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
The existing cross-selling logic on endress.com was defined internally rather than grounded in how customers actually buy, which meant the product recommendations shown alongside a main product were not always genuinely complementary.
Approach
I initiated and led a project to rebuild this logic around real purchasing behavior. Working with SAP sales order data, I drove the development of a calculation model that identifies which products are actually bought together, then used that 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 customers in industrial process automation actually build out their orders.
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