How AI Personalization in Retail Works — And Why Developers Must Build It

A developer-focused guide published on DEV Community explains how AI-driven personalization helps e-commerce platforms show shoppers relevant products based on purchase history, browsing behavior, and context. According to McKinsey's 2021 Next in Personalization report, 71% of consumers expect personalized interactions, and 76% express frustration when they do not receive them. Faster-growing companies also generate roughly 40% more revenue from personalization than slower-growing peers, though the report notes this is a correlation rather than proof of causation. The guide outlines three core recommendation approaches — collaborative filtering, content-based filtering, and hybrid models — tracing collaborative filtering's retail roots to a landmark 2003 Amazon paper by Linden, Smith, and York. It also walks developers through building a working recommendation engine in Python and highlights common failure points in production systems.