Many eCommerce companies calculate Customer Lifetime Value (CLV) as the sum of all historical purchases for each customer. However, CLV, by definition, is a forward-looking statement. By simply considering historical purchases, the underlying assumption is that the customer will not make any further purchases. There has been significant statistical research into creating reliable probabilistic models to calculate Residual Customer Value (RLV) i.e. the dollars your customers are yet to spend.
Here are some ways in which calculating residual lifetime value can be useful for your business -
The most popular of these statistical models is the Pareto/NBD model. Here is the paper that sets the foundation for it. It’s full of math, so here’s the short version -
The model takes as input a historical purchase log, and calculates basic RFM (recency, frequency, monetary) stats. It then spits out the following data points for each customer -
What you will need to predict CLV
Need help setting this up? Feel free to reach out here.
Note: This method is suitable for non-subscription businesses only. For subscription businesses, there are a host of other methods that can be used. Another post to follow shortly!