Metrics question
You are the PM at Meta. How would you define success metrics for Facebook Reels, and why was Facebook Reels launched?
- Meta
- Metrics
- Medium
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What this question tests
Metrics design plus business context: can you connect Reels metrics to the actual strategic reason Meta launched the product, competing with TikTok.
How to approach it
- State why Reels was launched: to compete with TikTok for short form video attention and keep users, especially younger ones, inside Meta's apps.
- Set the north star as time spent watching Reels, since the strategic goal is capturing attention that would otherwise go to TikTok.
- Add a creation side metric, Reels created per active user, since a healthy short form ecosystem needs a strong supply of new content, not just viewing.
- Add a guardrail, cannibalization of Feed and Stories time, since Reels could just shift existing engagement rather than growing total time spent.
- Segment by age group, since competing with TikTok matters most for younger users specifically.
- State success as growing total time spent across the app, not just Reels in isolation, since cannibalization would be a false win.
What a strong answer includes
- Ties the metric choice directly to the competitive reason Reels exists, not a generic short video metric list.
- Explicitly checks for cannibalization, a nuance that shows real strategic thinking about Meta's total engagement.
- Segments by age, since the TikTok competitive threat is concentrated among younger users.
Common mistakes
- Measuring Reels engagement in isolation without checking if it cannibalizes Feed or Stories.
- Ignoring the competitive context, treating Reels like any other feature launch.
Likely follow-up questions
- How would you tell if Reels growth is truly incremental versus cannibalized?
- How would you measure success differently for teens versus older users?
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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