I went straight to engagement metrics and started talking about surfacing reactions more prominently in the feed algorithm.
Start by clarifying the goal of optimizing reactions—whether it's increasing engagement, improving content ranking, or enhancing user expression. Then, analyze current usage data to identify gaps and opportunities, and propose data-driven improvements with clear success metrics.
Pro tip: Tie your recommendations to Meta's business objectives, such as increasing meaningful interactions or ad revenue, and consider the trade-offs between user experience and platform goals.
Ask clarifying questions to understand what 'optimize' means in this context—e.g., increase usage, improve sentiment analysis, or boost engagement. Align on the primary goal and constraints.
Examine existing data on reaction usage: which reactions are most/least used, how they correlate with engagement, and any user feedback. Identify pain points or underutilized reactions.
Brainstorm potential optimizations, such as adding new reactions, simplifying the reaction picker, personalizing suggestions, or using reactions to improve ranking algorithms.
Evaluate ideas based on impact and effort, and define success metrics (e.g., reaction rate, diversity of reactions, time spent, or downstream engagement). Consider A/B testing.
Outline a phased plan: quick wins, experiments, and long-term bets. Include how you would measure success and iterate based on results.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.