Estimation question
How many unique people write reviews on Amazon every month?
- Amazon
- Estimation
- Easy
Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.
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What this question tests
Estimation skill grounded in Amazon's known scale and typical reviewer behavior patterns.
How to approach it
- Pick a method: estimate Amazon's active monthly buyer base, then apply a realistic review-writing conversion rate.
- Assume roughly 200 million active US buyers monthly, as a reasonable order of magnitude for Amazon's scale.
- Assume a small share, roughly 2 to 5 percent, actually write a review in a given month, since most buyers never review.
- Multiply the buyer base by the review rate to estimate unique reviewers monthly.
- Sanity check against known review volume patterns, since Amazon receives millions of reviews across all products monthly.
- State the final estimate as an order of magnitude in the low millions of unique reviewers monthly.
What a strong answer includes
- Anchors on a specific, defensible buyer base assumption, roughly 200 million active US buyers, rather than guessing globally.
- Applies a realistic low conversion rate for review writing, since most buyers never leave reviews, and states it openly as an assumption.
- Cross checks the resulting figure against known scale of total reviews on Amazon to sanity check the math.
- Lands on an order of magnitude, low millions, with the calculation visible rather than a single unexplained guess.
Common mistakes
- Assuming most buyers write reviews, which vastly overstates the real conversion rate.
- Giving a global estimate without clarifying scope, since behavior varies a lot by country.
Likely follow-up questions
- How would incentivized reviews change this estimate?
- How would you validate this using Amazon's actual review count data?
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More questions from Amazon
Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 2: Data fluency: SQL, logs, and reading the truth yourself
- Chapter 9: Prove it paid off: outcomes, economics, and pricing
- Chapter 14: Get the job: the AI PM interview loop