The accidental clicks part is what tripped me up.
Start by defining a metric framework that covers ad revenue, user engagement, and user experience, then outline a rigorous A/B test design with randomization, sample size, and guardrail metrics. Finally, address accidental clicks by proposing methods to detect and mitigate them, and explain how to analyze their impact on the decision.
Pro tip: Emphasize that accidental clicks can inflate CTR and mislead launch decisions; propose using dwell time or post-click behavior to filter them out, and consider running a holdout group to measure long-term effects.
Identify primary metrics (e.g., ad revenue per user, CTR) and secondary metrics (e.g., DAU, session length, retention) to capture both monetization and user experience impacts.
Randomize users into control (no banner) and treatment (banner) groups, determine sample size and duration based on power analysis, and pre-register hypotheses and metrics.
Define accidental clicks (e.g., clicks with very short dwell time or immediate back navigation) and plan to measure them via event logging; consider excluding them from primary analysis or analyzing separately.
Compare metrics between groups using statistical tests, check guardrail metrics for negative impacts, and decide launch based on net positive effect and business goals.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the feature's goal (e.g., increase group engagement and content creation) and then define a metric hierarchy: primary success metric, secondary metrics, and guardrail metrics. For the experiment, specify the randomization unit (e.g., user or group), calculate sample size based on desired power and minimum detectable effect, and determine run duration considering novelty effects and business cycles. Finally, discuss analysis plan including heterogeneous treatment effects and potential pitfalls.
Pro tip: Emphasize that the randomization unit should align with the metric's unit of analysis to avoid dilution or contamination; for group-story features, randomizing by group may be more appropriate than by user, but consider network effects. Also, mention that you would run an A/A test or check pre-experiment balance to validate the setup.
Ask clarifying questions to understand what the group-story feature entails (e.g., collaborative stories within friend groups) and what business objectives it serves (e.g., increase daily active users, time spent, or content creation).
Propose a primary metric (e.g., number of group stories created per user per week) and secondary metrics (e.g., group story views, reactions, retention). Include guardrail metrics (e.g., app performance, user reports) to ensure no negative impact.
Choose randomization unit (e.g., user or group) based on where the treatment is applied and how metrics are measured. Discuss potential interference and consider cluster randomization if needed.
Calculate required sample size using power analysis (e.g., 80% power, 5% significance, minimum detectable effect from historical data or business relevance). Estimate run time based on traffic and desired sample size, accounting for novelty and primacy effects.
Outline analysis plan: compare primary metric between control and treatment, check guardrails, segment by user demographics or group size, and decide whether to launch, iterate, or abandon based on results.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.