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.

EasyTechnical

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What are DAU, MAU, and the DAU/MAU ratio? Explain what a rising DAU/MAU or a falling DAU/MAU indicates about product engagement, and list two limitations of using DAU/MAU as a standalone metric.

EasyTechnical

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Explain the difference between engagement, retention, monetization, and quality metrics. For a mobile e-commerce app, provide two concrete metric examples for each category, explain why each example fits the category, and mention one pitfall for each category (e.g., how it can be gamed or misinterpreted).

HardTechnical

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Compare and contrast using 7-day, 30-day, and 90-day retention as the primary retention KPI. Discuss implications for product decisions (e.g., short-term engagement vs long-term monetization), sensitivity to seasonality, cohort stability, and how different retention windows change experiment interpretation.

EasyTechnical

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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.

HardTechnical

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The events stream is high-volume and users access from multiple devices. Propose approaches to estimate unique active users for DAU reporting: (1) exact SQL-based deduplication using deterministic IDs, (2) probabilistic counting using HyperLogLog, and (3) deterministic user stitching across devices. For each approach explain implementation steps, accuracy trade-offs, and storage/performance implications.

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