Product design question
How would you improve the driver pickup experience on Uber?
- Uber
- Product design
- 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
Product sense for a two sided marketplace and the ability to scope a broad prompt into one measurable fix.
How to approach it
- Confirm scope: is the pain wrong pin drops, riders not visible curbside, or driver frustration at gated buildings and airports.
- List concrete pain points: mislocated pins in dense blocks, no way to signal exact standing spot, confusing airport queues.
- Segment by context since downtown, suburban, and airport pickups need different fixes and have different trip volume.
- Pick the segment with the most trips and highest pickup cancellation rate to prioritize.
- Propose a fix: a draggable live pin the rider can adjust plus a short text note field for recurring stops.
- Define success as pickup cancellation rate and average wait at curb, not overall trip ETA.
What a strong answer includes
- Names a real driver complaint, like arriving at a wrong pin dropped inside a mall instead of the entrance, rather than a generic 'bad UX' claim.
- Assumes a number to size the problem, for example roughly 10 to 15 percent of urban trips having a pickup mismatch, and marks it clearly as an assumption.
- Separates the rider-facing fix from the driver-facing fix instead of one blended feature.
- Chooses one metric to move, such as pickup cancellation rate, and states a directional target.
Common mistakes
- Jumping to a feature list before naming the actual pain point or who feels it most.
- Talking only about drivers and forgetting riders are the other half of this marketplace.
Likely follow-up questions
- How would you know the fix worked two weeks after launch?
- What would you do if drivers loved it but riders ignored it?
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More questions from Uber
Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 4: Discovery and strategy for AI products
- Chapter 7: AI UX and human oversight: design for a system that is wrong sometimes
- Chapter 14: Get the job: the AI PM interview loop