I went in thinking this was a metrics question and it kind of is, but it's also a product health question and an experiment design question all at once.
Start by clarifying the goal of the feature—likely to increase meaningful engagement and expression—then define success metrics across user engagement, content quality, and business impact. Propose specific A/B tests to measure causal effects, and consider guardrail metrics to avoid negative side effects.
Pro tip: Emphasize that reactions are not just about engagement quantity but also about sentiment and expression; therefore, measure both the distribution of reactions and their impact on downstream behavior like commenting and sharing.
Identify the primary objectives of the emotion reaction feature, such as increasing user expression, improving content ranking signals, or boosting engagement. Formulate hypotheses about how reactions affect user behavior.
Select metrics that capture the feature's impact: engagement metrics (e.g., reaction rate, posts per user), sentiment/expression metrics (e.g., diversity of reactions), and business metrics (e.g., time spent, retention). Include guardrail metrics like negative feedback or spam reports.
Propose controlled experiments comparing the reaction feature against a baseline (e.g., like-only). Randomize users, ensure sufficient power, and measure both short-term and long-term effects. Consider holdout groups for long-term impact.
Evaluate statistical significance, segment by user demographics and content types, and check for novelty effects. Use qualitative feedback to understand why certain reactions are used. Iterate on the feature based on findings.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.