Metrics question
What metrics will you use to calculate the happiness index of customers for the Medium blog?
- Medium
- Metrics
- Medium
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What this question tests
Metrics design skill for a qualitative concept, happiness, forcing you to build a proxy metric tree grounded in behavior.
How to approach it
- Clarify what happiness means here, likely reader satisfaction and writer satisfaction are both relevant for Medium's two-sided model, so confirm which side is in scope.
- Break happiness into observable proxies rather than trying to measure it directly: engagement depth, return behavior, and explicit feedback.
- Propose reader-side metrics: time spent reading relative to article length, return visit frequency, and claps or highlights per session.
- Propose writer-side metrics if in scope: publishing frequency, follower growth, and earnings via the partner program.
- Add a direct signal, a periodic short survey like NPS, to validate that behavioral proxies actually track satisfaction.
- Combine into a composite index, but flag that any single proxy can be gamed, so use multiple signals together.
What a strong answer includes
- Distinguishes reader and writer happiness explicitly, since Medium is two-sided and a single index would hide tension between the two.
- Proposes a specific proxy, completion rate as a percentage of article length read, as more meaningful than raw time spent.
- Adds NPS or a lightweight survey as a validation layer against gamed behavioral metrics.
- Notes a guardrail, watching for happiness metrics rising while overall usage falls, which would signal a shrinking but more satisfied base.
Common mistakes
- Proposing only one metric, like time spent, as a full proxy for happiness.
- Not separating reader and writer perspectives on a two-sided platform.
- Ignoring the risk that behavioral proxies can be gamed without a direct feedback signal.
Likely follow-up questions
- How would you weight reader happiness against writer happiness if they conflict?
- How would you detect if your happiness proxy was being gamed?
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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