About
The electronics retailer, wanted to improve their sales and customer service by analyzing the flow of people into their stores. We created a system using Machine Learning, image detection, and face recognition. The system tracks visitors' movements and the most viewed shelves and products. This information helps the store to focus on selling popular products and to avoid unpopular ones, ultimately improving the sales process.
Challenge, approach, and impact
Limited visibility into in-store customer behavior
The retailer lacked clear visibility into how customers moved through physical store spaces. Existing data sources did not provide an intuitive way to understand high-traffic zones, movement patterns, or underutilized areas. Without a visual representation of in-store behavior, decisions around layout changes, product placement, and space optimization were largely based on assumptions rather than observable customer flow data.
How we built
Testimonials
Jared D.
Consumer Electronics Retail
“DATAFOREST provides meaningful shopper-behavior Insights. They are very responsive and effective, trying to engineer and offer the best fit solution.“
Team structure
Client team
Jared
CEO
Project stakeholder
The client stakeholders were working closely with the team at Dataforest
Agency team
3 x Data Engineer
Production
2 x Product Manager
Production
