Metrics question
You are PM of FB Events and you saw an overnight drop in RSVPs by 10%. How would you go about determining what happened?
- Meta
- Metrics
- Medium
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What this question tests
Fast, structured root cause diagnosis for a sudden metric drop, distinguishing bugs from real behavioral shifts.
How to approach it
- First check for a tracking or instrumentation bug introduced by a recent release, before assuming a real drop in user behavior.
- Segment the drop by platform, region, and event category, to see if it is isolated or broad based.
- Check for a recent product change, a new release affecting the RSVP button, notification changes, or a UI change to the events surface.
- Check external factors, a seasonal dip, for example a holiday week with naturally fewer events, or a major competing event drawing attention elsewhere.
- Once isolated, propose the fix and define how recovery will be confirmed, RSVP rate returning to baseline within a defined window.
What a strong answer includes
- Starts with the instrumentation check first, since a logging or tracking bug is a common and fast to rule out cause of a sudden overnight drop.
- Shows real segmentation, the drop concentrated on one platform after a specific app version, pointing to a broken RSVP button in that release.
- Defines a clear recovery criterion, RSVP rate returning within 5 percent of baseline within 48 hours of the fix, to confirm the diagnosis was correct.
Common mistakes
- Jumping to external explanations like seasonality before ruling out a tracking or release bug.
- No segmentation by platform, region, or event type to isolate where the drop is concentrated.
- No defined way to confirm the eventual fix actually worked.
Likely follow-up questions
- What would you check first if the drop was isolated to just one country?
- How would you prevent a broken RSVP button from shipping undetected next time?
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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