts feature then sends push notifications when traffic falls below customized thresholds, giving staff the opportunity to reposition displays or engage customers before opportunities slip away. Combined with AI-driven Layout Suggestions, shop owners receive targeted recommendations on exactly which products to move and where to place them for maximum exposure. “Our goal was to democratize in-store analytics,” said Alex Johnson, CEO and co-founder of Heatway. “Too many independent retailers can’t justify costly hardware installations or complicated systems. Heatway flips that model on its head by putting powerful, actionable insights directly in the hands of store owners through technology they already have in their pockets.” Since launching its beta program earlier this year, Heatway has onboarded more than 500 independent shops across the United States, from boutique clothing stores in Portland to artisanal home décor outlets in Atlanta. Early adopters report an average 18% uplift in monthly sales as they quickly diagnose and correct layout inefficiencies in under an hour. One boutique owner noted that by shifting a high-margin accessory line into a newly identified traffic hotspot, she boosted accessory sales by 25% within two weeks. Heatway’s modular design means retailers can layer on advanced capabilities as their analytics needs evolve. The Time-Lapse Viewer offers a chronological replay of heatmaps over days or weeks, enabling owners to measure the impact of display changes over time. Overlay Customizer allows full branding control by customizing heatmap colors, intensities, and category filters. And SurgeSignal delivers real-time surge alerts pinpointing customer concentration with aisle location, visit duration metrics, and intensity flags—so teams can immediately address hot zones or deploy staff where they are most needed. “Traditional in-store analytics solutions can take months to install and thousands of dollars to operate,” said Johnson. “Heatway goes from zero to live insights in under five minutes with no sensors, no beacons, and no installation crew. It’s plug-and-play simplicity paired with enterprise-grade intelligence.” Looking ahead, Heatway plans to introduce predictive analytics modules that forecast upcoming foot traffic surges and revenue potential based on historical patterns and promotional calendars. These enhancements will allow shop owners to schedule staff shifts, time marketing campaigns, and arrange displays proactively—transforming reactive retail operations into forward-thinking, optimized experiences. About Heatway Heatway is a San Francisco-based technology company founded in 2024 with the mission of making advanced in-store analytics accessible to all retailers. By leveraging smartphone cameras and AI-driven heatmap technology, Heatway delivers real-time foot traffic insights, blindspot identification, and layout optimization recommendations—without the need for hardware installations or specialized equipment. The platform is trusted by hundreds of independent stores, franchises, and pop-up operators nationwide. Media Contact: Emma Davis Director of Communications, Heatway press@heatway.com +1 (415) 555-0123

Heatway Introduces PredictPulse AI Forecasting to Anticipate Retail Traffic Surges

Imagined Press Article

SAN FRANCISCO, CA – 2025-06-28 – Heatway, the leading provider of smartphone-based foot traffic analytics, today unveiled PredictPulse, an advanced AI-driven forecasting module that predicts upcoming store traffic surges down to the hour. By integrating historical heatmap data, promotional schedules, and regional trends, PredictPulse enables retail operators—from independent boutiques to multi-unit franchises—to proactively optimize staffing, inventory displays, and marketing promotions before peak periods arrive. With foot traffic often dictating sales opportunities, retailers who can anticipate busy intervals gain a critical competitive edge. PredictPulse uses machine learning algorithms to analyze weeks of accumulated heatmap recordings captured through Heatway’s Blindspot Meter and SurgeSignal features. The system then generates easy-to-read forecast charts indicating expected traffic volumes across different aisles and time blocks, complete with confidence scores and suggested operational responses. "Anticipating when and where customers will flow through your store allows you to allocate resources efficiently and enhance the shopping experience," said Priya Rao, CTO and co-founder of Heatway. "PredictPulse transforms retrospective analytics into forward-looking insights, so our users can reduce stockouts during peak times, ensure proper staffing, and align promotions for maximum impact." Early tester data shows that retailers leveraging PredictPulse achieve an average 22% increase in conversion rates during forecasted busy windows, primarily by pre-positioning high-margin products and making strategic staffing adjustments. In one multi-location clothing franchise, managers used PredictPulse forecasts to launch targeted mid-week flash promotions aligned with predicted traffic spikes, resulting in a 30% increase in upsell orders compared to unforecasted days. PredictPulse integrates seamlessly with Heatway’s existing feature suite: • Time-Lapse Viewer: Validates forecast accuracy by comparing predicted surges with actual historical patterns. • SurgeSignal: Converts forecast alerts into real-time notifications, prompting preemptive display or staffing adjustments. • StaffSync: Automates schedule recommendations based on predicted peak hours, ensuring optimum floor coverage without manual planning. • DisplayDrift: Offers AI-driven product positioning tips for upcoming busy zones, recommending which SKUs to highlight and where to place promotional signage. "Retail today demands agility," added Rao. "With PredictPulse, the guesswork is removed. Shop owners and franchise managers can focus on delivering excellent customer experiences instead of scrambling last minute to respond to foot traffic swings. It’s like having a weather forecast—but for shoppers." Heatway’s PredictPulse is available immediately to all existing subscribers and can be activated with a single click from the platform’s Analytics Hub. New users signing up before August 1, 2025, will receive a complimentary three-month trial of PredictPulse to experience the value of forecasting in their stores at no additional cost. About Heatway Heatway transforms smartphones into real-time foot traffic heatmap generators for retailers, delivering actionable analytics to optimize store layouts, displays, and staffing without expensive hardware. Trusted by independent shop owners, franchise strategists, and pop-up operators, Heatway leverages proprietary AI to surface blind spots, forecast traffic surges, and maximize revenue opportunities. Media Contact: Emma Davis Director of Communications, Heatway press@heatway.com +1 (415) 555-0123

Heatway Harmony Integrates POS Data for Unified Foot Traffic and Sales Analytics

Imagined Press Article

SAN FRANCISCO, CA – 2025-06-28 – Heatway today announced the general availability of Heatway Harmony, a seamless integration that overlays point-of-sale transaction data onto real-time foot traffic heatmaps. This groundbreaking capability enables retailers to correlate customer movement patterns with actual sales performance, revealing precise revenue hotspots and conversion blind spots across the entire store environment. By combining Heatway’s existing Revenue Radar and Conversion Correlator features with POS data streams, Heatway Harmony offers an end-to-end analytics solution. Merchants can now visualize which aisles not only receive the most traffic but also deliver the highest average transaction values. Conversely, underperforming high-traffic zones—where footfall fails to translate into revenue—are flagged for immediate investigation. "Our customers have long asked for a unified view that marries in-store behavior with transaction outcomes," said Vanessa Lee, VP of Product at Heatway. "Heatway Harmony answers that need by creating a consolidated dashboard where store managers can see, at a glance, where customers are shopping and spending. This integration unlocks actionable insights that drive smarter merchandising and promotion strategies." Key benefits of Heatway Harmony include: • Real-Time Revenue Overlays: Revenue Radar now displays live sales metrics directly on heatmap gradients, with color intensities representing dollars per square foot for each zone. • Conversion-Focused Heatmaps: ValueView automatically adjusts heatmap weighting to highlight areas where foot traffic correlates most strongly with sales volume and profit margins. • Basket Blend Analytics: Item-level POS synchronization reveals which product adjacencies drive cross-sells and upsells, empowering merchandisers to refine category placements and bundle promotions. • POS Pulse Alerts: Sends instant notifications when high-traffic sections show unexpectedly low sales conversions, prompting staff to engage customers or adjust pricing strategies in real time. Franchise strategists overseeing regional portfolios benefit from cross-store comparisons, allowing them to benchmark high-performing locations and standardize best practices. Pop-up pioneers can quickly assess whether temporary setups generate sufficient conversions relative to visitor counts. Visual merchandisers can verify that eye-catching displays also deliver bottom-line impact, not just footfall. "Heatway Harmony transforms qualitative visitor insights into quantitative revenue intelligence," said Lee. "Store teams can finally answer questions like: Is this new window display driving sales, or simply attracting window-shoppers? Which aisle layout yields the greatest basket size? That combination of heatmap visualization and POS correlation is game-changing for retail operators of all sizes." Heatway Harmony is available immediately to all Heatway Enterprise tier subscribers and can be configured in minutes through the platform’s Integrations Marketplace. Retailers interested in a live demonstration can schedule personalized demos via the Heatway website. About Heatway Heatway is on a mission to democratize in-store analytics by delivering advanced foot traffic and sales conversion insights through intuitive mobile solutions. Founded in 2024 and headquartered in San Francisco, Heatway serves a diverse customer base of small retailers, franchises, and pop-up operators who rely on data-driven decision making to optimize store layouts, staffing, and promotions. Media Contact: Emma Davis Director of Communications, Heatway press@heatway.com +1 (415) 555-0123

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