Product design question

How would you design a feature to reduce Amazon's order return rate.

Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.

What this question tests

Product sense for reducing a costly operational metric by diagnosing root causes across the purchase funnel, not just adding a return fee.

Break down why returns happen: wrong size or fit, not as described, changed mind, damaged in shipping, wrong item shipped.
Prioritize the largest driver with rough assumptions; apparel sizing is typically the single biggest return category.
Propose a targeted fix per driver, like a fit-prediction tool using purchase and return history for apparel, and clearer photos or specs for 'not as described' cases.
Define the primary metric, return rate by category, and a guardrail, purchase conversion rate, since overly cautious UX can suppress sales too.
Segment the rollout by category, starting with apparel, before generalizing.

Diagnoses root causes by return reason before proposing a fix, rather than jumping straight to a solution.
Picks apparel and fit as the highest-leverage category with a specific mechanism, size prediction from return history.
Balances the return-rate goal against a conversion guardrail so the fix doesn't just scare buyers away.

Proposing a blanket fix like a stricter return policy that ignores root cause and risks hurting trust and conversion.
Not segmenting by category, when return drivers vary hugely between apparel, electronics, and books.

How would you measure success without just looking at total returns dropping?
How would sellers react to a fit-prediction feature flagging their listings as high-return risk?