RFM (customer value)

PyMC-Marketing

Bayesian marketing toolbox in PyMC. Media Mix (MMM), customer lifetime value (CLV), buy-till-you-die (BTYD) models and more.

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

Churn Prediction

“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

How to project customer retention

A seminal paper, with a stronger approach compared to the one above: the exponential distributions is replaced by a geometric model, which is better suited to discrete time intervals like monthly contracts, the former being more appropriate to continuous time process