Product Metrics and Strategy Questions

Emphasizes connecting metric design to product strategy and business outcomes. Covers metric taxonomy such as north star metric, outcome metrics, driver metrics, and leading versus lagging indicators, governance and ownership of metrics, and preventing metric gaming. Includes thinking about long term versus short term trade offs, how to influence product direction through metric design, attribution challenges, prioritizing instrumentation and data science investment, and communicating metric driven insights to stakeholders. Appropriate for senior level discussions where metrics inform strategy, roadmap decisions, and organizational alignment.

MediumTechnical

23 practiced

You observe a 20% drop in DAU in the last 24 hours. Walk through a prioritized, time-boxed investigation plan: which SQL queries and segments you would run first (platform, version, geography, cohort), how you'd check for instrumentation or deployment issues, how you'd include experiment assignments, and how you'd communicate interim findings to stakeholders.

MediumTechnical

25 practiced

Long-term outcomes like retention and LTV take months to observe. Describe how you'd select and validate leading indicators or proxies to run experiments with reliable signals earlier. Include methods to verify proxy validity (correlation, predictive models, backtesting) and guardrails to avoid false positives.

MediumTechnical

43 practiced

Write pseudocode or SQL (Postgres) to compute a 95% confidence interval for a binary conversion rate in an A/B test cohort (conversions / visitors). Explain the statistical assumptions behind the method and recommend alternatives (e.g., bootstrap) when those assumptions do not hold.

HardTechnical

24 practiced

Design a composite 'product health' metric that combines new user growth, weekly active users, 30-day retention, and average revenue per user (ARPU). Explain how you would normalize different units, choose weights, handle missing components, and validate that the composite metric does not mask important component-level signals.

HardTechnical

27 practiced

Your company uses DAU as its north star. Teams are optimizing for short sessions and low-quality interactions that increase DAU but reduce retention and revenue. Propose a new metric structure (revised north star and component metrics) and a multi-quarter transition plan to shift teams to new incentives, dashboards, and performance reviews while minimizing disruption.

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