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.

EasyTechnical

0 practiced

Define leading and lagging indicators in the context of product analytics. Provide two examples of each for a subscription product, explain why they are leading or lagging, and describe a basic validation approach a data scientist would use to confirm a leading indicator predicts a lagging business outcome.

HardBehavioral

0 practiced

You must convince the executive team to deprecate a top-level KPI that has been driving incentives but no longer aligns with strategy. Draft your approach: what data and narratives you would present, counter-scenarios, replacement suggestions, and how you would manage political and incentive risks during the transition.

MediumTechnical

0 practiced

Design an attribution approach to measure the impact of promotional emails on purchases for an e-commerce business where users interact via web and app and often convert after multiple touches. Describe trade-offs between last-touch, first-touch, and multi-touch models, the data you would need, how you would handle delayed conversions, and how you would validate your chosen approach.

EasyTechnical

0 practiced

List and explain five automated data-quality checks you would implement to validate core product metrics such as DAU and conversion rate. For each check describe the purpose, typical alert thresholds, and suggested remediation steps if the check fails.

EasyTechnical

0 practiced

Given an events table with schema events(user_id text, event_name text, occurred_at timestamptz), write a PostgreSQL query to compute the funnel conversion from 'page_view' to 'sign_up' to 'purchase' for unique users over the last 30 days. Deduplicate multiple events per user per step and report counts and conversion rates between steps. Use SQL and explain assumptions about time windows and uniqueness.

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