AI personalization: growth strategies

How many widgets per screen, what belongs on a product card, which algorithm to use on the home screen, the catalogue, the product page and the cart — proven practices from dozens of retail app launches.

Three maturity levels of an experimentation culture, five principles without which an A/B test is pointless, and the five barriers that stall experiments in real teams — with what to do about each.

Ten reproducible personalization mechanics — from a chatbot that knows the browsing history to in-store personalization driven by online data — each with a step-by-step setup and the metric it moves.

Five placements for a repeat-order control — home screen, catalogue menu, bottom sheet, floating button and order history — with the pros, cons, targeting and the Last Purchase strategy behind each.

Research Online, Purchase Offline is the revenue your digital work generates in stores and nobody attributes to it. Promo codes, call tracking, offline conversion import and unified customer ID compared, plus a five-step roadmap.

Segmentation, recommendation strategies, A/B testing and analytics — the four stages of personalization walked through end to end on the example of a DIY and home goods retailer.

Three or four behavioural segments that cover 100% of traffic beat a hundred narrow ones. A step-by-step guide to defining segments, testing personalization on each, and a worked example from an electronics retailer.

Backend-Driven UI lets you change app screens without a release. What BDUI and an LMP are, why they matter in e-commerce, and how a team ships interface changes in hours instead of weeks.

A follow-up to the RecSys Studio article: what to do with an algorithm once it is loaded. How filters and merchandising rules (Include, Exclude, Pin) let you shape your model's output in the interface, in minutes, without retraining.