I went with confirmed-friends-per-DAU, acceptance rate, and qualified exposures-per-DAU as primaries, with a 14-day window.
Start by clarifying the product goal and the specific feature (friend recommendations) to align metrics with user value. Then define primary success metrics that capture both immediate engagement and long-term network health, and guardrail metrics to prevent negative side effects. Finally, specify attribution windows for each metric based on expected user behavior and business cycles, justifying your choices.
Pro tip: Emphasize that attribution windows should be pre-registered and aligned with the metric's sensitivity to the treatment; for example, use shorter windows for click-through and longer for retention to avoid diluting effects. Also, mention the importance of considering network effects and potential interference in social experiments.
Understand the purpose of the friend-recommendation algorithm: is it to increase connections, engagement, or long-term retention? This guides metric selection.
Choose metrics that directly measure the feature's intended impact, such as number of new friend connections, acceptance rate of recommendations, or downstream engagement (e.g., DAU, sessions).
Identify metrics that ensure the feature doesn't harm user experience or platform health, such as user reports, unfriend rate, or overall time spent.
For each metric, determine the appropriate time window to attribute the effect, based on expected user response time and metric stability. Justify with reasoning.
Address potential issues like network effects, novelty effects, and long-term holdouts to ensure robust measurement.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining the experimental unit based on the randomization unit and the level at which the treatment is applied, then discuss how network interference can violate the Stable Unit Treatment Value Assumption (SUTVA). Propose methods to detect, measure, and mitigate interference, such as cluster randomization, switchback experiments, or using a graph-based approach.
Pro tip: At Meta, where social networks are core, always consider the direction and magnitude of interference—it can bias results in either direction. Mentioning concrete examples like Facebook's cluster randomization or LinkedIn's ego-network experiments shows practical maturity.
Identify the unit of randomization (e.g., user, session, page) and the unit of analysis, ensuring they align with the treatment and outcome. Explain why the chosen unit is appropriate for the experiment's goals.
Describe how interactions between units (e.g., social influence, sharing) can cause spillover effects, violating SUTVA and biasing estimates. Give examples relevant to Meta's products.
Discuss methods to detect interference, such as comparing treatment and control groups' network exposure or using A/A tests with varying cluster sizes. Mention metrics like network overlap or exposure probability.
Propose design solutions: cluster randomization (randomize by community, geographic region, or social cluster), switchback experiments (time-based randomization), or using a graph cluster randomization algorithm. Discuss trade-offs like reduced power and increased variance.
After mitigation, validate assumptions and consider analysis techniques like exposure modeling or causal inference methods (e.g., instrumental variables) to account for residual interference.
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Start by clarifying the goal of the experiment and the primary decision metric, then evaluate each result in light of statistical significance, practical significance, and potential trade-offs. Weigh the significant increase in MAU against the significant decrease in engagement per user, and consider the non-significant friend metric as inconclusive. Finally, apply a decision rule that prioritizes long-term user value and aligns with company objectives.
Pro tip: Don't just look at p-values; consider the confidence intervals and effect sizes. A 5% MAU increase might be driven by low-quality users, so segment the analysis to see if the engagement drop is concentrated in a particular group.
Identify the primary success metric (e.g., MAU or engagement) and the overall goal of the feature. Confirm whether the experiment was designed to detect changes in these metrics and if the results are trustworthy (e.g., no sample ratio mismatch).
For each metric, note whether the change is statistically significant and whether the magnitude is practically meaningful. For the non-significant friend metric, consider the confidence interval and whether the effect could be meaningful if real.
Weigh the benefits (MAU increase) against the costs (engagement drop). Segment by user cohorts (new vs. existing, heavy vs. light users) to understand if the engagement decline is broad or concentrated, and if MAU growth is sustainable.
Use a pre-defined rule: launch if the primary metric improves significantly without harming key secondary metrics. Here, the engagement drop is concerning; consider a holdout or a follow-up experiment to validate long-term effects.
If the trade-off is unclear, recommend a limited launch or further analysis (e.g., long-term holdout) rather than a full launch. Justify your recommendation with data and business context.
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I picked some placeholder ARPU and margin numbers and worked through it.
Start by mapping the metric to revenue through a clear causal chain: confirmed friends per DAU → engagement → ad impressions → revenue. Then estimate the revenue impact using assumptions about ARPU and the relationship between friends and engagement, and finally determine the minimum detectable effect (MDE) needed to justify a full rollout based on statistical power and business thresholds.
Pro tip: Anchor your estimate with a sensitivity analysis: show how the revenue impact varies with key assumptions like ARPU and the elasticity of revenue to friends. This demonstrates rigor and helps you identify the minimum effect size that would be statistically and practically significant.
Explain how confirmed friends per DAU influences revenue: more friends → higher engagement → more ad impressions → more revenue. Identify intermediate metrics and assumptions.
Use known or assumed figures for DAU, ARPU, and ad revenue per user to establish a baseline. Calculate total annual revenue from the user base.
Assume a proportional relationship between friends per DAU and revenue, or use an elasticity estimate. Apply the 2% lift to the baseline revenue to get an estimated annual impact.
Consider the cost of rollout, statistical power, and business thresholds. Calculate the minimum lift that would yield a positive ROI and be detectable in an A/B test.
Test how the revenue impact changes with different assumptions (e.g., ARPU, elasticity). Present a range and recommend a decision criterion.
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Talked about real-time dashboards on the guardrail metrics, sequential testing to avoid peeking problems, and pre-specified thresholds like 'if spam reports exceed X% of baseline within 48 hours, auto-rollback.' Felt like the most natural part of the whole conversation for me.
Start by outlining a comprehensive monitoring plan that tracks both guardrail metrics (e.g., latency, error rates, revenue) and key product metrics (e.g., CTR, engagement) in real-time. Then, define clear statistical thresholds for regression detection, such as sequential testing or Bayesian methods, and specify automatic rollback triggers based on both statistical significance and practical significance. Emphasize the importance of pre-registering these criteria and having a rollback protocol ready.
Pro tip: At Meta, where experimentation velocity is high, it's crucial to balance sensitivity and specificity in rollback triggers to avoid false alarms while catching true regressions quickly. Consider using a combination of absolute and relative thresholds, and always validate with a holdback group.
Identify the key metrics that should not degrade (guardrails) and those that should improve (success metrics). Include both technical (e.g., latency, error rates) and business (e.g., revenue, engagement) metrics.
Implement dashboards and alerts that track these metrics continuously during and after launch. Use statistical process control or sequential testing to detect anomalies early.
Define statistical thresholds (e.g., p-value < 0.05, Bayesian posterior probability) and practical thresholds (e.g., >2% drop in revenue) that indicate a regression. Pre-register these criteria to avoid p-hacking.
Specify conditions that trigger an automatic rollback, such as a guardrail metric exceeding a threshold for a sustained period or a statistically significant negative impact on a key metric. Include both absolute and relative thresholds.
Ensure there is a clear, automated process to revert the change quickly. After rollback, conduct a post-mortem to understand the cause and improve future monitoring.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.