Product Recommendation Engine
A fashion retailer's website showed the same trending products to every visitor, missing opportunities to cross-sell and upsell based on individual preferences.
- 32%
- Higher AOV
- 18%
- Conversion Lift
- 50ms
- Response Time
- 3 mo
- To Delivery
With a catalogue of 15,000+ SKUs, the retailer relied on manual merchandising and static 'best sellers' lists. Customers often couldn't find products they'd love, and cross-sell opportunities at checkout were generic and ineffective.
They needed a real-time recommendation engine that personalized product suggestions based on browse history, purchase patterns, and similar customer behaviour — fast enough to not slow down page loads.
We built a collaborative filtering recommendation engine with real-time scoring that serves personalized suggestions on product pages, cart, and email campaigns. The system returns recommendations in under 50ms and A/B tests different algorithms automatically.
Average order value increased 32%, conversion rates lifted 18%, and the recommendation engine now drives a measurable share of revenue through personalized cross-sell and upsell suggestions.
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