This is the kind of question where you think you know where to start and then realize five minutes in that you've already painted yourself into a corner.
Start by clarifying the decision context: the goal is to determine where incremental investment yields the highest long-term value for the Stories ecosystem. Define metrics at the ecosystem level (not per surface) to capture cannibalization, then propose a unified measurement framework with primary, diagnostic, and guardrail metrics that guide investment decisions.
Pro tip: Emphasize that cannibalization is not inherently bad—if users shift from one Stories surface to another without reducing overall engagement or monetization, it may be a net positive. Focus on incremental value, not surface-level metrics.
Confirm that the goal is to maximize overall Stories ecosystem value (engagement, monetization, user well-being) rather than optimizing a single surface. Identify the time horizon and key stakeholders.
Choose a small set of North Star metrics that reflect the ultimate value of Stories, such as daily active users engaging with Stories, total time spent, or revenue from Stories ads. These should be ecosystem-level to avoid surface bias.
Select metrics that explain changes in primary metrics, such as creation rate, view rate, completion rate, sharing, and cross-surface usage. These help diagnose why a primary metric moved and inform investment decisions.
Identify metrics that must not degrade, such as user well-being (e.g., problematic use), overall app engagement, or advertiser satisfaction. These ensure that investment doesn't harm the broader ecosystem.
Use holdout experiments, incrementality tests, and cross-surface attribution to measure the true incremental impact of investing in one surface over another. Analyze substitution patterns and net ecosystem effects.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by acknowledging the challenge of comparing cross-platform metrics due to overlapping user bases and differing user intents. Propose a framework that normalizes for baseline behavior and isolates the impact of Stories, using techniques like propensity score matching or synthetic control. Emphasize the importance of defining clear success metrics that align with each platform's user intent.
Pro tip: Leverage existing cross-platform experiments or holdout groups to measure incremental impact, and consider using a difference-in-differences approach to account for platform-specific trends. Always validate assumptions with sensitivity analyses.
Clarify what 'performance' means for Stories on each platform (e.g., engagement, retention, monetization) and select metrics that reflect user intent. Ensure metrics are comparable across platforms by defining them consistently.
Identify overlapping users and segment them to compare behavior within the same user across platforms. Use statistical methods like propensity score matching or stratification to control for baseline differences in user intent and behavior.
Employ experimental designs (e.g., A/B tests, holdout groups) or quasi-experimental methods (e.g., difference-in-differences) to measure the incremental effect of Stories on each platform, controlling for platform-specific trends.
Normalize metrics to account for scale differences (e.g., per-user averages) and compare performance using statistical tests. Consider creating a composite score that weights metrics based on strategic priorities.
Conduct sensitivity analyses to test robustness of findings and interpret results in the context of user intent and platform dynamics. Communicate caveats and potential biases.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with a geo-based experiment as my main answer since a standard A/B at the user level felt too leaky given cross-app spillover.
Start by advocating for a randomized controlled experiment (A/B test) as the gold standard for causal identification, then discuss how to design it to measure the incremental impact of Stories. Acknowledge that observational methods may be needed when randomization isn't feasible, and outline the key biases and confounders to address.
Pro tip: Emphasize that the unit of randomization should align with the decision-making unit (e.g., user-level) and that you'd measure long-term effects to capture habit formation, not just short-term engagement.
Clarify what 'true impact' means: incremental engagement, retention, revenue, etc., and specify the target estimand (e.g., average treatment effect on the treated).
Prefer a randomized experiment (A/B test) with user-level randomization. If not possible, consider quasi-experimental methods like difference-in-differences, instrumental variables, or regression discontinuity.
Ensure proper randomization, sufficient power, and guard against network effects, novelty effects, and selection bias. Use intent-to-treat analysis and consider holdout groups.
List potential confounders such as user activity level, time trends, and cross-platform spillovers. Use stratification, covariate adjustment, or CUPED to increase sensitivity.
Check for SRM, run sensitivity analyses, and measure long-term effects. Consider heterogeneous treatment effects and external validity.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This part felt more like product strategy than data science and I think that's intentional.
Start by clarifying the goal and defining success metrics for each scenario, then use a hypothesis-driven approach to diagnose the root causes before recommending actions. Prioritize decisions based on impact and alignment with long-term company objectives, and always consider trade-offs and potential second-order effects.
Pro tip: Show that you can balance short-term wins with long-term health by explicitly stating what you would measure and how you would iterate. Demonstrating a test-and-learn mindset with clear success criteria will set you apart.
Ask clarifying questions to understand the overarching goal (e.g., sustainable growth, revenue targets) and define what 'engagement', 'revenue', 'creator supply', and 'retention' mean in this context. Identify the primary metric to optimize and guardrail metrics to monitor.
For each scenario, hypothesize potential drivers. For engagement up but revenue down, consider if engagement is low-quality or if monetization is misaligned. For creator supply up but retention flat, check if new creators are low-quality or if user experience is suffering.
Brainstorm potential actions for each scenario, such as adjusting monetization strategies, improving creator onboarding, or enhancing user personalization. Evaluate each option based on expected impact, effort, and alignment with goals.
Select the most promising option(s) using a prioritization framework (e.g., ICE, RICE). Clearly state your recommendation, the rationale, and how you would measure success and iterate.
Acknowledge potential downsides of your recommendation and how you would mitigate them. Discuss how you would monitor for unintended consequences and adjust course if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.