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.

EasySystem Design

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Design a one-page operational dashboard for a support organization to monitor day-to-day health. List key KPIs, charts, top filters, required drilldowns, and at least three guardrail metrics you would include to detect regressions quickly.

HardTechnical

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Randomized experiments are not always practical. Given observational logs after rolling a new recommendation algorithm to a subset of users, describe a causal inference strategy to estimate whether the algorithm increased retention. Discuss assumptions, matching or instrumental variable strategies, and sensitivity analysis you would run.

EasyTechnical

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What automated daily data quality checks would you implement for product metrics pipelines to detect issues early? List at least six checks, explain why each matters, and describe a simple alerting rule for each check.

EasyTechnical

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Describe how you would set up a funnel analysis for a new user onboarding flow consisting of four steps (visit landing, create account, complete profile, first action). Define the conversion rates you would report, how you'd handle multi-session flows, and how to interpret drop-offs by step.

EasyTechnical

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You are the Product Manager for a customer support product. Identify the five most important KPIs you would monitor weekly to measure overall support effectiveness. For each KPI, justify why it matters, state whether it is a leading or lagging indicator, and describe exactly how you would compute it from a tickets/events dataset (mention necessary fields). Also pick one KPI as the north-star metric and explain your choice.

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