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.

HardSystem Design

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Design a production pipeline to compute cohort-level LTV (lifetime value) and CAC (customer acquisition cost), and produce an LTV/CAC ratio across cohorts. Describe data sources, required joins, handling refunds/churn, discounting future revenue, and how you'd surface uncertainty around estimates.

HardTechnical

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Design a real-time anomaly detection system for a multi-step conversion funnel that must detect statistically significant drops within minutes. Compare lightweight statistical methods (e.g., sequential hypothesis tests, CUSUM) with ML methods (autoencoders, LSTM), and describe trade-offs in latency, false positives, interpretability, and maintenance.

HardTechnical

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Design a pipeline and model architecture for multi-touch attribution that outputs incremental lift per channel and per campaign. Describe data requirements, features, handling of time-varying exposures, model choice (e.g., multi-touch heuristic vs uplift models), and how you'd validate incremental estimates.

EasyTechnical

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Explain the difference between retention and churn. For a subscription product, define how you'd compute churn (monthly vs annual), and discuss edge cases (e.g., voluntary pauses, grace periods). How do retention curves and churn rate complement each other?

EasyTechnical

0 practiced

Explain the difference between leading and lagging metrics. Provide two examples of each for an e-commerce product, explain why leading metrics are useful for operational decisions, and describe a situation where a leading metric gave a false sense of security.

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