Segmentation and Lifetime Value Models Using SAS

6Data-Mining Approaches toLifetime Value

Contents

6.1 The data-mining approach to predicting future behaviors

6.2 Regression models for highly skewed data

6.3 Evaluating data-mining models

6.4 Accounting for the long-term effects of a marketing contact

6.5 Chapter summary

     In Chapters 3–5 we discussed probabilistic models for CLV. Each chapter began with a set of assumptions. For example, customers join and generate some fixed cash flow until they cancel and never return; the chance that a customer cancels is constant over time and customers; the event that a customer is retained in one period is independent of the event in other time periods. Based on such assumptions, we could derive an expected CLV. The point is that these models begin ...