Metrics question
You are the PM of the Facebook timeline. How would you measure retention?
Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.
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What this question tests
Ability to define retention for a content-consumption feature and connect it to a broader growth loop.
How to approach it
- Clarify what 'timeline' means here: the profile/wall product where people post and revisit their history.
- Define retention precisely: N-day retention of users who return to view or post on their own or others' timelines.
- Break retention into cohorts: new users (day 1/7/30 retention) versus long-tenured users (monthly active rate).
- Identify leading indicators: posts per active user, and comments/reactions received, since social feedback drives return visits.
- Propose a specific retention curve to track, like D7 and D30, segmented by whether the user received engagement on their last post.
- Set a guardrail: ensure retention isn't propped up by notification spam alone.
What a strong answer includes
- Separates retention of posters from retention of viewers, since the timeline serves both and each needs a different lever.
- Proposes 'received at least one reaction' as a leading indicator of return visits, tying social feedback to retention.
- Gives an illustrative benchmark, e.g. D7 retention of 40% for users who got a comment on their last post, marked as an assumption.
- Flags notification-driven return visits as a metric that can mislead if not paired with genuine engagement signals.
Common mistakes
- Defining retention only as login frequency without tying it to timeline-specific behavior.
- Ignoring the difference between posters and passive viewers.
Likely follow-up questions
- How would you separate genuine retention from notification-driven logins?
- What would you do if new user D7 retention was low but D30 was fine?
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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