Metrics question

Imagine you are a PM in Amazon. How would you reduce the rate of returns?

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

Tests diagnosing and reducing a costly operational metric (returns) by segmenting root causes before proposing fixes.

Segment returns by cause: item not as described, wrong size/fit, damaged in transit, or simple buyer's remorse, since each needs a different fix.
Segment by category, since electronics, apparel, and general goods have very different return-rate baselines and root causes.
For 'not as described': improve listing accuracy via better photos, size charts, and seller-provided detail requirements.
For 'damaged in transit': tighten packaging standards for fragile categories and hold sellers accountable via a packaging quality score.
Define success: return rate reduction by category, paired with a guardrail that customer satisfaction doesn't drop (avoiding return friction just to suppress the metric).

Breaks returns into distinct root causes before proposing any fix, rather than one blanket 'improve quality' answer.
Ties each cause to a specific, actionable fix (better size charts for fit issues, packaging standards for damage) rather than generic ideas.
Uses an illustrative number, e.g. assume apparel has a 25% return rate driven mostly by fit versus 5% for electronics driven by defects, to justify category-specific fixes.
Includes a guardrail against just making returns harder to file, which would hurt trust even as the metric improves.

Proposing a single universal fix (like stricter return policy) without segmenting the actual causes.
Reducing returns by adding friction to the return process itself, which would hurt customer trust even if the number improves.

How would you hold third-party sellers accountable for high return rates on their listings?
What would you do if tightening the return policy hurt purchase conversion?