Why does collaborative filtering work so well in the browse experience specifically? Because while shoppers explore, their every view and wishlist add is a signal — and an engine that reads those signals can surface the items that grow the basket. ASOS applies exactly that during browsing.
The result: ASOS uses AI collaborative filtering to analyze viewing patterns, purchase decisions, and wishlist additions during browsing — driving a 25% increase in average order value.
Browsing generates the richest signals#
The browse session is where shoppers reveal the most: what they linger on, what they save, what they pass over. ASOS’s collaborative filtering reads these signals in real time during browsing and surfaces products that shoppers with similar behavior tend to buy together — including complementary items the shopper hadn’t considered. That’s how it lifts average order value 25%: by turning browse-time signals into basket-growing suggestions.
Collaborative filtering is well-suited to this because it captures taste that category rules miss — “people who behave like you also bought this,” even across unexpected categories.
How browse AI works#
Browse AI personalizes the exploration experience based on behavior. ASOS’s version uses collaborative filtering — learning from collective behavior — applied to live browsing signals.
Three mechanics drive the AOV lift. The engine reads browse-time signals — views, wishlist adds, and purchases — to infer taste and intent. It surfaces complementary items via collaborative filtering, suggesting what similar shoppers bought together. And it personalizes in real time, adapting as the session unfolds.
Why browse signals grow the basket#
The strategic point is that browsing is the richest, most under-used signal source in most stores. Purchase history tells you what a shopper bought; browse behavior tells you what they’re considering right now. Reading that live signal and surfacing complementary items — the pieces that complete a look or pair with what’s being viewed — grows the basket while the shopper is still exploring, before they’ve decided. That’s why ASOS’s browse-time collaborative filtering moves AOV.
What this means for your store#
Any store can put browse-time signals to work:
- Read live browsing signals — views, wishlist adds — not just purchase history.
- Use collaborative filtering to surface complementary items similar shoppers buy together.
- Personalize in real time during the session to grow the basket before checkout.
The browse is where shoppers reveal the most. Reading those signals is how you grow order value.
Bring browse AI to your store with CartAmplify#
CartAmplify brings collaborative-filtering browse AI to any store — Shopify, dropshipping, or marketplace. Real-time, signal-driven suggestions that grow average order value, the way ASOS lifts AOV 25%.
Related reading#
- How ASOS Lifts Email Engagement 75% with AI Discovery
- How ASOS Lifts AOV 25% with AI Outfit Recommendations
- How Adlibris Tripled Conversion with Browse AI
Figures cited from publicly reported ASOS AI case studies. Results vary by catalog, traffic, and implementation.
