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.

EasyTechnical

0 practiced

Explain the difference between acquisition cohorts and behavioral cohorts. Provide concrete examples for when you'd use each, and describe a situation where behavioral cohorts provide clearer insight than acquisition cohorts.

HardTechnical

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You must compute 30-day retention for 100M users from event logs stored as Parquet on S3 with 10B rows. Describe an optimized approach using Spark or SQL-on-Hadoop: how you'd partition data, push down predicates, use approximate algorithms (HLL) if acceptable, incremental pipelines, and how you'd validate approximations against exact counts.

EasyTechnical

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You're onboarding event instrumentation for a new mobile feature. Define an event taxonomy and naming convention and list the minimum required properties for each event to support funnel analysis, attribution, and user-level metrics. Also describe a versioning strategy for events and a small set of automated tests to ensure instrumentation quality.

MediumTechnical

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Design an experiment to reduce average first-response time for the support team. Define a clear hypothesis, primary and secondary metrics, unit of randomization, sample size estimation approach, guardrail metrics, and possible confounders. Explain how you'd treat priority tickets in the experiment.

EasyTechnical

0 practiced

You are building a metrics dashboard for a customer support organization. List the eight most important KPIs you would include (for example: customer satisfaction, response time, resolution rate, FCR). For each KPI provide:1) a precise business definition (numerator/denominator and time window),2) primary instrumentation/events required to measure it,3) one known caveat or bias to watch for.Limit the list to KPIs that executives and managers would find actionable.

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