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

0 practiced

How would you establish metric governance in a 200-person product organization? Describe the roles and responsibilities (owners, stewards), processes for metric approval and change, a central metrics catalog design, SLAs for metric delivery, and how to handle exceptions and ad-hoc definitions.

EasyTechnical

0 practiced

Explain what a 'north star metric' is for a product. As a data scientist working with a consumer mobile app, describe the criteria you would use to choose a north star metric, give two candidate north star metrics for a social app (for example 'meaningful-interactions' and 'weekly-active-retention'), and explain how you would validate that the chosen metric correlates with long-term business value rather than short-term vanity.

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.

HardTechnical

0 practiced

A driver metric currently relies on a simple heuristic. Propose criteria and a step-by-step plan to replace it with a machine learning model, covering feature availability, label quality, offline validation, interpretability requirements for product, monitoring and drift detection, retraining cadence, and governance considerations.

HardSystem Design

0 practiced

Design a globally-consistent metrics system for a product used across 50 countries with regional data sovereignty rules. Requirements: daily global DAU and per-country funnels, near-real-time alerts, handling time zones, duplicate users across regions, and complying with regional data residency constraints. Outline architecture, data flows, and reconciliation strategy.

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