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AI-Powered Store Heatmap and Customer Behavior Analysis System

dataforest.ai

Retail

Consumer products & services

eCommerce

Information technology

Retail Store Optimization Challenges

The client struggles with inefficient store layouts causing dead zones, inability to measure promotional display effectiveness, suboptimal product placement, and poor customer service in high-traffic areas. These issues lead to lost sales opportunities and reduced customer satisfaction.

About the Client

Leading electronics retailer seeking to optimize store layouts and customer engagement through AI-driven analytics

Store Performance Enhancement Goals

Implement real-time customer movement tracking system
Identify and eliminate store dead zones
Optimize product placement based on customer engagement
Improve customer service in high-traffic areas
Increase overall sales and customer satisfaction metrics

Core System Functionalities

Machine Learning-based customer movement tracking
Face recognition for engagement analysis
Centralized data processing from multiple cameras
Interactive heatmaps showing popular shelves/products
Dead zone detection and reporting
Product placement optimization recommendations

Technology Stack Requirements

Python

Flask

PostgreSQL

MySQL

AWS

System Integration Needs

Point-of-Sale (POS) systems
Existing CRM platform
Inventory management system
Security camera infrastructure

Performance and Security Requirements

Scalability for 100+ store locations
Real-time data processing (sub-500ms latency)
GDPR-compliant data handling
99.9% system availability
Role-based access control dashboard

Expected Business Impact and Performance Metrics

Implementation is projected to deliver 8-10% sales growth through optimized layouts, complete elimination of dead zones, 20% improvement in customer service response times, and 15% increase in inventory turnover. The system will provide predictive analytics for demand forecasting with 88% accuracy and reduce stockouts by 0.9%.