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Unit Economics and Scaling Questions

Covers measuring and modelling the economics of acquiring and servicing customers and how those economics change as a business grows. Candidates should be able to calculate Customer Lifetime Value for cohorts using retention, spend per period, and margin assumptions; compute payback period and contribution margin per customer; and compare Customer Lifetime Value across acquisition channels and customer segments. Understand the relationship between Customer Lifetime Value and Customer Acquisition Cost and how that ratio informs sustainable growth. Expand analysis to unit economics beyond customers to units of product or transaction level, identifying fixed and variable cost drivers, per unit gross margin, and break even points. Reason about scale effects including economies and diseconomies of scale, what operational components break or become bottlenecks at higher volume, and how unit costs change with automation, capacity constraints, supplier pricing, fraud and support load. Be prepared to build simple spreadsheet models and run sensitivity and scenario analyses, propose operational and pricing levers to improve unit economics, and design experiments and metrics to track improvements over time.

MediumTechnical
0 practiced
A product manager asks you to build a spreadsheet model to show how LTV changes with churn rate. Describe the formulas you would use in Excel/Sheets to model: (a) monthly retention r, (b) average monthly revenue ARPU, (c) gross margin %, and compute LTV as a closed-form formula and as a discrete sum for 24 months. Explain how you'd present sensitivity analysis for churn +/- 2%, +/- 5%.
HardTechnical
0 practiced
Advanced SQL optimization: describe 4 strategies (with examples) you would use to speed up cohort LTV calculations that join large transaction and user tables: consider partitioning, clustering, window functions, materialized views, and approximate algorithms.
HardSystem Design
0 practiced
Design a BI system to compute daily rolling 12-month LTV per acquisition channel for a company with 50M customers and 2B transactions. Describe data model (tables/partitions), incremental computation strategy, caching/aggregation layers, and how you'd keep dashboards performant in Looker/Tableau/Power BI.
HardTechnical
0 practiced
Explain survival analysis and how a hazard model (e.g., exponential or Weibull) can be used to model customer churn and predict long-term LTV. When is a parametric survival model preferable to using simple period-to-period retention rates?
MediumTechnical
0 practiced
Design an A/B experiment to test a retention improvement intervention that offers a 20% discount to users who are predicted to churn next month. Describe the randomization strategy, primary and secondary metrics, minimum viable sample size considerations, and how you would measure impact on LTV and payback period.

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