Metrics question
You are the PM for Facebook, Instagram. You want to add new "Mute showing food" feature. How do you measure success?
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What this question tests
Metric design for a content preference control feature, balancing user satisfaction against potential engagement or reach trade offs.
How to approach it
- State the feature's goal, let users reduce unwanted content, food posts, to improve their feed relevance and satisfaction.
- Define the primary metric, adoption rate of the mute feature among users who see food content frequently, and change in self reported feed satisfaction for those users.
- Define an engagement guardrail, overall time spent and session frequency for users who use the mute feature, checking it does not reduce their platform usage.
- Define a content ecosystem guardrail, impact on food content creators' reach and engagement, since muting reduces their audience even if individual users are happier.
- Propose testing via an A B test comparing feed satisfaction and retention between users with access to the feature and a control group without it.
What a strong answer includes
- Separates the user satisfaction win from the content ecosystem cost explicitly, naming food creators' reduced reach as a real trade off, not just a pure positive feature.
- Proposes a specific guardrail, engagement and session frequency for feature adopters, to confirm the mute option does not inadvertently reduce their overall platform usage.
- Frames the launch decision around both sides, shipping only if satisfaction improves without a meaningful drop in food category creator engagement.
Common mistakes
- Only measuring feature adoption with no check on whether it actually improved user satisfaction or just reduced usage overall.
- Ignoring the impact on food content creators who would lose reach from the mute option.
- No plan to test the feature's effect before a full rollout.
Likely follow-up questions
- How would you communicate this trade off to food content creators?
- What would make you decide this feature is not worth keeping after launch?
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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