I started with user trust and engagement metrics and felt pretty good about it, but then realized I was basically just listing numbers without a clear north star.
Start by clarifying the goal of the blue verified checkmark feature—likely to authenticate notable accounts, reduce impersonation, and increase trust. Then define success metrics across user, creator, and platform dimensions, and propose how to measure them (e.g., A/B tests, holdouts, or pre/post analysis). Finally, prioritize metrics and discuss trade-offs.
Pro tip: Acknowledge that verification is a trust signal, so success isn't just about adoption but also about reducing impersonation reports and increasing user confidence. Mention that you'd track counter-metrics like false negatives (legitimate users unable to get verified) to avoid unintended consequences.
Confirm that the blue checkmark aims to verify authentic notable accounts, reduce impersonation, and build trust. This ensures metrics align with the intended purpose.
Identify metrics for users (trust, engagement), creators (verification rate, satisfaction), and platform (impersonation reports, support tickets). Include both quantitative and qualitative measures.
Propose A/B testing where possible (e.g., rollout to random groups), holdout groups, or pre/post analysis. Use surveys for trust perception and log analysis for behavioral metrics.
Rank metrics by importance (e.g., reduction in impersonation as primary) and set realistic targets. Consider leading and lagging indicators.
Establish dashboards to track metrics over time, watch for unintended consequences (e.g., decreased engagement due to perceived exclusivity), and be ready to adjust.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.