Metrics question
How would you increase the average Uber driver rating in your area from its current 4.5?
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
Root-cause analysis and solution design for a supply-side quality metric in a two-sided marketplace.
How to approach it
- Break down what drives rating: car cleanliness, driving smoothness, navigation accuracy, communication, and pickup wait time.
- Pull data on which sub-factor correlates most with low ratings in this specific area, since the fix depends on the cause.
- Segment by driver tenure, since new drivers likely rate lower due to unfamiliarity with the app and routes.
- Prioritize the highest-impact, most fixable cause, for example pickup accuracy if GPS pin drops are common in dense buildings here.
- Propose a fix: in-app pickup instructions or a driver tutorial targeted at new drivers in this region.
- Define success: rating trend over the next 4 to 8 weeks isolated to this area versus a control region.
What a strong answer includes
- Decomposes rating into concrete sub-drivers instead of treating it as one opaque number to nudge generically.
- Uses tenure segmentation to find the biggest lever, since new-driver onboarding is a common real cause.
- Proposes a targeted, testable fix tied to a specific diagnosed cause, not a blanket incentive.
- Notes the risk of just paying drivers a bonus, which doesn't fix root causes and isn't scalable.
Common mistakes
- Jumping straight to incentives or bonuses without diagnosing the cause.
- Treating rating as one undifferentiated number.
- Ignoring that ratings can also reflect rider behavior, not just driver quality.
Likely follow-up questions
- How would you rule out rider-side bias in the ratings themselves?
- What would you do if the low ratings are concentrated among new drivers only?
- How would you measure the ROI of any fix you propose?
More metrics questions
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- How would reduce cancellations for Uber?Shopify · Metrics · Medium
- There is a data point that indicates that there are more Uber drop-offs at the airport than pick-ups from the airport. Why is this the case and what would you do within the product to change that?PayPal · Metrics · Hard
- How would you measure the success of Uber Ride?Lyft · Metrics · Easy
- If a large number of drivers are dropping out of a particular city, why would it be?Lyft · Metrics · Medium
- Drivers are dropping out of a city on Lyft. How do you figure out what's going on?PayPal · Metrics · Medium
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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