E-Commerce Recommendation Engine

An e-commerce store with 5,000+ SKUs shows the same "bestsellers" to every visitor. Static merchandising ignores browsing signals: categories viewed, time on product pages, cart adds/removes, price sensitivity, brand preferences. Conversion sits at 2-3%.

Algolia Recommendations operates with sub-10ms response times. Nosto delivers individualized experiences. Dynamic Yield offers ML-powered strategies with A/B testing. These prove personalization lifts conversion and AOV.

Mid-market merchants face meaningful monthly costs and integration complexity. Many default to Shopify's built-in "you may also like" using simple co-purchase data.

The agent builds real-time behavioral profiles from clickstream data: pages viewed, products examined, time spent, cart activity, price sensitivity, and category affinity. For returning customers, purchase history and reviews layer on top.

Hybrid recommendations: collaborative filtering (similar customers bought these), content-based (similar along style, price, brand dimensions), and contextual signals (time, device, referral). Different weights for different placements: discovery on homepage, complementary on PDP, completion at checkout.

Recommendations update within the session. A visitor shifting from running shoes to hiking boots sees recommendations adapt immediately. Email uses the most recent profile, not stale purchase data.