Product design question
How would you improve the post-booking experience on Lyft?
- Lyft
- Product design
- Easy
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What this question tests
Tests product improvement thinking on a well-scoped, familiar journey stage: can you find a specific, high-leverage friction point rather than a generic wish list.
How to approach it
- Define the post-booking window precisely: from ride confirmation to pickup, and separately, the post-ride window (receipt, rating, tipping, lost items).
- Identify pain points in each: uncertainty about driver ETA accuracy, lack of clarity on pickup location in crowded areas, and friction in reporting a lost item or disputing a charge after the ride.
- Prioritize the pickup-window pain point as highest impact, since ETA uncertainty and pickup-spot confusion are common causes of cancellations and low ratings.
- Propose a concrete fix: live, precise pickup-pin refinement with landmark suggestions in dense areas, plus proactive delay notifications if the driver's ETA changes meaningfully.
- Propose a smaller fix for post-ride: a one-tap lost-item report that auto-fills the trip details, reducing the current multi-step support flow.
- Define success as reduced pre-pickup cancellation rate and reduced time-to-resolution for lost-item reports.
What a strong answer includes
- Splits 'post-booking' into two distinct sub-journeys (pre-pickup and post-ride) since each has different pain points and fixes.
- Picks pickup-pin accuracy as the priority with a clear rationale tied to cancellations and ratings, both measurable business outcomes.
- Proposes a specific, buildable feature (landmark-based pin refinement) rather than a vague 'improve communication'.
- Gives a concrete metric (pre-pickup cancellation rate) that is easy to test and directly tied to the improvement.
Common mistakes
- Treating 'post-booking' as one undifferentiated stage instead of breaking it into pickup and post-ride sub-journeys.
- Proposing changes with no clear tie to a measurable outcome like cancellations or ratings.
- Overlooking edge cases like dense urban pickup zones where pin accuracy matters most.
Likely follow-up questions
- How would you handle pickup accuracy differently in a rural area versus a dense city?
- What would you measure to know if the lost-item flow improvement worked?
- How would you prioritize this against other product bets on the roadmap?
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More questions from Lyft
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