ReturnsRadar
Returns, down. Confidence, up.
Reads fit signals, outfit cohesion, and past return reasons per shopper — and intervenes before returns happen with better guidance on the product page and in post-purchase.
- 0–100 Return-risk score per item
- Structured Reasons, always
- Learn From every return
ReturnsRadar is a prevention layer for the costliest part of fashion e-commerce. It watches signals that predict returns — wrong size chosen, shopper history, style mismatch — and steers the experience in the moment to avoid them.
When returns do happen, the radar captures structured reasons and sends them back into FitProfile and the style graph. Every return makes the next order a little smarter.
Everything ReturnsRadar handles for you
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Return-risk score
Per-shopper, per-item score used by the site to gently nudge better choices.
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Proactive nudges
Size suggestions, fit warnings, outfit notes — surfaced before "add to cart".
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Structured reasons
Returns capture real reasons — "too tight in the shoulders" — not "did not like".
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Learning loop
Every return feeds FitProfile and StyleGraph so the next session is sharper.
How ReturnsRadar plugs into the fashion stack
ReturnsRadar is a risk and learning layer. It reads from shopper data and writes refinements back into the same sources.
- FitProfile feeds refinements back into each shopper's fit rules.
- StyleGraph learns from returned outfits — not just purchased ones.
- BoutiqueSite renders the proactive nudges on product pages.
- AIM CRM logs return context on the shopper timeline for service use.
Wire ReturnsRadar into your product today
Book a consultation with our founders and we'll walk you through the whole microservice stack — not just this one — live on your domain.