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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.

EasyTechnical
24 practiced
A user can complete the same conversion across multiple touchpoints (email, web, mobile). Describe three practical approaches to avoid double-counting conversions in reports and the trade-offs of each approach.
HardTechnical
31 practiced
You're asked to compare two metrics: ARPU (average revenue per user) and ARPPU (average revenue per paying user). Explain which problems each solves, when ARPU may be preferable to ARPPU, and provide an example where ARPPU hides a declining conversion trend that ARPU reveals.
HardTechnical
45 practiced
Design an attribution algorithm that implements first-touch, last-touch, linear, and exponential time-decay and can be applied to sessionized event sequences in Python. Describe data structures, algorithmic complexity, and how you'd validate correctness.
MediumTechnical
26 practiced
You're asked to measure Customer Lifetime Value (LTV) for a freemium product that monetizes via in-app purchases and subscriptions. Describe an approach to define LTV, key assumptions, how to segment LTV (cohort vs user-level), and pitfalls when comparing LTV across countries.
HardTechnical
33 practiced
You design an A/B experiment where the primary metric is a ratio (purchasers / active users) with baseline conversion of 0.5%. Compute required sample size for 80% power to detect a 10% relative uplift. Explain formula choices and approximations, and how you would adjust for multiple metrics.

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