Metrics question
Uber's MAU had gone down a few months back in August. Explore the reasons why that might have happened, and what would you do? What can be done to receive warning signals of this beforehand?
- Microsoft
- Metrics
- Hard
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What this question tests
Diagnostic thinking for a metric decline plus building forward-looking monitoring, not just after the fact explanation.
How to approach it
- Segment the MAU drop: by geography, rider versus driver side, new versus existing users, to find where the decline concentrated.
- Check external factors: seasonality, since August often includes lower urban commuting due to vacation season, or a local regulatory or pricing change.
- Check competitive factors: a competitor launching a promotion or expanding in a specific region during that period.
- Check internal factors: any pricing change, app update, or driver supply issue like surge pricing frustration reducing rider retention.
- Propose an action: address the specific driver identified, for example targeted win-back promotions in the affected region.
- Propose early warning signals: track leading indicators like weekly active trend, driver cancellation rate, and search-to-book conversion, rather than waiting for a full monthly MAU drop to notice a problem.
What a strong answer includes
- Segments the investigation by geography and user type before concluding a cause, avoiding a single blanket explanation.
- Considers seasonality explicitly, a plausible and specific explanation for an August dip in a ride-hailing context.
- Proposes concrete leading indicators, weekly trends and cancellation rate, to catch the next decline earlier than a monthly MAU report would.
Common mistakes
- Jumping to one cause without segmenting the data to confirm where the drop concentrated.
- Ignoring seasonality as a plausible explanation for an August-specific dip.
- No proposal for earlier warning signals, just explaining after the fact.
Likely follow-up questions
- Which single leading indicator would you monitor most closely going forward?
- How would you distinguish a seasonal dip from a genuine competitive threat?
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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