I went straight to total watch time as the north star and listed a few guardrail metrics like session drop-off rate and support contacts.
Start by defining primary metrics that capture the core value of auto-play (e.g., increased watch time or episodes started) and guardrail metrics that ensure no harm (e.g., user retention or satisfaction). Then outline an A/B test design specifying randomization unit (user-level), key parameters (sample size, duration), and analysis plan to measure impact.
Pro tip: Emphasize that guardrails should include both user experience metrics (e.g., churn, complaints) and system metrics (e.g., bandwidth, latency) to catch unintended consequences. Also, mention that run duration should cover at least one full content cycle to account for novelty effects.
Identify metrics that directly measure the success of auto-play, such as average watch time per user, number of episodes started per session, or completion rate of next episode.
Select metrics to monitor for negative side effects, including user retention, churn rate, explicit user feedback (e.g., thumbs down), and technical performance (e.g., buffering ratio).
Choose unit of randomization (typically user-level to avoid contamination), determine sample size based on expected effect size and power, and set run duration to capture weekly seasonality and novelty effects (e.g., 2-4 weeks).
Pre-register analysis methods: compare primary and guardrail metrics between control and treatment using appropriate statistical tests, check for heterogeneous treatment effects, and ensure novelty/primacy effects are accounted for.
Establish criteria for success: primary metric must show significant positive lift without guardrail metrics degrading beyond acceptable thresholds. Consider long-term holdback for retention impact.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the business objective and defining what 'power users' and 'churn' mean in this context, then evaluate whether the total watch time increase is driven by the same segment or by other users. Weigh the trade-offs using a decision framework that considers statistical significance, effect size, and long-term impact, and recommend a path forward such as launching with guardrails, iterating, or running a follow-up experiment.
Pro tip: Don't just look at the aggregate metric; segment the data to see if the watch time increase is concentrated in non-power users and if the churn is statistically significant. Also, consider the long-term value of power users—they often drive disproportionate revenue and engagement, so short-term watch time gains may not justify losing them.
Define what constitutes a 'power user' and how churn is measured (e.g., 7-day inactivity). Confirm the primary success metric and any guardrail metrics for the experiment.
Check if the churn increase and watch time increase are statistically significant and practically meaningful. Look at confidence intervals and p-values for both metrics.
Break down the metrics by user segments to see if the watch time increase is driven by non-power users while power users churn. Investigate potential reasons for churn (e.g., feature changes, bugs).
Estimate the long-term value of power users versus the short-term watch time gain. Consider revenue, retention, and network effects. Use a decision matrix or expected value calculation.
Based on the analysis, recommend launching, not launching, or iterating. Suggest follow-up experiments or guardrail metrics to monitor if launching.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.