I went with watch time per daily active user, like-to-view ratio, and session frequency.
Start by clarifying the goal of measuring engagement—whether it's to optimize for retention, time spent, or meaningful interactions—and then propose a balanced set of metrics that capture depth, breadth, and quality of engagement. Structure your answer around a framework like 'active engagement, passive consumption, and retention' to show strategic thinking, and tie each metric to a business outcome.
Pro tip: Emphasize that no single metric is perfect and that you'd triangulate multiple metrics to avoid gaming; mention that you'd validate metrics through A/B tests and guardrail metrics to ensure they align with long-term user value.
Ask whether the goal is to maximize short-term engagement, long-term retention, or user satisfaction, as this shapes metric selection.
Break engagement into active (e.g., likes, comments, shares), passive (e.g., watch time), and retention (e.g., return frequency) to cover the full spectrum.
Choose three metrics that balance these dimensions, such as average watch time per user, daily active users (DAU) or session frequency, and interaction rate (likes/comments/shares per view).
Explain why each metric matters, how they complement each other, and how they might be gamed or misused, suggesting guardrails.
Mention how you would validate these metrics through A/B tests and monitor them alongside counter-metrics to ensure healthy engagement.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the goal and primary metric (e.g., CTR or engagement), then walk through the statistical steps: define hypotheses, calculate sample size using power analysis, determine duration based on traffic and novelty effects, and set stopping criteria that balance statistical rigor with practical constraints. Emphasize the importance of pre-registration and avoiding peeking to maintain validity.
Pro tip: At Meta, always consider the network effects and potential interference between users in social experiments; mention using cluster-based randomization or switchback tests when appropriate. Also, be prepared to discuss how you'd handle multiple testing corrections if you're evaluating multiple metrics.
Clearly state the null and alternative hypotheses, and select a primary metric (e.g., click-through rate) along with guardrail metrics (e.g., user satisfaction, latency).
Use power analysis: specify significance level (α=0.05), power (1-β=0.8), minimum detectable effect (MDE), and baseline variance to compute required sample size per variant.
Estimate daily traffic to the experiment, divide required sample size by daily traffic to get minimum days, then adjust for weekly seasonality and novelty effects (e.g., run for at least 1-2 weeks).
Pre-register stopping rules: either fixed-horizon (stop after reaching sample size) or sequential testing with alpha-spending to allow early stopping for efficacy/futility while controlling Type I error.
After experiment concludes, perform statistical tests (e.g., t-test or bootstrap) on primary metric, check guardrails, and consider practical significance before making a ship/no-ship decision.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.