AI Personalization Engine for Retail Giant

The client is a general merchandise retailer with both physical stores and a substantial e-commerce presence, sitting on years of point-of-sale, web, and app data that had never been unified into a single customer view. However, they faced significant hurdles:

• Data Silos: Online and offline purchase data were never merged, so the same customer looked like two different people to the business.
• Generic UX: Every visitor saw the same homepage products regardless of browsing or purchase history.
• Slow API: The existing search stack was too slow for real-time suggestions at storefront-scale traffic.
• Cold-Start Problem: New shoppers with no purchase history got no useful recommendations at all under the old rules-based system.

We built a custom recommendation engine on top of a newly unified customer data platform:

• Data Lakehouse: Consolidated data from point-of-sale, website, and app into Databricks, creating a single customer view for the first time.
• Collaborative Filtering Models: To suggest "customers who bought X also bought Y" using purchase-pattern similarity.
• Real-Time API: Served recommendations in under 50ms during page loads, fast enough to appear above the fold without a layout shift.
• Cold-Start Fallback: A content-based model kicked in automatically for new visitors until enough behavioral signal was collected.

Personalized product suggestions drove an 8% increase in conversion rate. The "Recommended for You" section became one of the highest-revenue placements on the site, outperforming several long-standing merchandised banners.

The unified customer data platform also became a foundation the marketing team now uses for email segmentation and loyalty program targeting, well beyond the original recommendation-engine scope.