Customer Lifetime Value Modeling

We start by setting the stage and defining CLV and its key parameters. We then elaborate on RFM analysis, a key building block to many CLV models. We extensively zoom in on churn prediction: how to define it, predict it and evaluate the resulting analytical models. Next, we cover response modeling for both customer acquisition and deepening customer relationships. Markov chains are discussed as a very handy and intuitive approach for CLV modeling. We then review some probability models for CLV such as the Pareto/NBD model. Survival analysis is covered in depth since estimating accurate survival probabilities is key to calculating well-calibrated CLV values. We zoom in on profit-driven machine learning for CLV Modeling and discuss ProfLogit and ProfTree, both developed in our research group. We then briefly review some of our research on CLV modeling and churn prediction. The course concludes with some closing thoughts on the topic.

The course provides a sound mix of both theoretical and technical insights , as well as practical implementation details. These are illustrated by several real-life case studies and examples. Throughout the course, the instructors also extensively report upon their research and industry experience .

The course also features code examples in both R and Python and R/Python tutorials are also provided.

The course features more than 4 hours of video lectures, more than 100 multiple choice questions, and various references to background literature. A certificate signed by the instructors is provided upon successful completion.