CLV Basics
Customer lifetime value comes in many shapes and sizes. From the popular RFM approach at the cohort-level to predictive lifetime value, this lesson explores the various flavors of CLV.
Customer lifetime value comes in many shapes and sizes. From the popular RFM approach at the cohort-level to predictive lifetime value, this lesson explores the various flavors of CLV.
There are several methods you can use to calculate customer lifetime value, from simple to complex. This lesson will explore how to predict CLV at the aggregate and individual level.
Let’s take a look back at the history of BTYD models, the staple in Bayesian CLV prediction. Lifetime value comes to life using probabilistic models to handle churn and transaction timing, rather than the relatively straightforward RFM models.
In this lesson, we’ll share a popular method for calculating customer lifetime value using the R programming language.
Most traditional BTYD models are Bayesian statistical. In this lesson, we’ll explore an alternative way of modeling short-term CLV using Long-Short Term Memory (LSTM) Recurrent Neural Networks (RNNs), which are a type of supervised machine learning that can handle time series well.