Business Metrics Definition and Strategy Questions

Emphasizes defining meaningful metrics and measurement frameworks that answer business questions and drive decisions. Candidates should be able to distinguish between count metrics, ratio metrics, and rate metrics; select appropriate observation windows and time alignment for retention, churn, and conversion analyses; account for multiple user touch points and events when attributing actions; and identify leading versus lagging indicators. This topic covers designing metric definitions that avoid double counting, selecting denominators and numerators that match the business question, segmenting users for insight, and documenting business logic to ensure consistency. At senior levels expect discussion of trade offs between simplicity and fidelity, governance of metric definitions, and how to prioritize which metrics matter for different stakeholders.

HardTechnical

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Financial metrics (hard): For a subscription business, define how you would compute Customer Lifetime Value (LTV) and Customer Acquisition Cost (CAC) at a cohort level. Describe assumptions you must make (churn model, discount rate), how you would incorporate expansion/contraction MRR, and how to compute payback period. Provide formulas and explain data requirements.

MediumBehavioral

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Behavioral (medium): Describe a time when a change in metric definition caused conflict between product and engineering or analytics teams. What steps did you take to resolve the issue, how did you communicate the change, and what lasting processes did you put in place to prevent similar conflicts?

HardTechnical

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Modeling (hard): Design an attribution model that handles multi-touch online channels, offline conversions (e.g., phone sales), and cross-device behavior. Specify the data sources required, the modeling choices (rule-based vs algorithmic), key assumptions, and how you would validate and explain model outputs to marketing stakeholders.

MediumTechnical

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Rate metrics (medium): You need to compute 'sessions per active user in the last 30 days' as a rate metric used by product managers. Explain a correct aggregation approach that avoids double counting and accounts for users with zero sessions, describe SQL or aggregation logic at a high level, and discuss how sliding vs fixed windows change the interpretation.

MediumSystem Design

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System design (medium): Architect a metrics pipeline for daily aggregate metrics (DAU, new signups, conversions) that handles duplicate events, late-arriving events, and allows safe backfills. Describe the components, data stores, windowing strategy, and how you would ensure idempotent aggregations.

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