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

1 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.

MediumTechnical

1 practiced

You need to produce a cohort retention curve (day 0 to day 30) for users who installed an app in a given week. Given a table

installs(user_id, install_date)

and

events(user_id, event_date)

, write SQL or describe the steps to compute retention rates per day for that install cohort, and explain how you'd visualize and interpret the curve.

EasyTechnical

1 practiced

Explain what a metric hierarchy is and provide a concrete 3-level example (north star → supporting metrics → instrumentation events) for a photo-sharing app. For each level describe the relationship to the level above and how you would instrument to ensure traceability from raw events to the north star.

MediumTechnical

1 practiced

Explain how seasonality (weekly, monthly, holiday) affects metric baselines and experiment analysis. Provide strategies for accounting for seasonality in metric dashboards and A/B test setups, including blocking, rolling windows, and time-series decomposition.

HardTechnical

1 practiced

A release caused a 20% drop in DAU. Provide a structured case-study plan to investigate: list hypotheses, required metrics/segments, analytical steps, how to use logs/traces and experiment data, and criteria for rollback vs hotfix. Include stakeholder communication steps.

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