An AI-powered cross-selling engine built on real sales data
Replacing manually curated cross-selling logic with a data model that recommends genuinely complementary products.
Problem
Cross-selling recommendations are only as good as the logic behind them. When that logic is curated by hand rather than grounded in how customers actually buy, the products shown alongside a main product are not always genuinely complementary.
Approach
I initiated and led a project to rebuild this logic around real purchasing behavior. Working from real sales data, I drove the development of a calculation model that surfaces genuinely complementary products, then used it to power the recommendations shown on the product pages.
Outcome
The result is a cross-selling engine grounded in actual demand patterns rather than internal assumptions — surfacing product pairings that reflect how B2B customers in industrial settings actually build out their orders.
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