Product Metrics and Key Performance Indicators Questions

Covers designing, implementing, and governing metric frameworks for products. Topics include defining a north star metric that aligns the organization, identifying supporting and diagnostic metrics that drive and explain the north star, and understanding metric types such as engagement, retention, monetization, and quality. Candidates should be able to discuss metric hierarchies, instrumentation and data pipeline considerations, segmentation and cohort analysis, and the use of metrics for experimentation and decision making. Governance topics include ownership, alerting and anomaly detection, preventing metric manipulation, establishing thresholds and statistical rigor, retiring obsolete metrics, and balancing business and product analytics needs across stakeholders.

MediumTechnical

65 practiced

Write a PostgreSQL query that returns, for each calendar day in the past 90 days, the DAU (distinct users active that day), MAU (distinct users active in the 30-day window ending that day), and the DAU/MAU ratio. Use the table 'events(user_id, occurred_at)'. Explain any performance considerations and how you'd optimize this query for production reporting.

EasyTechnical

75 practiced

Describe a metric hierarchy for a consumer social app. Start from a proposed north-star metric and list at least three supporting metrics and two guardrail metrics. For each supporting metric, explain the causal relationship (how it feeds into or predicts the north star) and how you would instrument one of them.

MediumSystem Design

80 practiced

Design an alerting and anomaly detection strategy for a key conversion metric (e.g., checkout conversion). Describe threshold-based and statistical approaches you would use, how to handle seasonality and holidays, alert routing (who gets notified), and what a runbook should contain for triage.

MediumTechnical

71 practiced

User retention for your core cohort dropped 10 percentage points week-over-week after a new release. Outline a step-by-step root cause analysis plan: which queries and segmentations you'd run first, hypotheses to test, how to use funnels/cohorts to isolate the issue, and when to roll back or hotfix.

MediumTechnical

89 practiced

Explain p-values, Type I and Type II errors, statistical significance, and statistical power in the context of product A/B tests. Provide a practical example interpreting a p-value of 0.03 for a checkout conversion test and outline what additional checks you would perform before declaring a winner.

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