Strategy question
Maximize Lyft's net revenue given the following condition described.
- Lyft
- Strategy
- Hard
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What this question tests
Strategic and quantitative reasoning: proposing concrete levers to maximize net revenue on a two-sided marketplace under stated conditions.
How to approach it
- Clarify the specific conditions given, such as current match rate, failed-ride rate, or driver supply constraints, and confirm what's fixed versus flexible.
- Identify the core levers available: driver pay (take rate), rider pricing, and incentive programs to shift supply or demand.
- Build a simple framework: net revenue equals completed rides times Lyft's take, so improving completion rate matters as much as the take percentage.
- Propose a specific lever, such as adjusting driver pay to reduce failed rides where undersupply is the binding constraint.
- Recommend testing the change on a limited scope first, with a clear metric like net revenue per ride to measure impact.
What a strong answer includes
- Builds an explicit revenue framework (completed rides times take rate) instead of guessing a lever with no structure.
- Identifies which constraint is binding (supply or demand) before proposing a specific fix.
- Proposes a testable, scoped experiment rather than a blanket platform-wide change.
- States a clear metric, e.g. net revenue per ride, to judge whether the lever worked.
Common mistakes
- Proposing a lever with no framework connecting it to net revenue.
- Ignoring whether supply or demand is the actual binding constraint.
- No plan to test before a full rollout.
Likely follow-up questions
- How would you identify whether supply or demand is the binding constraint here?
- What would you test first, and how would you measure it?
- How would this change during a high-demand event?
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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 9: Prove it paid off: outcomes, economics, and pricing
- Chapter 14: Get the job: the AI PM interview loop