Metrics question
You are the product manager for Facebook News. What success metric would you track and why?
- Meta
- Metrics
- Medium
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
Picking one defensible north star metric for a news product and explicitly justifying it over weaker alternatives.
How to approach it
- Clarify the product's goal: connect users with quality journalism while building publisher relationships.
- List candidates: time spent, click-through to publisher, return frequency, and publisher revenue generated.
- Reject pure time-spent as the north star since it doesn't capture whether users found real value or publishers benefited.
- Propose the north star: weekly active readers who click through to at least one full article, a proxy for genuine engaged reading.
- Pair it with a guardrail: publisher referral traffic and revenue, since the product's health depends on sustained publisher participation.
- Add a diversity guardrail: source diversity per user, avoiding an engagement-optimized feed narrowing to a few dominant outlets.
What a strong answer includes
- Picks one clear north star, engaged click-through readers, and explicitly defends it over the more obvious but weaker 'time spent.'
- Adds a publisher-side guardrail, recognizing this is a two-sided relationship, not just a consumer feature.
- Names source diversity as a specific, non-obvious guardrail relevant to a news product's real risk, filter bubbles.
Common mistakes
- Defaulting to time spent or DAU without considering they don't reflect genuine informational value.
- Ignoring the publisher side of the equation entirely.
Likely follow-up questions
- How would you weigh publisher revenue against user engagement if they conflict?
- How would you detect if the feed is becoming an echo chamber?
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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