Size & fit prediction:
The right size first time
How a collaborative-filtering model uses purchase history and return patterns to reduce the fit-related return rate for jeans from 38% to 22% - with a checkout widget.
01 The problem - "does not fit" is the #1 return reason
Why 34% of all jeans returns are due to incorrect size selection
Jeans are the most return-intensive garment: the return rate stands at 38%, with 34% attributed to "does not fit". The problem is not quality - it is uncertainty at the point of purchase. A "32/32" from Brand A fits completely differently than from Brand B. And within the same label, slim fit, straight and wide leg can vary by up to 2 dress sizes.
02 Model - collaborative filtering + body-shape matching
For 68% of fit-related returns, a correct size recommendation would have been sufficient. This is not AI magic - it is the systematic evaluation of data that already exists: which customer kept which size in which cut, and which they returned. The €4.8M saving requires no hardware investment - only an algorithm in the checkout. For new customers without purchase history, the model falls back on category averages — full recommendation quality is reached from the second purchase onwards.
03 Next steps
Export your returns data with size information. Calibration of the size run per SKU.
Size recommender as a widget on the product page: "Based on your previous purchases, we recommend size 31/32".
Automated detection of miscalibrated size tables. Report to product team: "SKU X runs 1.2 sizes too large."