Metrics question
How would you determine success for the Facebook profile verification checkmark?
- Meta
- Metrics
- Medium
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What this question tests
Tests defining success for a trust-signaling feature where the real goal is reducing impersonation, not raising verification volume.
How to approach it
- Clarify the checkmark's purpose: help users trust they're interacting with the authentic account of a public figure or brand, reducing impersonation-driven scams and confusion.
- Reject raw verified-account count as the top metric, since it rewards volume, not whether verification is solving the actual trust problem.
- Propose the core metric: reported impersonation incidents involving verified-eligible figures, tracking whether it declines as verification coverage grows among the target population.
- Add a trust-perception metric: survey-based user trust in verified accounts' authenticity, since perceived trust is the actual product goal.
- Add a guardrail: false-positive verification rate (incorrectly verified accounts) and verification review turnaround time, since a slow or error-prone process undermines the whole feature's credibility.
What a strong answer includes
- Ties success back to the checkmark's real purpose, reducing impersonation and building trust, rather than a vanity count of verified accounts.
- Uses an illustrative number, e.g. assume impersonation reports for high-profile figures drop 30% after verification coverage crosses a certain threshold, framing what success would look like.
- Adds a perception-based metric (survey trust score) since the checkmark's value is fundamentally about user perception, which isn't fully captured by behavioral data alone.
- Flags the false-positive risk explicitly, since a single high-profile verification mistake can undermine trust in the whole system.
Common mistakes
- Picking total verified accounts as the success metric without connecting it to the actual trust or anti-impersonation goal.
- Ignoring the false-positive/error risk, which is a real and reputationally costly failure mode for verification systems.
Likely follow-up questions
- How would you handle appeals from users denied verification who believe they qualify?
- How would you prevent the checkmark itself from becoming a target for account takeover attacks?
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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