Build an analytics application that evaluates customer lifetime value estimates and exposes the assumptions that can make them misleading.
Allow users to import customer acquisition costs, transaction histories, subscription records, refunds, discounts, gross margins, retention data, and customer cohort information.
Calculate historical contribution by customer and cohort. Where sufficient data exists, estimate future customer value using transparent retention, margin, and revenue assumptions. Compare simple historical methods with more advanced models and show how the results differ.
Include sensitivity analysis for churn, repeat purchase rates, acquisition cost, expansion revenue, and gross margin. Detect incomplete observation periods, survivorship bias, unusually valuable outliers, and cases where averages conceal substantial segment differences.
Display confidence ranges or scenario bands when justified by the available data. Separate observed historical metrics from forecasts and avoid treating correlation as proof of causation.
Generate a decision report explaining whether current acquisition spending appears sustainable under the tested assumptions.
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