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

0 practiced

A checkout optimization increases conversion but reduces average order value (AOV). Outline an analytical plan to quantify the net revenue impact, including per-user vs per-order metrics, statistical tests or bootstrap methods to compute confidence intervals, segment-level analysis, and decision rules you would recommend to product stakeholders.

MediumTechnical

0 practiced

Explain how to compute required sample size for an A/B test comparing conversion rates when the baseline conversion is 2% and the minimal detectable effect (MDE) is a 10% relative lift (i.e., 0.2 percentage points absolute). Assume power = 80% and alpha = 5%. Show the formula and numeric steps (normal approximation acceptable) and state assumptions.

HardTechnical

0 practiced

After launching a self-serve support feature, ticket volume dropped but satisfaction also decreased. Propose an analysis plan to determine whether the self-serve caused lower satisfaction for some user segments. Include funnel analysis, cohort comparisons, propensity-score matching or other quasi-experimental approaches, and text analysis of feedback to support your conclusions.

HardTechnical

0 practiced

Given an event_log table (ticket_id, event_type where event_type in ('created','assigned','in_progress','wait_for_customer','resolved','reopened'), event_ts TIMESTAMP, actor_id), write SQL or pseudocode to compute time_in_progress per ticket (sum of durations between entering 'in_progress' and leaving 'in_progress') and then compute average time_to_resolution defined as time_in_progress until the first 'resolved', ignoring any time after 'reopened'. Discuss edge cases and how you handle missing transitions.

EasyTechnical

0 practiced

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.

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