Generic product suggestions led to low engagement and missed cross-sell opportunities.
We replaced a rules-based "popular products" widget with a personalized recommendation engine that adapts to individual browsing patterns and purchase history, serving relevant suggestions in real time.
Client
An online retailer with...
Timeline
12 weeks
Team
2 engineers
Industry
E-commerce
An online retailer was showing the same "popular products" to every visitor regardless of their behavior. Their existing recommendation engine was rules-based and couldn't adapt to individual browsing patterns or purchase history, leaving significant cross-sell and upsell opportunities on the table.
Implemented collaborative filtering using implicit feedback signals — views, cart additions, and purchases
Built real-time session-based recommendations for anonymous visitors who don't have purchase history
Created an A/B testing framework to measure recommendation quality against the existing rules-based system
Added contextual modules like "frequently bought together" and "complete the look" for product detail pages
+18%
Avg Order Value
3.5x
Click-Through Rate
12 wks
Delivered In
Real-time
Personalization
Average order value increased by 18%
Recommendation click-through rate improved from 2% to 7%
Cart abandonment decreased noticeably
Revenue attributed to recommendations grew to a meaningful share of total
“We went from showing everyone the same "trending" section to actually personalizing the experience. The AOV jump was almost immediate once we rolled it out to 100% of traffic.”
— E-commerce Director, Online Retailer
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