Metrics Analysis and Data Driven Problem Solving Questions

Skills for using quantitative metrics to diagnose and solve product or support problems. Candidates should be able to identify relevant key performance indicators such as customer satisfaction, response time, resolution rate, and first contact resolution, detect anomalies and trends, formulate and prioritize hypotheses about root causes, design experiments and controlled tests to validate hypotheses, perform cohort and time series analysis, evaluate statistical significance and practical impact, and implement and monitor data backed solutions. This also includes instrumentation and data collection best practices, dashboarding and visualization to surface insights, trade off analysis when balancing multiple metrics, and communicating findings and recommended changes to cross functional stakeholders.

MediumTechnical

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You must decide between two initiatives: (A) a retention improvement expected to increase 6-month LTV by 5% and (B) a monetization change expected to increase short-term ARPU by 3% but risk marginally reducing retention. Propose a quantitative framework to evaluate and compare these trade-offs for a freemium product, including required inputs and key sensitivity checks.

MediumTechnical

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Write a SQL query that returns, for the last 12 weeks, daily WAU and MAU per week and the DAU/MAU ratio per week. Assume events(user_id, occurred_at) and that WAU is unique users in a calendar week. Use Postgres or BigQuery syntax and explain how you handle overlaps between DAU and MAU calculations.

MediumTechnical

0 practiced

Explain Type I (false positive) and Type II (false negative) errors in the context of product experimentation. Given a growth experiment, how would you choose the significance level (alpha) and power (1 - beta), and what business considerations influence those choices?

EasyTechnical

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Define seasonality in time series for product metrics and describe two practical methods you would use to account for weekly and monthly seasonality when analyzing Year-over-Year changes in Weekly Active Users (WAU).

MediumTechnical

0 practiced

Describe how you would detect whether a decline in overall retention is due to churn (users leaving permanently) versus seasonal behavior. Explain how to combine cohort analysis and time series decomposition across at least five customer segments to quantify the drivers.

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