How to estimate the value of your customers the right way

Implementing a probabilistic model for customer lifetime value

When it comes to customer lifetime value (CLV), most people are doing it wrong, according to Wharton marketing professor Peter Fader. At face value, CLV is an easy concept to understand —it’s a measurement of how much a business’s customers are worth over their lifetime. In practice, it’s deceptively hard to implement in a way that accurately captures the variation in customer behavior. CLV is so valuable to every business that it’s worth putting in the time and study to estimate it properly.

To help you estimate CLV the right way, we’ll walk through the formal definition, examine the pitfalls that plague the most popular approaches, learn some theory behind the buy-til-you-die model for CLV, and see how to implement a model with the lifetimes package in Python.

Defining customer lifetime value

Customer lifetime value is a prediction of how much value (in most cases, monetary value) a customer will bring to our company over their lifetime. The formal definition of customer lifetime value is as follows:

The present value of the expected sum of discounted cash flows of an individual customer.