Metrics question
Set a goal for Facebook groups admins.
- Meta
- Metrics
- Medium
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What this question tests
Tests defining a goal and metric tree for a specific, less obvious user segment, group admins, rather than end users of the platform.
How to approach it
- Clarify what admins need help with, growing membership, moderating content, or engagement, since the goal differs.
- Identify the core problem: unpaid volunteer admins who churn out due to moderation burden and unclear signals of group health.
- Propose the goal as increasing admin retention, since admin churn directly threatens whether a group survives.
- Build a metric tree: admin retention feeds from moderation tool usage and time spent moderating, which feeds from group health signals like spam and report rates.
- Add a guardrail that member growth and engagement shouldn't drop as a side effect of stricter moderation tools.
- Set the north star as 90-day active admin retention rate.
What a strong answer includes
- Picks admin retention, not generic engagement, correctly identifying admins as the hardest resource to replace.
- Splits the metric tree into moderation efficiency, time saved, and group outcomes, spam and reports, as two distinct legs.
- Uses illustrative numbers as assumptions: assume 10M active group admins with 15% annual churn, and cutting that by a third meaningfully protects group health.
- States the guardrail explicitly: more moderation tooling shouldn't make groups feel over-policed to members.
Common mistakes
- Setting a member-facing goal like DAU instead of an admin-specific one.
- Ignoring that admins are unpaid volunteers with different incentives than regular users.
- Skipping any guardrail against over-moderation.
Likely follow-up questions
- How would you measure admin burnout directly?
- What feature would move this metric fastest?
- How do you avoid penalizing small, low-traffic groups?
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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