I went straight for CTR as primary and ad spend as guardrail, which felt right but I didn't do a great job explaining the hierarchy.
Start by clarifying the business goal and the user experience you're optimizing for, then define primary metrics that directly measure success against that goal and guardrail metrics that ensure you're not causing harm. Explain how you'd validate these metrics through A/B testing and monitor them post-launch.
Pro tip: Emphasize that guardrail metrics should be chosen based on potential negative side effects of the new system, such as increased latency or reduced diversity, and that you'd set thresholds for alerting. Also, mention the importance of long-term metrics like user retention to avoid optimizing for short-term gains.
Identify the core goal of the ad recommendation system, such as increasing ad revenue or improving user engagement, and how it aligns with company objectives.
Select 1-2 primary metrics that directly measure the success of the new system, such as CTR, conversion rate, or revenue per user, ensuring they are sensitive to changes.
Choose guardrail metrics that capture potential negative impacts, such as user satisfaction, latency, diversity of ads, or long-term retention, and set acceptable thresholds.
Plan an A/B test with proper randomization and sample size, and establish ongoing monitoring for both primary and guardrail metrics post-launch.
Analyze results, check for statistical significance, and ensure guardrails are not violated; be prepared to iterate on the model or metrics if needed.
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Talked about user-level randomization and holdout groups.
Start by defining the randomization unit and ensuring proper random assignment to create comparable groups. Then discuss techniques to validate comparability, such as pre-experiment covariate balance checks and stratification. Finally, address potential threats to comparability like network effects or interference and how to mitigate them.
Pro tip: At Meta, where social networks and interference are common, consider using cluster-based randomization or ego-network randomization to ensure control and treatment groups are comparable and avoid contamination. Also, always run an A/A test to validate your randomization and metrics before the actual A/B test.
Choose the appropriate randomization unit (e.g., user, session, cluster) based on the system and potential interference. Ensure random assignment using a deterministic hash or random number generator to avoid bias.
Verify that control and treatment groups are comparable on key pre-experiment covariates (e.g., demographics, past behavior) using statistical tests like t-tests or chi-squared tests. If imbalances exist, consider stratification or re-randomization.
If the system involves social interactions, use cluster-based randomization (e.g., by friendship clusters) or ego-network randomization to minimize spillover effects between groups.
Continuously monitor for sample ratio mismatch (SRM) and other anomalies that could indicate assignment issues. Use guardrail metrics to detect unintended effects.
Before the actual A/B test, run an A/A test to ensure the randomization and metrics are working as expected and that there is no inherent bias.
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Start by clarifying the experiment's goal and key metrics, then present the standard formulas for sample size and duration, explaining each component. Emphasize that these are estimates and discuss practical considerations like traffic allocation and novelty effects.
Pro tip: Mention that at Meta, sample size is often calculated using the formula for detecting a relative change, and duration should account for weekly seasonality and ramp-up periods. Also, highlight the importance of using historical data to estimate variance and baseline conversion rates.
Identify the primary metric, baseline conversion rate, minimum detectable effect (MDE), significance level (α), and power (1-β). These inputs are essential for the formulas.
For a two-sample test comparing proportions, use n = (Z_{α/2} + Z_β)^2 * (p1(1-p1) + p2(1-p2)) / (p2 - p1)^2, where p1 is baseline and p2 = p1 * (1 + MDE). For continuous metrics, use n = 2 * (Z_{α/2} + Z_β)^2 * σ^2 / Δ^2.
Duration = required sample size per variant / (daily traffic per variant * allocation ratio). Ensure to account for the proportion of users eligible for the experiment.
Mention that duration should be rounded up to full weeks to capture weekly seasonality, and consider ramp-up time, novelty effects, and potential dilution from other experiments.
Note that in practice, tools like Python's statsmodels or online calculators are used to compute these values, and sensitivity analysis can be performed by varying assumptions.
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This was the most interesting question in the set.
Start by clarifying that CTR is a proxy for ad relevance, not a direct driver of advertiser spend. Then map the causal chain from CTR to advertiser ROI and budget decisions, highlighting assumptions and feedback loops. Finally, propose additional analyses to validate each link and quantify the net effect.
Pro tip: Emphasize that advertiser spend is ultimately driven by ROI and budget constraints, so a CTR lift only matters if it translates to better ROI or shifts budget from other channels. Mention that you'd check for novelty effects and long-term holdout to avoid overestimating the impact.
Define what 'treatment CTR' means (e.g., ad CTR in an experiment) and the population (advertisers, campaigns). Confirm whether the 5% lift is relative or absolute and if it's observed in a controlled experiment.
Trace how a CTR increase could lead to higher advertiser spend: higher CTR → better ad relevance/quality → lower cost per action (CPA) → higher ROI → increased budgets. Also consider potential negative paths, such as higher CTR leading to higher CPC and offsetting ROI gains.
List key assumptions: advertisers are ROI-driven, budgets are elastic, and the CTR lift is sustainable. Moderators include advertiser size, industry, campaign objectives (awareness vs. conversion), and auction dynamics.
Suggest analyses to validate each link: measure downstream metrics (CPA, ROAS), run a long-term holdout to check persistence, segment by advertiser type, and analyze budget allocation changes via a difference-in-differences or causal impact model.
Conclude that a CTR lift does not necessarily mean higher spend; it depends on the full causal chain. Recommend a holistic evaluation using multiple metrics and experiments to inform decision-making.
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Pre-experiment divergence means randomization probably failed, or there's a logging issue, or the chart is showing something else entirely like a novelty effect from a soft launch.
First, identify that pre-experiment differences indicate a randomization or assignment issue, not a treatment effect. Then, propose diagnostic steps to verify the root cause and suggest visualization fixes to properly show the imbalance and its implications.
Pro tip: Always check for pre-experiment balance using A/A tests or covariate balance checks; if imbalance exists, consider using CUPED or stratification to adjust, and visualize with confidence intervals to highlight uncertainty.
Acknowledge that a pre-existing difference in CTR between groups before the experiment launches signals a fundamental flaw in randomization or data collection.
Investigate common causes such as biased assignment (e.g., non-random split), sample ratio mismatch (SRM), data pipeline issues, or external factors affecting one group.
Suggest re-randomization, using a proper randomization unit, checking for SRM, or applying statistical adjustments like CUPED if re-running is not possible.
Recommend plotting pre-experiment CTR with confidence intervals, clearly labeling the pre-period, and possibly showing the difference with error bars to highlight uncertainty.
Explain that any post-experiment difference may be confounded by the pre-existing imbalance, so results should be interpreted with caution or the experiment should be invalidated.
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Kept it short: lead with the business outcome, one or two supporting metrics, flag the main risk (auction interference), and give a clear recommendation.
Structure your answer around a clear, executive-friendly narrative: start with the headline result and its business impact, then outline next steps with owners and timelines, and finally surface key risks with mitigation plans. Emphasize how you would tailor the communication to leadership's priorities—strategic alignment, resource needs, and decision-making—rather than diving into technical details.
Pro tip: Lead with the 'so what'—the single most important takeaway and its implication for the business—and keep the rest of your answer at a strategic level, offering to go deeper only if asked. This shows you understand that executives care about outcomes, not methodology.
Open with the single most important result and its business impact, framed in terms of metrics leadership cares about (e.g., revenue, engagement, user growth). Avoid technical jargon.
Briefly highlight 2-3 supporting findings that validate the headline, using clear, non-technical language and visual aids if possible. Focus on what the data means, not how it was analyzed.
Propose concrete next steps with clear owners, timelines, and expected outcomes. Tie each step to a business goal or decision point to show strategic alignment.
Acknowledge potential risks (e.g., data limitations, implementation challenges, external factors) and pair each with a mitigation plan or ask for leadership support. Be transparent but solution-oriented.
End with a clear request for a decision, resource, or feedback to keep momentum. This ensures the meeting drives action rather than just informing.
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