AI & Automation · Retail & Commerce
For a retailer with a large catalog, a recommendation engine that helps customers find relevant products, gives new listings a route to visibility and keeps merchandisers in control.
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01Challenge
Recommendations based on co-purchase alone kept promoting the same small set of products.
New listings had no history, so the existing approach never recommended them.
The merchandising team could not steer what appeared, even for stock that needed to move.
02Approach
Blend behavioral signals with product attributes so the system has something sensible to say about items with little history, and keep the ranking explainable enough for merchandisers to trust and override.
03Outcome
Generic “customers also bought” strips kept recommending what was already popular. New products and slower lines never got seen.
Blending what customers do with what products are gives the model something sensible to say about items with little history.
A rules layer above the model lets the team pin, exclude and boost products without retraining anything.
Ranking strategies are compared in controlled experiments rather than swapped on instinct.
What changed
How it works
01 / 05
Inside the system
Built with
Customers find more of the catalog, and the team still decides what matters.
More work
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If something here is close to what you need, tell us about your situation and we will walk you through how we would approach it.
Intelligence → Innovation → Automation → Growth