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Meta·Product Manager·Onsite - Product Sense / Strategy·Senior

Senior
Apr 2026

Summary

A PM interview question from Meta asking you to think back to when Facebook only had likes and decide which reactions to actually ship. Short prompt, but it opens up into a surprisingly deep product strategy exercise.

Questions Asked (1)

Q1

You're the PM responsible for launching Facebook Reactions. Facebook currently only has the Like button. How do you decide which specific reactions to include in the launch?

Product Sense & IdeationProduct StrategyRoadmap Prioritization
Author's notes

I kept wanting to jump straight to listing emotions but the real work is justifying why those and not others.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the goal of Reactions: to increase expressive range and reduce miscommunication without harming engagement. Then propose a data-driven framework that balances user needs, product simplicity, and business impact to select a small set of reactions for launch.

Pro tip: Emphasize that the initial set should be minimal and extensible, and that you'd validate with A/B tests and qualitative research before global rollout. Show awareness of the risk of diluting the Like and the importance of internationalization.

1. Define Objectives and Success Metrics

Clarify what Reactions should achieve: increase expression, reduce miscommunication, maintain engagement, and align with Facebook's mission. Define success metrics like reaction usage, Like usage, comments, and user sentiment.

2. Research User Needs and Pain Points

Conduct qualitative and quantitative research to identify the most common emotional responses users want to express. Analyze existing workarounds (e.g., commenting 'haha' or 'sad') and survey users across key markets.

3. Prioritize Candidate Reactions

Generate a list of potential reactions (e.g., Love, Haha, Wow, Sad, Angry) and prioritize using criteria: frequency of need, distinctiveness from Like, universality across cultures, and technical feasibility. Use a scoring matrix.

4. Validate with Experiments

Test the top candidates via A/B tests in select markets to measure impact on engagement and sentiment. Iterate based on data, and consider cultural nuances before finalizing.

5. Launch and Iterate

Launch a minimal set (e.g., 5-6 reactions) and monitor metrics. Plan for future expansion based on user feedback and data, ensuring the system remains simple and scalable.

Key Points to Mention

  • User empathy: understanding the need for nuanced emotional expression beyond Like.
  • Data-driven prioritization: using research and metrics to select reactions.
  • Simplicity and scalability: starting small to avoid overwhelming users and allowing future additions.
  • Cultural considerations: ensuring reactions are universally understood or adaptable.
  • Impact on existing behavior: monitoring how Reactions affect Likes, comments, and overall engagement.
  • Competitive analysis: learning from other platforms' use of reactions (e.g., Slack, Messenger).

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.