Metrics Selection and Diagnostic Interpretation Questions

Addresses how to choose appropriate metrics and how to interpret and diagnose metric changes. Includes selecting primary and secondary metrics for experiments and initiatives, balancing leading indicators against lagging indicators, avoiding metric gaming, and handling conflicting signals when different metrics move in different directions. Also covers anomaly detection and root cause diagnosis: given a metric change, enumerate potential causes, propose investigative steps, identify supporting diagnostic metrics or logs, design quick experiments or data queries to validate hypotheses, and recommend remedial actions. Communication of nuanced or inconclusive results to non technical stakeholders is also emphasized.

HardTechnical

0 practiced

You suspect a marketing campaign increased low-quality traffic causing conversions to rise but revenue per user to fall. Propose an analysis plan to measure the campaign's incremental value including SQL queries, metrics (LTV, ARPU, churn), and how to estimate ROI given limited tracking (no user-level attribution for some channels).

HardTechnical

0 practiced

Create a standard 'diagnostic runbook' template that analysts use when a key metric regresses. List required sections (e.g., incident summary, impact, hypothesis checklist, queries to run, decision criteria), and provide two example SQL queries that should be included in every runbook for conversion regressions.

MediumSystem Design

0 practiced

Design a growth dashboard for a cross-functional growth team that tracks Acquisition, Activation, Retention, and Monetization. For each pillar, list 4 metrics (16 total), the visualization type, update frequency, and one diagnostic metric to help explain changes.

HardTechnical

0 practiced

For a low-traffic new feature where an A/B test isn't feasible (insufficient power), propose alternative validation frameworks (e.g., holdout, time-series intervention, synthetic control, qualitative research). For each option, describe assumptions, pros/cons, and a quick plan to implement it.

MediumTechnical

0 practiced

Design an anomaly detection approach for daily conversion rate where per-day sample size is small (low traffic). Discuss choices for aggregation, statistical tests, bootstrapping, or Bayesian methods to detect meaningful changes while limiting false positives.

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