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.
MediumTechnical
0 practiced
You have limited engineering resources and five candidate growth experiments. Describe a prioritization framework you would use (for example RICE or ICE), how you would score ideas, and walk through scoring three hypothetical experiments with made-up numbers to show which to run first.
HardTechnical
0 practiced
Design a privacy-preserving analytics approach to measure conversion and run experiments while complying with GDPR and CCPA. Discuss trade-offs between deterministic identity, probabilistic modeling, differential privacy, and aggregation levels; specify what you would store, how to handle opt-outs, and how to estimate incremental impact under privacy constraints.
MediumSystem Design
0 practiced
Design a KPI dashboard focused on improving landing page conversion. Specify which charts and dimensions to include (e.g., conversion by traffic source, device, geo, cohort), data refresh frequency, alert rules, and at least two drill-down queries or analyses you would make available to investigate drops quickly.
EasyTechnical
0 practiced
List and explain common pitfalls when instrumenting conversion events and product metrics in a consumer-facing mobile or web product, including event deduplication, schema drift, timezone issues, sampling, and client-side retries. For each pitfall describe how you would detect it and at least one mitigation strategy.
HardTechnical
0 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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