Metrics question
Imagine you are a PM of Uber's search map. How would you calculate its success?
- Uber
- Metrics
- Medium
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What this question tests
Metrics design: can you build a metric tree for a specific product surface (search/map) that ties to the business outcome (completed rides), including guardrails.
How to approach it
- Define the user job: find a pickup point or destination quickly and accurately via search/map.
- Set the north-star metric: search-to-ride-request conversion rate.
- Build supporting metrics: search success rate (result selected without re-searching), time-to-select, and map load/accuracy issues.
- Add guardrails: search latency and rate of 'no results found', since a fast but inaccurate search hurts trust.
- Segment by context: new vs unfamiliar addresses, dense urban vs suburban, since search difficulty varies a lot.
- Tie back to the business outcome: completed rides and driver-passenger match time.
What a strong answer includes
- Builds an explicit metric tree from a north-star (conversion) down to diagnostic metrics (search success rate, re-search rate).
- Adds a guardrail, not just growth metrics, e.g. search latency, since a slow search would hurt even if conversion looked fine short-term.
- Segments by scenario (ambiguous address, landmark search, new city) where search is hardest, since averages would hide the real problem.
- Connects the metric back to the business outcome, completed rides, rather than stopping at search-specific numbers.
Common mistakes
- Only naming one metric (e.g. 'search accuracy') without building a tree connecting it to the business outcome.
- Forgetting guardrail metrics like latency or false-result rate.
Likely follow-up questions
- How would you detect the map search is failing silently, e.g. users search but never request a ride?
- How would you weight search success versus search speed if they trade off?
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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 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