I went straight for user need segmentation and probably spent too long on that before getting to metrics.
Start by clarifying the goal of adding a reaction—likely to increase user expression and engagement—then identify user needs and gaps in the current set. Propose a hypothesis-driven approach: validate the need through research and data, define success metrics, and design an A/B test to measure impact post-launch.
Pro tip: Anchor your answer in a clear product philosophy: reactions should reduce friction in expressing nuanced emotions. Mention that adding a reaction is not just about usage but about whether it improves the overall ecosystem (e.g., reduces negative comments or increases meaningful interactions).
Define why Facebook might add a reaction: to enhance self-expression, capture nuanced feedback, or increase engagement. Identify user segments and use cases where current reactions fall short.
Analyze existing reaction usage patterns, sentiment analysis of comments, and user surveys to find gaps. Look for frequent requests or workarounds (e.g., using comments to express a missing emotion).
Establish primary metrics (e.g., reaction usage, engagement rate) and guardrail metrics (e.g., negative feedback, comment quality). Form a hypothesis: adding reaction X will increase Y by Z% without harming guardrails.
Propose an A/B test with a control group (current reactions) and treatment (new reaction). Define sample size, duration, and rollout strategy (e.g., gradual release to monitor).
After launch, track metrics against baseline, segment by user type, and gather qualitative feedback. Decide whether to keep, modify, or remove the reaction based on results.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.