Returns analysis:
Why jeans come back - the real reasons
How NLP analysis of 640,000 free-text comments uncovers the 5 true root causes behind "doesn't fit" - and which 3 measures reduce the return rate from 28% to 21%.
01 The problem - €42 per return, 640,000 times a year
Why "does not fit" as a return reason is not sufficient
With 3.2 million units sold and an average return rate of 28%, that amounts to 896,000 returns per year. Each costs on average €42 (shipping, handling, refurbishment, value loss). Official statistics show "does not fit" at 42% - but the NLP model reveals: behind that single label lie 5 distinct problems with 5 distinct solutions.
02 Model - NLP root-cause analysis on 640,000 free-text comments
The total potential is €8.4M — the €4.6M impact shown here is based on the two most quickly implementable measures: size-run correction and product-photography improvement. Cluster 1 (31%): Incorrect size run → Fix: size recommender (Module 3). Cluster 2 (22%): Colour/material differs from expectation → Fix: better product photography + fabric close-ups. Cluster 3 (19%): Quality defect → Fix: tighter QC requirements with suppliers. Cluster 4 (16%): Expectation vs. reality → Fix: more honest product descriptions + customer reviews prominently placed. Cluster 5 (12%): Serial returner → Fix: checkout prompts + order limits.
03 Next steps
All free-text comments from the past 2 years through the topic model. Output: root-cause map + top 20 problem SKUs.
Correct size tables, improve product photography, review integration. Combined: €3.8M/year.
Return probability per order in real time. Targeted interventions for high-risk orders.