AI Product Recommendation Integrations
Behavior-driven recommendations across five global DTC storefronts.
What we needed to solve.
- Static merchandising rules were under-performing on PDP and cart pages.
- Five storefronts on different theme architectures needed one consistent recommendation layer.
- Recommendation widgets had to load without harming Core Web Vitals.
How it was built.
Unified data layer
Standardized product, collection, and customer events across all five themes so the AI engine received clean, comparable signals.
Behavioral signals
Tracked view, dwell, add-to-cart, and post-purchase patterns to power 'frequently bought together', 'recently viewed', and personalized PDP rails.
Performance-first widgets
Lazy-loaded recommendation sections with skeleton states, deferred JS, and edge-cached responses to protect LCP.
The toolkit.
What's live.
The outcome.
Driven by complete-the-look and cart cross-sells.
Higher click-through into related products and bundles.
Site-wide uplift across the five storefronts.
Maintained after rollout with deferred loading.
Five storefronts now ship with a shared, performance-safe recommendation layer that brand teams can tune per collection — translating directly into AOV and conversion gains.