Estimation question
How much more or less should Lyft pay drivers per trip (by changing Lyft’s take) to maximize net revenue for the next 12 months on this route in Toledo, Ohio? The goal is to find the optimal balance between Lyft's take and driver pay to improve the match rate and reduce failed rides while maximizing net revenue.
- Lyft
- Estimation
- Hard
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What this question tests
Estimation and marketplace economics: reasoning through a two-sided pricing trade-off using elasticity and net revenue logic.
How to approach it
- Clarify the goal: find the driver pay level that maximizes Lyft's net revenue on this specific route over 12 months.
- Identify the key trade-off: higher driver pay improves match rate (fewer failed rides) but raises Lyft's cost per ride.
- Set up a simple model: net revenue equals completed rides times take rate, where completed rides rises with driver pay up to a point.
- Estimate directionally: if failed rides are high today, raising pay likely increases net revenue by unlocking more completed trips.
- State the answer as directional and assumption-based, e.g. 'increase driver pay modestly' pending real elasticity data from a test.
What a strong answer includes
- Builds an explicit trade-off model (pay up, completion up, take rate down) rather than guessing a number outright.
- Proposes testing the change with a small experiment before a full route-wide rollout.
- States assumptions clearly, e.g. assuming current failed-ride rate is 15 percent due to underpaying drivers.
- Gives a directional, defensible answer rather than an oddly precise made-up dollar figure.
Common mistakes
- Giving a precise dollar amount with no model or reasoning behind it.
- Ignoring the two-sided trade-off between driver pay and Lyft's take rate.
- Not proposing any way to test the change before full rollout.
Likely follow-up questions
- How would you design a test to validate this before a full rollout?
- What data would you need to build a more precise model?
- How would this change if rider demand on the route were higher?
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 2: Data fluency: SQL, logs, and reading the truth yourself
- Chapter 9: Prove it paid off: outcomes, economics, and pricing
- Chapter 14: Get the job: the AI PM interview loop