I went with something like revenue per session filtered to sessions with at least one ranking impression, and the interviewer pushed back immediately asking why not per user or per page load.
Start by clarifying the product goal (e.g., increase user engagement or revenue) and the specific behavior the ranking feature aims to influence. Then define a primary success metric that directly measures that goal, with a precise formula including numerator, denominator, and filters. Finally, justify why this metric is the best choice and how you would validate it through experimentation.
Pro tip: Choose a metric that balances sensitivity and business relevance—avoid purely engagement metrics that can be gamed, and instead anchor to a long-term value metric like revenue per session or successful conversion rate. Also, mention guardrail metrics to ensure the ranking doesn't harm other key areas.
Ask clarifying questions to understand the product's objective (e.g., increase clicks, conversions, or revenue) and how the personalized ranking is expected to drive that. State your assumption explicitly.
Propose a metric that directly measures the goal, such as click-through rate (CTR) on the listing page or conversion rate. Provide the exact formula with numerator and denominator.
Detail any filters: user segments (e.g., new vs. returning), time window (e.g., first 7 days), device type, and exclude bot traffic or internal users. Explain why these filters matter.
Explain why this metric is the best proxy for success, and mention guardrail metrics (e.g., latency, diversity of results) to monitor unintended consequences.
Briefly describe how you'd test the metric via A/B experiment, including sample size, duration, and statistical significance. Mention potential pitfalls like novelty effects.
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Start by defining guardrail metrics as metrics that should not degrade significantly during an experiment, then select three that cover different failure modes (e.g., user experience, business health, system performance). For each, specify a concrete threshold (e.g., 'no more than a 2% relative decrease') and explain the failure mode it protects against, using examples from Google's context.
Pro tip: Tie each guardrail to a real or plausible Google product scenario (e.g., Search, Ads, YouTube) and mention how you'd monitor them with sequential testing or automated alerts to show practical maturity.
Explain that guardrail metrics are metrics that should not be harmed by the experiment, and they act as safety checks to prevent launching changes that hurt key aspects of the product.
Choose three guardrail metrics that cover different failure modes: user experience (e.g., task success rate), business health (e.g., revenue per user), and system performance (e.g., page load time).
For each metric, state a concrete threshold, such as 'no more than a 1% relative decrease' or 'no more than a 5% increase in latency', and justify why that threshold is meaningful.
For each metric, describe the failure mode it protects against, e.g., user frustration, revenue loss, or system overload, and how that would manifest if the metric degraded.
Conclude by explaining how you would monitor these guardrails during the experiment and what actions you'd take if a threshold is breached (e.g., pause the experiment, investigate).
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User-level randomization was the obvious call here since personalization is user-specific and you'd get interference issues with session-level.
Start by clarifying the experiment's goal and constraints, then systematically address each component: randomization unit, exposure rules, pre-exposure filtering, stratification, and cross-device repeat visitors. Emphasize trade-offs and justify your choices based on validity, sensitivity, and practical implementation.
Pro tip: Always consider the 'trigger' or 'exposure' point as the moment of randomization to avoid dilution and ensure valid causal inference. For cross-device users, use a stable user ID (e.g., Google account) when available; otherwise, acknowledge limitations and propose sensitivity analyses.
Understand what metric you're trying to move, the population, and any technical constraints (e.g., login status, device graph availability). This informs the choice of randomization unit and exposure rules.
Decide between user-level, session-level, or device-level randomization. Justify based on the intervention, interference risk, and ability to track users across devices. User-level is often preferred for consistency and to capture cross-device effects.
Specify when a user is considered exposed (e.g., first view of the feature) and filter out users who were exposed before the experiment start. This prevents contamination and ensures clean pre-period baselines.
Use stratification to balance key covariates (e.g., device type, geography) and improve sensitivity. For repeat visitors across devices, use a consistent user identifier (e.g., logged-in ID) to assign the same variant; if unavailable, discuss potential biases and mitigation.
Run A/A tests, check for sample ratio mismatch (SRM), and monitor key metrics to ensure the randomization and exposure logic are working as intended. Be prepared to adjust if issues arise.
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The formula is n = 2 * (z_alpha/2 + z_beta)^2 * sigma^2 / delta^2, where delta is the absolute lift.
Start by clarifying the metric and assumptions: the primary metric is a per-user-day mean, so the unit of analysis is user-day. Compute the absolute lift from the relative lift (1.5% of 3.2 = 0.048), then use the standard formula for sample size per arm for a two-sample t-test: n = 2 * (z_{α/2} + z_β)^2 * σ^2 / Δ^2. Plug in z_{0.025}=1.96, z_{0.10}=1.28, σ=2.1, Δ=0.048, and round up to the nearest integer.
Pro tip: Always state the unit of analysis (user-day vs. user) and mention that if the metric is per-user-day, you need to account for the number of days per user to get the number of users, and consider clustering if multiple observations per user. Also, note that this calculation assumes independent observations and normally distributed data; for skewed metrics, consider transformations or non-parametric methods.
Confirm that the primary metric is a mean per user-day, and that the lift is relative to the baseline mean. State assumptions: independent observations, known variance (or large sample), two-sided test, equal allocation.
Convert the relative lift to an absolute difference: Δ = baseline_mean * relative_lift = 3.2 * 0.015 = 0.048.
For two-sided alpha=0.05, z_{α/2}=1.96. For 90% power, z_β=1.28 (since β=0.10).
Use n = 2 * (z_{α/2} + z_β)^2 * σ^2 / Δ^2. Plug in values: n = 2 * (1.96+1.28)^2 * (2.1)^2 / (0.048)^2. Compute step by step.
Perform the arithmetic: (1.96+1.28)=3.24, squared=10.4976; σ^2=4.41; Δ^2=0.002304. Numerator: 2*10.4976*4.41 ≈ 92.59. Divide by 0.002304 ≈ 40187. Round up to 40188 per arm.
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Fixed horizon is safer for avoiding peeking inflation, I said I'd use alpha spending only if there was a strong business reason to peek early.
Structure your answer around three pillars: stopping rule, variance reduction, and SRM detection. For the stopping rule, discuss sequential testing or fixed-horizon with alpha spending, and explain how you balance speed and error control. For variance reduction, compare CUPED and re-randomization, highlighting when each is appropriate, and for SRM, describe a daily test like chi-square or sequential probability ratio test with practical thresholds.
Pro tip: Emphasize that SRM checks should be automated and run daily, but avoid overreacting to minor deviations—use a sequential test to control false alarms. Also, mention that CUPED requires pre-experiment data and works best when correlated with the metric, while re-randomization is a design-time solution.
Start by asking about the experiment's objectives, expected effect size, and constraints (e.g., traffic, duration). This shows you tailor solutions to business needs.
Discuss options like fixed-horizon with power analysis, sequential testing (e.g., always-valid p-values), or Bayesian methods. Explain trade-offs between early stopping and error control.
Explain CUPED: use pre-experiment covariates to adjust post-experiment metrics, reducing variance. For re-randomization, describe how to balance covariates at assignment to improve sensitivity.
Describe running a chi-square test on sample ratios daily, with a low p-value threshold (e.g., 0.001) to flag issues. Mention sequential testing to account for multiple looks.
Combine these methods into a monitoring dashboard, and set up alerts for SRM and stopping criteria. Discuss how to handle violations (e.g., pause experiment, investigate).
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Start by outlining a structured visualization and diagnostic strategy for experiments, emphasizing quantile treatment effects, funnel breakouts, and time-since-exposure plots. Then, explain how to interpret heterogeneous treatment effects rigorously, using pre-registration, correction for multiple comparisons, and validation to avoid p-hacking. Conclude with a practical example or framework that ties these elements together.
Pro tip: Always pre-register your hypotheses and analysis plan to prevent p-hacking, and use simulation or bootstrapping to validate that your heterogeneous treatment effect findings are not artifacts of multiple testing.
Use density plots, QQ plots, or box plots to compare distributions between control and treatment groups. For quantile treatment effects, plot the difference in quantiles (e.g., 10th, 50th, 90th) with confidence intervals to see where the treatment has the most impact.
Break down the conversion funnel by step and segment. Visualize conversion rates at each stage with error bars, and use heatmaps or bar charts to identify where the treatment causes drop-offs or improvements.
Plot the treatment effect over time since exposure to detect novelty effects, delayed effects, or decay. Use line charts with confidence bands to show how the effect evolves.
Pre-register subgroups and hypotheses. Use methods like Bonferroni correction, false discovery rate control, or Bayesian shrinkage to adjust for multiple comparisons. Validate findings with holdout sets or replication.
Present findings with clear visualizations and caveats. Recommend follow-up experiments to confirm heterogeneous effects, and emphasize that correlation does not imply causation without proper validation.
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