Overview
How might we design return prediction so people can trust and act on AI output?
When to use
- Perfect for e-commerce platforms, fashion retailers, and businesses where predicting and preventing returns improves profitability and customer satisfaction.
When to skip
- Using scores to deny legitimate returns illegally.
- Showing likely returner labels to shoppers.
- Categories where return reasons are random gift-season noise.
Rules
Blocking checkout based on opaque risk score.
No action playbook attached to high-risk flag.
Bias against size or category without monitoring.
Predictions never back-tested against actual returns.
Evidence
| Product | Implementation |
|---|---|
| Amazon | Internal return risk models for ops and listing quality. |
| Zara | Fit and sizing content driven by return analytics. |
| ASOS | Fit assistant and reviews to reduce size returns. |
| E-commerce platforms | Merchant dashboards flagging high-return SKUs. |