I went with creator post frequency as the engagement north star and incremental ad revenue per DAU for monetization.
Start by clarifying the feature's goal and target users, then define success metrics aligned with Meta's objectives. Propose a North Star metric for engagement (e.g., weekly active collectors) and one for revenue (e.g., incremental revenue per collector), and identify counter metrics to monitor for unintended consequences. Structure your answer to show a balanced view of growth and health.
Pro tip: Tie your metrics to Meta's overarching goals like meaningful social interactions and monetization efficiency, and emphasize how you'd validate metrics with A/B tests and guardrail metrics to avoid gaming.
Restate the feature's purpose and align on objectives (e.g., increase engagement, drive revenue) and target users (e.g., creators, casual users).
Propose one engagement metric (e.g., weekly active collectors) and one revenue metric (e.g., incremental revenue per user from collections).
List at least three counter metrics to monitor negative side effects, such as decreased overall engagement, user complaints, or cannibalization.
Describe how you'd measure these metrics (e.g., A/B tests, holdouts) and set thresholds for success and failure.
Conclude by prioritizing metrics and suggesting a dashboard or review cadence for ongoing monitoring.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify the feature's nature and potential for network effects, then recommend user-level or geo-clustered A/B testing based on that assessment. Explain how you would mitigate contamination through stratification and randomization, and discuss trade-offs between the two approaches.
Pro tip: At Meta, where network effects are common, geo-clustered tests are often necessary but require careful handling of spillover and interference; always quantify the bias-variance trade-off and consider using switchback or cluster-level randomization when appropriate.
Ask about the feature's scope and whether it can create interference between users (e.g., social sharing, marketplace). Determine if network effects are present and their likely magnitude.
If network effects are minimal, use user-level randomization for higher power and simpler analysis. If significant, use geo-clustered randomization to minimize contamination, accepting reduced power and potential confounding.
For user-level tests, stratify by factors like geography, demographics, or activity to balance groups and reduce spillover. For geo tests, stratify by region characteristics (e.g., size, baseline metrics) and randomize within strata.
Use methods like cluster-robust standard errors, or model interference (e.g., exposure mapping). Consider techniques like switchback or ego-cluster randomization if applicable.
Discuss trade-offs: user-level gives more power but risks bias; geo-level reduces bias but lowers power. Validate assumptions with A/A tests, pre-experiment covariates, and sensitivity analysis.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Blanked for a second on whether 1% relative meant 0.3% absolute or 30.3% vs 30%.
Start by clarifying the metric and test design, then state the formula for sample size calculation for two proportions. Plug in the baseline rate, absolute lift (1% relative of 30% = 0.3 percentage points), alpha, and power, and compute the required sample size per arm. Finally, discuss assumptions and practical considerations like duration and novelty effects.
Pro tip: Always convert relative lift to absolute lift before calculating; a 1% relative lift on 30% is 0.3 percentage points, which significantly impacts sample size. Also, mention that you'd round up and consider adding a buffer for data loss or multiple testing corrections.
Confirm that D30 retention is a binary metric (retained or not) and that the test is a two-sample proportion test with equal allocation. Ensure understanding of baseline rate (30%) and target relative lift (1%).
Calculate the absolute lift: 1% relative lift on 30% baseline means the treatment rate is 30% * 1.01 = 30.3%, so absolute lift = 0.3 percentage points (0.003).
Use the standard formula for two proportions: n = (Z_{α/2} + Z_β)^2 * (p1(1-p1) + p2(1-p2)) / (p2 - p1)^2, where p1=0.30, p2=0.303, Z_{α/2}=1.96, Z_β=1.28.
Plug in values: p1(1-p1)=0.21, p2(1-p2)=0.211191, sum=0.421191. (1.96+1.28)^2 = 10.4976. Multiply: 10.4976 * 0.421191 ≈ 4.421. Divide by (0.003)^2 = 0.000009: 4.421 / 0.000009 ≈ 491,222 per arm. Round up to 491,223.
Mention assumptions: independence, no interference, sufficient sample, normal approximation. Discuss practical implications: large sample size, test duration, potential for novelty effects, and need for guardrail metrics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Pre-registration I covered: locking the primary metric, sample size, and analysis window before any data comes in.
Start by framing the answer around the experiment's goal and key metrics, then detail the pre-registration process, stopping rules, and guardrail thresholds. Emphasize how these elements work together to ensure valid, ethical, and actionable results.
Pro tip: Mention that guardrail metrics should be monitored continuously with automated alerts, and that stopping rules must account for multiple comparisons to avoid false positives. This shows you understand the practical challenges of running experiments at scale.
Clearly specify the primary success metric (OEC) and secondary metrics, along with guardrail metrics that ensure user experience and system health are not harmed.
Document the hypothesis, metrics, sample size, duration, and analysis plan in advance to prevent p-hacking and ensure accountability.
Determine when to stop the experiment early for success, futility, or harm, using methods like sequential testing or alpha spending to control error rates.
Define acceptable limits for guardrail metrics (e.g., latency, crash rate, unsubscribe rate) and specify actions if thresholds are breached.
Set up real-time dashboards and automated alerts to track metrics and trigger reviews if guardrails are violated or stopping criteria are met.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The logging spec part I could've done better.
Start by outlining a logging spec that captures user interactions with ads, including impressions, clicks, and revenue attribution. Then, calculate the incremental revenue by estimating the baseline clicks and applying the 2% increase, ensuring to account for the given metrics and time frame.
Pro tip: Always clarify whether the 2% increase is relative or absolute, and consider potential cannibalization or novelty effects in your estimate.
Specify the events to log (e.g., ad impression, ad click) and the attributes for each event (user ID, timestamp, ad ID, revenue). Ensure the spec supports revenue attribution by linking clicks to revenue.
Decide on an attribution model (e.g., last-click) and specify how revenue is assigned to events. Include details on how to handle multiple clicks or impressions per user.
Compute daily ad clicks per user: 4 impressions * 1% CTR = 0.04 clicks per DAU per day. Multiply by 100 million DAUs to get 4 million clicks per day.
Increase baseline clicks by 2%: 4 million * 0.02 = 80,000 additional clicks per day. Multiply by revenue per click ($0.50) to get $40,000 incremental revenue per day.
Multiply daily incremental revenue by 7 days: $40,000 * 7 = $280,000 incremental revenue per week.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Acknowledge that statistical significance is necessary but not sufficient for a launch decision. Structure your answer around key business, technical, and user experience considerations that could override positive experiment results. Emphasize a holistic evaluation that includes long-term impact, guardrail metrics, and strategic alignment.
Pro tip: Show maturity by mentioning that you would dig into the experiment's validity and heterogeneity before making a decision. Also, consider the opportunity cost and whether the feature aligns with the company's long-term vision.
Check for internal validity issues such as sample ratio mismatch, novelty effects, or metric misinterpretation. Ensure the experiment was run for a sufficient duration and covers a representative user base.
Examine whether the feature negatively impacts guardrail metrics like user retention, engagement, revenue, or system performance, even if the primary metric improved.
Consider whether the short-term gain is sustainable and aligns with long-term product strategy. Look for potential cannibalization, ecosystem effects, or brand risks.
Weigh the engineering, maintenance, and opportunity costs of launching the feature. Determine if resources could be better allocated elsewhere.
Analyze if the positive effect is driven by a small segment while harming others. Ensure the feature benefits the overall user base or key demographics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.