Product design question
How do you improve Facebook Watch?
- Meta
- Product design
- Easy
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What this question tests
Product design judgment on a lower-traction video product competing against dominant players like YouTube and Netflix.
How to approach it
- Users: existing Facebook users who watch video incidentally in-feed versus people seeking dedicated long-form viewing.
- Pain points: Watch lacks a strong discovery habit compared to YouTube, and content quality is inconsistent.
- Prioritize: focus on leveraging Facebook's unique advantage, the social graph, for discovery rather than competing head-on with YouTube's content library.
- Solution: social-driven discovery, like showing what friends are watching or discussing, and creator tools tied to Facebook Groups communities.
- Success metric: session frequency and watch time from social-driven recommendations specifically, isolating that lever's impact.
- Confirm scope: whether the goal is to build a standalone destination habit or simply increase in-feed video engagement.
What a strong answer includes
- Identifies Facebook's real differentiator, the social graph, rather than trying to out-content YouTube directly.
- Proposes a specific, testable mechanic, friend activity-driven discovery, not a vague better recommendations claim.
- Isolates the metric to the specific lever, social-driven watch time, to know if the change actually worked.
Common mistakes
- Proposing to compete with YouTube on content library size, which Facebook cannot realistically win.
- No use of Facebook's actual differentiator, the social graph.
- Vague success metric like overall engagement with no isolation of the change's effect.
Likely follow-up questions
- How would you measure if social-driven discovery is actually better than algorithmic discovery alone?
- What would make you recommend shutting Watch down instead of improving it?
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 4: Discovery and strategy for AI products
- Chapter 7: AI UX and human oversight: design for a system that is wrong sometimes
- Chapter 14: Get the job: the AI PM interview loop