Imagine an online fashion store that behaves like a top personal stylist who knows your size, taste, budget, and what’s trending right now—and instantly rearranges the whole store just for you, in real time. That’s what ASOS is building with AI.
Traditional ecommerce shows the same catalog to everyone, leading to choice overload, poor fit, high returns, and low conversion. ASOS uses AI to personalize discovery, sizing, recommendations, and merchandising at scale so each shopper finds the right item faster and is more likely to keep it.
Behavioral data at fashion scale (clicks, searches, purchases, returns, fit feedback), combined with merchandising expertise and iterative in-house algorithms, creates a dataset and feedback loop that are hard for new entrants to copy quickly.
Hybrid
Vector Search
High (Custom Models/Infra)
Real-time personalization at global scale (latency + infrastructure cost for recommendations and search), and maintaining model quality as catalog and user behavior shift quickly with trends.
Early Majority
ASOS is applying AI deeply across the fashion ecommerce funnel—personalized discovery, search, recommendations, and potentially sizing/fit—using its large, trend-driven catalog and rich behavioral data. The breadth and integration of AI across the shopping journey go beyond basic ‘people also bought’ engines many retailers still rely on.