Question-led guides for commerce teams evaluating personalization. Each article gives the short answer first, then explains what to test and how to measure it.
RelevanceRail supports Shopify, Shopify Plus, BigCommerce, WooCommerce, and custom storefronts. Integration is a snippet plus a catalog and order feed, with guided setup for each platform. Search and collection ranking use lightweight APIs that sit alongside your existing theme, so there is no replatforming and no theme rebuild required to go live.
Built in recommendation blocks usually run one simple rule, like items also bought, in one slot on the product page. RelevanceRail runs a full ranking engine across every surface, including cart, collections, search, and post purchase, all sharing one shopper profile. It also includes holdout measurement so you can see exactly what the engine adds over your current setup.
An AI product recommendation engine ranks a store's catalog for each individual shopper using behavioral data. It learns from browsing, purchases, and the choices of similar shoppers to predict what a visitor is most likely to buy next, then serves those products across the store. Done well, it replaces generic bestseller widgets with suggestions that feel handpicked for each person.
Send your store URL and get the three highest-impact personalization opportunities in a short report you keep.
Get a free audit