Recommendation Systems for eCommerce: Do You Actually Need One Yet?

Product recommendation systems get pitched as a must-have for any online store, but they need a meaningful volume of purchase data to actually outperform simple rules.

The data threshold that matters

A sophisticated recommendation model trained on a handful of orders per day will underperform simple, transparent rules like "customers who bought X also bought Y" — the fancier approach needs real scale to pay off.

Start simple

Rule-based recommendations (related products, frequently bought together, recently viewed) capture most of the value at a fraction of the complexity and cost of a full machine learning pipeline.

When a real ML-based system starts making sense

Once you have enough historical order data and consistent traffic, a trained model can meaningfully outperform static rules — this is usually a "scale up" project, not a launch-day feature.

Measure the actual impact

Whatever approach you use, track whether recommended products actually get added to cart — a recommendation engine that looks sophisticated but doesn't move the metric isn't worth the complexity.

Running an online store and wondering if you're ready for this? Talk to us — we'll give you an honest read based on your actual traffic and order volume.