Metrics question
Walmart's order return rate is increasing. As a product manager, what things would you look into to isolate the problem?
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What this question tests
Tests structured root-cause analysis: decomposing a metric change into hypotheses across product, operational, and external causes before jumping to conclusions.
How to approach it
- Clarify the metric: define 'return rate' precisely, e.g., returned units / delivered units, and confirm the timeframe and whether it's online, in-store, or both.
- Segment the increase: by category (apparel vs electronics vs grocery), channel (online vs in-store), region, and new vs returning customers.
- Check for a single confound first: a recent policy change (e.g., extended return window), a new product line with quality issues, or a website change that misled buyers (bad size guide, wrong images).
- Look at operational causes: fulfillment errors (wrong item shipped), damaged-in-transit rates, and a new carrier or warehouse.
- Look at external causes: seasonality (post-holiday returns), a competitor promotion drawing impulse buyers, or economic pressure increasing price-driven returns.
- Prioritize hypotheses by size of segment affected and ease of verifying with existing data, then propose the fastest test for the top one or two.
What a strong answer includes
- Starts by precisely defining and segmenting the metric instead of guessing at a cause immediately.
- Separates product-side causes (bad listings, sizing) from operational causes (fulfillment errors) from external causes (seasonality, competitor promos).
- Names a specific, testable first hypothesis, e.g., 'apparel returns up because size-guide accuracy dropped after a supplier switch,' and how to verify it against return-reason codes.
- Proposes using existing structured data first (return-reason codes, category breakdown) before commissioning new research.
Common mistakes
- Jumping straight to one cause (e.g., 'it's probably quality') without segmenting the data first.
- Not checking return-reason codes, which Walmart almost certainly already captures.
- Ignoring seasonality or timing effects that could fully explain the change.
Likely follow-up questions
- Which segment would you look at first and why?
- How would you distinguish a policy-driven increase from a quality-driven one?
- What would you do operationally in the first week while root cause is still unclear?
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 9: Prove it paid off: outcomes, economics, and pricing
- Chapter 2: Data fluency: SQL, logs, and reading the truth yourself
- Chapter 14: Get the job: the AI PM interview loop