I started talking about correlational analysis between notification rank position and account selection rate, which felt right, but I kept second-guessing myself on the selection bias piece.
Use historical session-level logs to simulate the new ranking offline, then compare key engagement metrics (e.g., notification clicks, session duration) between the current and proposed ranking. Validate the simulation with a holdout or counterfactual analysis to estimate the potential impact before running a live experiment.
Pro tip: Leverage the fact that ranking changes can have heterogeneous effects across user segments; analyze results by account activity level and notification volume to uncover where the change might help or hurt. Also, check for novelty effects by examining long-term trends in the historical data.
Identify the primary metric (e.g., notification click-through rate) and guardrail metrics (e.g., user retention) that the ranking change should impact. Formulate a hypothesis about how ranking by notification count will affect user engagement.
Using session-level logs, for each user session, compute the ranking of accounts under both the current (recency) and proposed (notification count with recency tiebreaker) schemes. Ensure you have the necessary data on account visits and notification counts at the time of each session.
Estimate what users would have clicked or engaged with under the new ranking by using models or heuristics (e.g., assume users click the top-ranked account with some probability). Compare simulated metrics to actual historical metrics to gauge potential lift.
Break down the simulated impact by user segments (e.g., heavy vs. light users, high vs. low notification volume) to understand where the change is most beneficial. Perform sensitivity analysis on assumptions (e.g., click probability) to test robustness.
Based on the offline analysis, determine if the change is promising enough to warrant a live A/B test. If so, design the experiment with appropriate power, duration, and guardrail metrics to validate the findings.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the product context and defining the causal question, then systematically walk through each design element: eligible population, randomization unit, metrics, guardrails, and power analysis. Emphasize trade-offs and justify choices with statistical reasoning and practical constraints.
Pro tip: Mention that you would check for network effects or interference between users, especially if the ranking affects content visibility, and consider using cluster randomization if needed. Also, proactively discuss how you would handle multiple testing corrections for secondary metrics.
Clarify the product change and target population: who is eligible (e.g., active users who can receive notifications), and exclude users with prior exposure or those in conflicting experiments. Consider whether the ranking affects only notification recipients or broader user experience.
Decide whether to randomize at user level or a cluster level (e.g., by social graph clusters) to avoid interference. Ensure proper randomization and balance checks, and consider using a holdout group for long-term effects.
Define a clear primary metric (e.g., notification click-through rate or engagement) that directly measures the impact. Choose secondary metrics (e.g., session time, retention) to capture broader effects, and guardrail metrics (e.g., user reports, unsubscribe rate) to monitor negative impacts.
Determine the minimum detectable effect (MDE) based on business relevance, then calculate required sample size and duration using power analysis. Account for variance, expected effect size, and multiple comparisons.
Predefine key segments (e.g., new vs. existing users, heavy vs. light users) for heterogeneous treatment effect analysis. Address edge cases like users with no notifications, inactive users, and potential novelty effects.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.