I tried to anchor on exploration rate as my leading indicator, basically sessions where a user touched at least one similar-product interaction divided by total sessions.
Frame the problem as a causal inference question: use Facebook's 'More like this' as a natural experiment to estimate its impact on engagement, then validate that Instagram's product and interaction data can support a similar effect. Propose a pre-launch measurement plan that uses proxy metrics and quasi-experimental methods to build a compelling case for the PM.
Pro tip: Emphasize that you're not just looking for correlation but trying to establish causality by leveraging Facebook's launch as a natural experiment, and always tie your metrics back to a clear product hypothesis about user intent.
Articulate the product hypothesis: 'More like this' increases discovery and engagement by reducing search friction. Define proxy success metrics (e.g., click-through rate on similar items, session depth, time to next interaction) that are measurable on Instagram without revenue.
Use Facebook's launch as a quasi-experiment: compare user engagement before and after the button's introduction, and use difference-in-differences or synthetic control to isolate the button's effect. Ensure comparability between Facebook and Instagram user behaviors.
Check if Instagram's interaction data (e.g., likes, saves, shares) and product schemas can support a similar feature. Conduct a power analysis to see if you can detect an effect of the expected size, and use propensity score matching to create comparable groups.
Estimate the lift in engagement metrics from Facebook's results and project it onto Instagram's user base, accounting for differences in user behavior and product context. Identify potential cannibalization or negative effects and propose mitigation.
Synthesize findings into a clear story: the evidence from Facebook, the applicability to Instagram, the expected impact, and the recommended next steps (e.g., a limited A/B test). Highlight the low cost of testing and the potential upside.
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Network effects on Instagram are real and I knew user-level randomization would be contaminated fast.
Start by clarifying the feature and the nature of potential network effects, then propose a randomization unit that balances bias reduction with practical constraints. Discuss trade-offs between user-level, cluster-level, and other randomization units, and suggest methods to detect and mitigate interference.
Pro tip: Mention that you would run a power analysis and consider variance reduction techniques like CUPED, and that you would pre-register the analysis plan to avoid p-hacking.
Ask questions to understand how the feature works and how users interact. Identify if network effects are likely (e.g., social features) and their direction (positive or negative).
Evaluate options: user-level, cluster-level (e.g., by social graph clusters), or time-based. Consider bias-variance trade-off and feasibility.
Define primary and guardrail metrics. Plan for sufficient power, and consider using variance reduction techniques. Decide on duration and sample size.
If network effects exist, use cluster randomization or other methods like ego-cluster randomization. Consider measuring spillover effects.
Use appropriate statistical methods (e.g., cluster-robust standard errors). Check for interference and adjust analysis if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Primary metric I went with was click-through rate on the button plus downstream saves and session depth.
Start by clarifying the experiment's goal and the specific engagement behavior you're targeting, then define a primary metric that directly measures that behavior and secondary metrics that capture broader engagement quality. Propose guardrails to ensure no harm to user experience or long-term value, and outline event logging needed to compute these metrics and diagnose results.
Pro tip: Tie your primary metric to a concrete user action that reflects meaningful engagement (e.g., daily active usage or content interactions), not just time spent, and explicitly state how you'd validate it with a counter-metric to avoid optimizing for vanity.
Restate the experiment's objective (engagement, not revenue) and identify the specific user behavior or segment you aim to influence. Ask clarifying questions if needed to align on what 'engagement' means for this product.
Choose a single primary metric that directly measures the targeted engagement behavior (e.g., daily active users, sessions per user, or meaningful actions per user). Justify why it's the best proxy for the goal and how it will be measured.
Pick 2-3 secondary metrics that capture other dimensions of engagement (e.g., depth, breadth, retention) to provide a fuller picture and support diagnosis if the primary metric moves.
Identify guardrail metrics to monitor for unintended negative consequences, such as user satisfaction, performance, or long-term retention. Specify thresholds for alerting if guardrails degrade.
List the events and properties needed to compute the metrics, including user actions, timestamps, and dimensions for segmentation. Ensure logging is consistent and can support both primary and guardrail analyses.
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The cluster randomization adjustment to sample size tripped me up a bit.
Start by framing the problem: cluster randomization introduces intra-cluster correlation, so you need to adjust sample size using the design effect (1 + (m-1)ρ). Then explain how to compute MDE by inverting the power formula with the adjusted variance, and finally discuss sequential testing methods like alpha-spending or always-valid inference to handle multiple looks.
Pro tip: Emphasize that in practice, you often don't know ρ a priori, so you'd estimate it from historical data or run a pilot, and you might use a variance reduction technique like CUPED to improve sensitivity. Also, mention that multiple looks require pre-registration of the analysis plan to avoid p-hacking.
Specify the outcome metric, cluster size (m), number of clusters (k), intra-cluster correlation (ρ), significance level (α), power (1-β), and minimum detectable effect (MDE) or sample size. Assume clusters are independent and outcomes within clusters are exchangeable.
Compute the design effect (DE = 1 + (m-1)ρ) and inflate the standard sample size formula by DE. For continuous outcomes, n_individual = 2*(z_{α/2}+z_β)^2 * σ^2 / Δ^2, then n_clusters = n_individual * DE / m.
Invert the power formula: MDE = (z_{α/2}+z_β) * sqrt( (σ^2 * DE) / (k*m) ) for a two-sample t-test. For proportions, use the appropriate variance formula with DE.
Use group sequential methods (e.g., O'Brien-Fleming alpha-spending) or always-valid confidence sequences to control type I error. Pre-specify the number and timing of interim analyses, and adjust α at each look.
Simulate the design to check power and false positive rates under realistic ρ and cluster size distributions. If ρ is uncertain, conduct sensitivity analysis or use a Bayesian approach to incorporate prior uncertainty.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Said I'd require both p-value below threshold and a lift above some pre-agreed minimum meaningful effect, not just one or the other.
Start by defining launch criteria that balance statistical significance (e.g., p-value < 0.05) with practical significance (e.g., minimum detectable effect or business impact threshold). Then, outline a staged rollout plan that addresses heterogeneous treatment effects, using techniques like segment-level analysis and sequential testing to decide whether to launch fully, partially, or iterate.
Pro tip: Emphasize that practical significance should be tied to business metrics (e.g., revenue, engagement) and that segment-level results require correction for multiple comparisons to avoid false positives. Also, mention the importance of pre-registering the analysis plan to maintain rigor.
Identify primary and secondary metrics, and set both statistical significance (alpha) and practical significance (minimum effect size) thresholds based on business impact.
Conduct overall hypothesis tests and segment-level analyses (e.g., by user demographics, geography) while adjusting for multiple comparisons.
Assess whether segment differences are meaningful and consistent, and consider potential risks of partial rollout (e.g., user experience inconsistency, technical complexity).
If results are promising in some segments, propose a phased rollout starting with those segments, with clear go/no-go criteria and monitoring for adverse effects.
Continuously monitor metrics post-launch, and be prepared to iterate or expand based on real-world performance and feedback.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.