Metrics question
50% of users who RSVP yes to Facebook Events do not show up. What would you do?
- Meta
- Metrics
- Hard
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What this question tests
Whether you can diagnose a gap between stated intent and actual behavior and target a fix at the real cause.
How to approach it
- Define the metric precisely: no-show rate among yes-RSVPs, and confirm whether this is worse for certain event types.
- Segment the no-shows: large public events versus small friend gatherings, since intent-to-attend differs a lot by context.
- Generate hypotheses: RSVP yes used as a soft interest signal rather than a commitment, no reminder close to event time, or last-minute conflicts.
- Check data support for each, such as comparing no-show rate for events with and without a day-of reminder sent.
- Prioritize the reminder and friction hypothesis first, since it's the cheapest to test and likely explains a meaningful share.
- Propose a fix: stronger day-of reminders and a lightweight re-confirmation prompt, and measure the change in no-show rate.
What a strong answer includes
- Frames RSVP yes as a soft signal of interest rather than assuming it always means firm commitment.
- Segments by event type, since a 50 percent no-show rate likely hides very different causes for public versus close-friend events.
- Proposes a cheap, testable fix, day-of reminders, before assuming a deeper redesign of RSVP is needed.
- Gives an illustrative number, assuming events without a reminder show a materially higher no-show rate than those with one.
Common mistakes
- Assuming RSVP yes always means firm intent and treating every no-show as a broken promise to fix with penalties.
- Proposing a heavy-handed fix, like penalizing users, before testing a lighter one like reminders.
Likely follow-up questions
- Would you consider changing the RSVP options themselves, like adding a maybe with lower commitment framing?
- How would you tell whether reminders alone closed most of the gap?
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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