I went with user-level randomization pretty quickly and they pushed back asking why not session.
Start by clarifying the goal: to measure the causal impact of the ranking algorithm on ad performance. Then discuss possible randomization units (user, session, ad impression) and evaluate tradeoffs like interference, variance, and implementation feasibility. Conclude with a recommendation based on the specific context of shop ads at Meta.
Pro tip: Mention that the choice of randomization unit should align with the unit of analysis and the mechanism of the treatment effect. Also, consider using cluster randomization if interference is a concern, but be aware of increased variance.
Understand what metrics you want to improve (e.g., CTR, conversion rate) and any technical or business constraints (e.g., user experience, system limitations).
List feasible units: user, session, ad impression, or even ad campaign. Consider the level at which the treatment is applied and the level at which you measure outcomes.
For each unit, discuss interference (spillover), variance, sample size requirements, and implementation complexity. For example, user-level randomization avoids interference but may have higher variance if users see multiple ads.
Shop ads may involve multiple ads from the same shop or interactions between ads. If the algorithm changes ranking within a shop, user-level randomization might dilute the effect; impression-level might capture it but introduce interference.
Choose the unit that best balances validity and practicality. Often, user-level randomization is preferred for its simplicity and lack of interference, but if the treatment is at the impression level, consider session-level or impression-level with careful handling of interference.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I listed CTR, CVR, and revenue per session as primaries, then added advertiser ROI and long-term retention as secondary.
Start by clarifying the goal of the new ads ranking algorithm—likely to improve ad relevance and user experience while maintaining advertiser value. Then define primary metrics that directly measure success (e.g., ad CTR, conversion rate) and guardrail metrics that ensure no harm to user experience or ecosystem health (e.g., user engagement, ad load). Finally, discuss how to monitor these metrics in an A/B test and make a launch decision.
Pro tip: Emphasize the trade-off between short-term ad revenue and long-term user trust, and mention that guardrails should be set with pre-defined thresholds to avoid p-hacking. Also, consider segment-level analysis to detect disparate impacts.
Confirm that the new ranking algorithm aims to improve ad performance (e.g., CTR, conversions) without degrading user experience. This sets the context for choosing metrics.
Select 1-2 metrics that directly measure the algorithm's intended outcome, such as ad click-through rate (CTR), conversion rate, or return on ad spend (ROAS). These should be sensitive to the change.
Choose metrics that ensure no negative side effects, such as user engagement (time spent, DAU), user satisfaction (surveys), ad load, and advertiser diversity. Set acceptable thresholds.
Describe how to measure these metrics in a controlled experiment, including statistical power, duration, and segment analysis. Mention the need to correct for multiple comparisons.
Outline criteria for success: primary metric improves significantly and guardrails remain within thresholds. If trade-offs exist, discuss how to weigh them.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Mentioned winsorization and log transforms for the heavy tail problem.
Start by clarifying the experiment's goal and the primary metric, then discuss how to handle skewness in revenue metrics through transformations or alternative approaches. Outline a step-by-step power analysis that accounts for the distribution, and emphasize the importance of simulation and sensitivity analysis.
Pro tip: Mention that at Meta, revenue metrics are often analyzed using bootstrapping or quantile-based methods to handle skewness, and that power analysis should be done on the transformed metric or using simulations that preserve the distributional properties.
Define the primary metric (e.g., revenue per user), the unit of analysis, the expected effect size, significance level, and desired power. Also consider guardrail metrics and multiple testing corrections.
Examine historical data to understand the skewness of the revenue metric. Decide whether to use parametric methods (with transformations like log) or non-parametric methods (e.g., bootstrapping, quantile regression).
Use simulations or analytical formulas (e.g., for t-test on transformed data) to estimate the required sample size. For skewed data, simulations that resample from historical data are often more reliable.
Check the power analysis under different assumptions (e.g., varying effect sizes, different transformations) and validate with historical A/B tests if available. Consider the impact of outliers and heavy tails.
Summarize the recommended sample size and duration, highlighting assumptions and limitations. Discuss potential risks and mitigation strategies with stakeholders.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the metric definitions and the scope of the change, then systematically walk through potential reasons why CTR and revenue could diverge, grouping them into categories like ad quality, user behavior, and auction dynamics. For each reason, propose a specific investigation using data segmentation, A/B test analysis, and causal inference methods.
Pro tip: Emphasize that you would first validate the data pipeline and metric definitions to rule out logging or attribution errors, as these are common culprits in such scenarios. Also, consider the possibility of cannibalization: higher CTR on lower-value ads might reduce overall revenue.
Confirm the exact metrics (CTR, revenue), time period, and algorithm change. Check for data quality issues, logging errors, or metric definition changes that could explain the divergence.
Break down metrics by user segments, ad types, placements, and geographies to identify where CTR increased but revenue decreased. Look for shifts in mix (e.g., more low-value ad clicks).
Generate hypotheses: (1) Ad quality shift: algorithm favors high-CTR but low-revenue ads. (2) Auction dynamics: changes in bid landscape reduce revenue per click. (3) User behavior: increased clicks on low-intent ads leading to lower conversion. (4) Cannibalization: higher CTR on organic vs. paid. (5) Seasonality or external factors.
Use A/B test data to compare treatment and control groups. Perform causal analysis (e.g., holdout, switchback) to isolate the algorithm's effect. Check for interactions with other experiments.
Based on findings, suggest adjustments: re-weight the algorithm to balance CTR and revenue, adjust bidding strategies, or refine targeting. Monitor long-term effects.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Covered SRM checks, bot traffic filtering, novelty effects requiring longer run times, and multiple testing corrections.
Start by acknowledging that ads experiments have unique challenges due to user-level randomization, interference, and business metrics. Then, structure your answer around the experiment lifecycle: design, execution, analysis, and interpretation, highlighting key pitfalls at each stage and how to detect them.
Pro tip: Emphasize the importance of pre-registration and guardrail metrics to prevent p-hacking and ensure user experience is not harmed. Also, mention that you would simulate or backtest the experiment design to estimate power and detect subtle biases before launch.
Watch for inadequate power, wrong randomization unit, and interference between users. Use power analysis, cluster randomization if needed, and consider switchback or geo experiments to mitigate interference.
Monitor for sample ratio mismatch (SRM), data pipeline issues, and novelty/primacy effects. Implement real-time dashboards and automated alerts for SRM and metric anomalies.
Avoid multiple comparisons, peeking, and ignoring heterogeneous treatment effects. Use sequential testing or Bayesian methods, correct for multiple testing, and pre-specify subgroups.
Beware of confusing statistical with practical significance, and of external validity issues. Consider business impact, long-term effects, and whether results generalize beyond the experiment.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.