Metrics question
How would reduce cancellations for Uber?
Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.
Start a mock interview on this question · Mock interview from a job description
What this question tests
Diagnosing a specific funnel drop, ride cancellations, by breaking it into rider and driver side causes before proposing fixes.
How to approach it
- Clarify who is cancelling, rider or driver, since causes and fixes differ, and state you will cover both.
- List likely rider side causes, long ETA, price surge shown after booking, and driver taking a different pickup route.
- List likely driver side causes, low fare trips, long pickup distance, and rider not at the pickup point.
- Propose targeted fixes per cause, for example more accurate ETA before booking and surge shown upfront.
- Define the metric, cancellation rate segmented by rider versus driver initiated, and time to cancel.
What a strong answer includes
- Separates rider and driver cancellations since each needs a different fix, not one blanket plan.
- Names a fix tied to a specific cause, showing live driver location before confirming the ride to cut ETA surprise cancellations.
- Proposes a guardrail, driver acceptance rate, so a rider focused fix does not push more cancellations onto drivers.
Common mistakes
- Treating all cancellations as one problem instead of segmenting by who cancels.
- Proposing generic fixes with no tie to a specific cause.
- No metric to measure whether the fix worked.
Likely follow-up questions
- Which segment would you prioritize first and why?
- How would you A/B test the ETA accuracy fix?
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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