In business, especially marketing, it is often necessary to perform customer selection. The typical task involves defining a set of customers for upselling, cross-selling or retention actions. Traditionally, the selection criterion is the positive response probability. While this identifies customers who are the most likely to respond, it does not necessarily provide the optimal solution from which the business profits the most.
Over the last years, the concept of customer lifetime value (CLV) has attracted increasing attention. It suggests rating and selecting the customers by the present value of all future revenues that are attributed to their relationship. Hence, the focus lies on customer quality, rather than customer quantity. While this may sound logical, it provides a huge analytical challenge: the CLV is driven by the future behavior of a customer, which of course is not known in advance. Predictive analytics can and needs to be used for modeling customer dynamics, and often big data is involved.