I went straight for engagement metrics and kind of forgot to anchor on what problem the AI was actually solving first.
Start by clarifying the AI feature's goal and the platform's key objectives (e.g., engagement, retention, revenue). Then propose a metrics framework that includes guardrail, success, and counter metrics, and describe how you'd validate impact through A/B testing and long-term holdouts.
Pro tip: Emphasize that AI features often have delayed or indirect effects, so you should measure both immediate engagement and long-term retention, and be wary of novelty effects. Also, consider the cost of AI (e.g., compute) as part of the ROI calculation.
Ask clarifying questions to understand what the AI feature is designed to do (e.g., improve recommendations, personalize content) and which company objectives it supports (e.g., increase watch time, reduce churn).
Identify one primary success metric (e.g., increase in daily watch time per user) and supporting secondary metrics (e.g., click-through rate, completion rate). Also define guardrail metrics (e.g., streaming quality, user complaints) and counter metrics (e.g., diversity of content consumed).
Propose an A/B test with a control group, ensuring proper randomization and sufficient power. Consider long-term holdout groups to measure sustained impact and detect novelty effects.
Evaluate statistical significance, segment results by user cohorts (e.g., new vs. existing users), and assess trade-offs between metrics. Use insights to iterate on the feature or rollout strategy.
After full rollout, continue tracking metrics to ensure sustained success, and calculate return on investment by comparing gains against costs (e.g., infrastructure, engineering).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.