Growth and Product Metrics Analysis Questions

Analysis skills specific to growth and product contexts: interpreting funnel metrics, cohort and retention analyses, attribution of acquisition versus activation, detecting seasonality and external event impacts, and diagnosing conversion or engagement changes. Candidates should be able to form hypotheses about what drove changes, propose targeted follow up analyses or A B tests, and identify which additional metrics are needed to evaluate unit economics and growth efficiency.

HardTechnical

40 practiced

You ran an A/B test and observed a 0.5 percentage-point absolute lift on conversion with p=0.04, but the metric has high variance and the business impact is small. Describe how you would evaluate practical significance versus statistical significance, discuss sequential testing, false discovery concerns, and what additional analyses you would run before recommending rollout.

MediumTechnical

37 practiced

List the key metrics and calculations you need to evaluate unit economics and growth efficiency for a subscription product. Given CAC = $50, gross margin = 70%, monthly ARPU = $10, and a monthly retention rate of 90%, estimate LTV (simple discounted or non-discounted assumptions) and payback period. State assumptions clearly.

HardTechnical

39 practiced

You have 36 months of monthly active users and want to forecast the next 12 months accounting for seasonality, promotions, and shifting growth rates. Outline candidate models (e.g., ARIMA, Prophet, state-space, causal impact adjustments), the features you would engineer, how you'd validate the model, and how you would present forecast uncertainty to stakeholders.

HardSystem Design

52 practiced

Design an experimentation platform and metrics pipeline that supports: randomized assignment, feature flags, metric computation (rolling aggregates and cohorts), and offline re-computation for 100M monthly users. Discuss components for event collection, storage, metric definitions, experiment assignment, monitoring, and how to ensure data quality and low-latency dashboards.

HardTechnical

49 practiced

Design a practical multi-touch attribution approach for an omnichannel business with web, mobile, email, and offline sales. Describe required data sources, features for the model, a choice between rule-based and data-driven approaches, how you would validate the model, and how you would operationalize the outputs for marketing budget decisions.

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