Metrics question
You’re the product manager for Facebook Live. You notice usage is down 20%. How do you go about finding the root cause?
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What this question tests
Root-cause analysis for a usage decline: can you structure hypotheses across product, external, and measurement causes before jumping to conclusions.
How to approach it
- Confirm the metric definition: usage of Facebook Live specifically, down 20% over what time window and versus what baseline.
- Check for a measurement artifact first: a tracking change, a redefinition of 'live usage', or a seasonal comparison issue.
- Hypothesis: internal, a recent product change (e.g. algorithm deprioritizing Live in the feed, or a UI change reducing discoverability).
- Hypothesis: external, competition from Reels/TikTok/Instagram Live pulling the same creators and viewers away.
- Hypothesis: creator-side, fewer creators going live due to reduced reach or incentive changes.
- Segment the drop by viewer vs creator side, and by cohort (new vs returning) to isolate where the decline concentrates.
What a strong answer includes
- Checks for a measurement/tracking issue before assuming it's a real behavioral decline, showing rigor.
- Separates viewer-side decline from creator-side decline (fewer broadcasts vs fewer viewers per broadcast), since the fix differs.
- Names a specific, plausible external cause, competition from Reels/TikTok pulling live/short-form attention, grounded in known industry shifts.
- Proposes segmenting by cohort and geography before proposing a fix, avoiding a premature solution.
Common mistakes
- Jumping straight to a fix without separating creator-side from viewer-side causes.
- Ignoring the possibility of a measurement or definitional change causing an apparent, not real, decline.
Likely follow-up questions
- How would you tell if this is a creator supply problem or a viewer demand problem?
- What would you check first to rule out a tracking issue?
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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