Retentionizer is a Python package that applies the shifted-beta-geometric distribution model from Fader & Hardie's research to project customer retention rates and calculate lifetime value (LTV). It helps businesses forecast cohort survival and make data-driven decisions about customer value. ## Key Features - **Retention Projection** — Fits a shifted-beta-geometric distribution to historical cohort survival data to project future retention rates. - **Cohort Analysis** — Displays projected retention rates and imputed beta distributions for each customer cohort. - **LTV Calculation** — Computes the lifetime value of a given customer within their cohort based on the fitted model. - **Visual Output** — Generates clear graphical representations of retention projections and distribution fits. ## Philosophy Retentionizer provides an accessible, open-source implementation of academic research on customer retention modeling, enabling practical application without proprietary tools.

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You're all calculating churn rates wrong

“On the surface, churn rate may seem like a natural proxy for changes in customer lifetimes. Let's dig into why that is not true.” Churn rate is not a meaningful metric to compute CLV: during the customer lifetime, the churn probability is not constant. Most of the time because of your free trial and vouchers. This article illustrate the influence of the distribution used to model the probability of a customer quitting

“How to use Python in a simplistic way to fuel your company's growth by applying the predictive approach to all your actions.” Relies on XGBoost binary classification

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