Propose a composite metric that measures total ecosystem value by combining time spent and quality of interactions across both apps, while penalizing zero-sum cannibalization. Emphasize that the metric should be validated through experiments that test for net incremental value, not just shifts in time. Frame it as a north-star metric that aligns with Meta's long-term goals of user well-being and sustainable engagement.
Pro tip: Acknowledge that time spent is an imperfect proxy and suggest incorporating qualitative signals like user satisfaction or meaningful interactions to avoid optimizing for addictive behavior. This shows maturity and aligns with Meta's broader focus on well-being.
Clarify that the criterion should capture healthy, sustainable engagement that benefits users and the ecosystem, not just maximize time in one app.
List key components: total time spent across both apps, cross-app interactions (e.g., sharing, cross-posting), and user-perceived value (e.g., satisfaction, meaningful connections).
Propose a composite metric, such as a weighted sum of time spent and quality interactions, with weights determined by their contribution to long-term retention or user value. Include a penalty for cannibalization if it reduces overall ecosystem value.
Outline how to test the metric using A/B tests or holdout groups, measuring net incremental value and ensuring it doesn't incentivize harmful cannibalization.
Emphasize continuous monitoring and refinement based on user feedback, business goals, and ethical considerations.
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Geo-level randomization was the right call here and I got there, but my reasoning was shaky at first.
Start by clarifying the goal: measure the causal impact of the new Instagram feature on both Instagram and Facebook engagement, accounting for potential cannibalization. Then propose a randomized experiment with a carefully chosen randomization unit (e.g., user-level) and discuss methods to handle interference, such as cluster randomization or measuring spillover effects.
Pro tip: Acknowledge that cross-app interference can bias results, and propose a design that either isolates the effect or quantifies it, showing you understand the trade-offs between internal validity and scalability.
Clearly state the estimand: the effect of the feature on Instagram engagement and Facebook engagement (e.g., time spent, sessions). Identify primary and guardrail metrics to detect cannibalization.
Select the unit of randomization (e.g., user, device, or geographic cluster) based on interference risk. Discuss trade-offs: user-level minimizes interference but may not capture network effects; cluster randomization can handle interference but reduces power.
Propose methods to mitigate or measure interference: use cluster randomization (e.g., by social graph clusters), run a switchback or geo-based experiment, or model spillover effects. Consider measuring both direct and indirect effects.
Plan the analysis: use appropriate statistical methods (e.g., CUPED, regression adjustment) to increase sensitivity. Estimate the overall impact and decompose into direct and spillover effects if possible.
Run A/A tests or holdout groups to validate the design. If interference is detected, consider follow-up experiments or quasi-experimental methods to confirm findings.
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I blanked for a second on the ICC adjustment.
Start by clarifying the metric definition and assumptions (e.g., daily active users, variance of the metric). Then compute the required sample size per arm using the standard formula for a two-sample t-test, adjusting for the intraclass correlation (ICC) to account for geo-level clustering. Finally, translate the sample size into test duration by estimating daily traffic and considering any ramp-up or novelty effects.
Pro tip: Always state your assumptions explicitly and note that the ICC adjustment increases the required sample size by a factor of 1 + (m-1)*ICC, where m is the average cluster size. This shows you understand the impact of clustering and can communicate uncertainty.
Confirm that the metric is a mean (e.g., average time spent per user) and that the baseline is 20 minutes. Assume a standard deviation (if not given, use a reasonable estimate or state that it's needed).
Use the formula for a two-sample t-test: n = 2 * (Z_alpha/2 + Z_beta)^2 * sigma^2 / delta^2, where delta is the minimum detectable effect (1% of 20 minutes = 0.2 minutes).
Multiply the base sample size by the design effect: 1 + (m - 1) * ICC, where m is the average number of users per geo. This accounts for the correlation of users within geos.
Divide the total required sample size (per arm) by the expected daily traffic per arm to get the number of days. Consider adding a ramp-up period and buffer for data cleaning.
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Crashes, feed quality scores, creator DAU, and ads lifetime value were the ones I listed.
Start by defining guardrail metrics as a balanced set covering user experience, ecosystem health, and business sustainability, then explain how you'd set thresholds and monitoring to trigger early stops. Emphasize that the decision to stop early should be based on pre-registered rules and statistical rigor, not ad-hoc peeking.
Pro tip: Frame guardrails as a 'canary in the coal mine'—they protect against unintended harm even when the primary metric wins. Mention that you'd pre-register stopping rules and use sequential testing or alpha-spending to avoid inflating false positives.
Identify key areas: user experience (e.g., latency, crashes), ecosystem health (e.g., content reports, spam), and business metrics (e.g., revenue, retention).
Choose 1-2 metrics per category that are sensitive to changes and aligned with company values, such as DAU, session length, or user reports.
Establish acceptable bounds (e.g., no more than 1% degradation) and set up real-time dashboards with alerts for violations.
Pre-register criteria for early stopping: if any guardrail breaches its threshold with statistical significance, or if the primary metric shows significant harm.
If triggered, halt the experiment, investigate root cause, and communicate findings to stakeholders for next steps.
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This was the most interesting part of the whole conversation.
First, clarify what the ecosystem metric and Facebook time represent, then assess whether the 3% drop is statistically significant and practically meaningful. Consider trade-offs and potential cannibalization, and recommend next steps like deeper analysis or follow-up experiments.
Pro tip: Don't jump to conclusions; a 3% drop might be noise or a short-term shift. Show you understand the business context and long-term goals before recommending action.
Define the ecosystem metric and Facebook time, and confirm the experiment's primary objective. Understand if the drop is a guardrail metric or a secondary metric.
Check if the 3% drop is statistically significant and whether it exceeds the minimum detectable effect. Consider confidence intervals and sample size.
Weigh the improvement in the ecosystem metric against the decline in Facebook time. Consider revenue, user engagement, and strategic priorities.
Segment the data to see if the drop is concentrated in certain user groups or regions. Look for cannibalization or external factors.
Recommend actions such as extending the experiment, running a follow-up test, or shipping if the trade-off is acceptable. Communicate with stakeholders.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.