Building a Recommendation Engine from Real Purchase Data

The Situation

Picture a mid-sized retailer selling across a website and a couple of marketplace channels, with a few years of transaction history sitting in disconnected systems. They want product recommendations that actually reflect what customers buy together, instead of the generic "customers also viewed" widget that ships with most e-commerce platforms by default.

How We'd Approach It

Before any model gets built, the real work is data engineering: consolidating order history, inventory, and catalog data from separate channels into one place where it can actually be analyzed together. Without that step, a recommendation model trained on incomplete or inconsistent data would just produce confidently wrong suggestions.

With clean, unified purchase data in place, our AI automation approach would likely start with a simpler collaborative-filtering or association-rule model rather than jumping straight to a complex deep-learning system, since for a mid-sized catalog, the simpler approach is often both cheaper to run and easier to explain when a recommendation looks strange. We'd only reach for something more sophisticated if the catalog size and data volume actually justified it.

What an Engagement Like This Would Aim For

Success here would look like recommendations that make sense to someone who actually knows the catalog, not a specific lift percentage we could quote without real traffic behind it. Any conversion or revenue impact depends heavily on the specific catalog, traffic, and existing baseline, which is exactly why we're not attaching an invented number to a hypothetical scenario.