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Segmentation and Lifetime Value Models Using SAS

Book Description

In marketing, customer lifetime value modelling is the science of predicting a customer's value to a company throughout their relationship. This book describes techniques, using SAS software, for creating such models. The book can be used in industry as well as in university marketing courses.

Table of Contents

  1. About This Book
  2. About The Authors
  3. Acknowledgments
  4. Chapter 1 Strategic Foundations for Segmentation and Lifetime Value Models
  5. 1.1 Introduction
  6. 1.2 A process for increasing CLV
  7. 1.3 A taxonomy of CLV models
  8. Chapter 2 Segmentation Models
  9. 2.1 Introduction to segmentation models
  10. 2.2 <i xmlns="http://www.w3.org/1999/xhtml" xmlns:epub="http://www.idpf.org/2007/ops" xmlns:ns2="http://www.w3.org/2001/10/synthesis">K</i>-means clustering-means clustering
  11. 2.3 A process for building segmentations
  12. 2.4 The finite mixture model
  13. 2.5 Chapter summary
  14. Chapter 3 The Simple Retention Model
  15. 3.1 The customer annuity model
  16. 3.2 The simple retention model
  17. 3.3 Estimating retention rates
  18. 3.4 Per-period cash flows <i xmlns="http://www.w3.org/1999/xhtml" xmlns:epub="http://www.idpf.org/2007/ops" xmlns:ns2="http://www.w3.org/2001/10/synthesis">m</i>
  19. 3.5 Chapter summary
  20. Chapter 4 The General Retention Model
  21. 4.1 The general retention model
  22. 4.2 Introduction to survival analysis
  23. 4.3 Product-moment estimates of retention rates
  24. 4.4 The discrete-time survival model
  25. 4.5 Application: trigger events
  26. 4.6 The beta-geometric model
  27. 4.7 Chapter summary
  28. Chapter 5 The Migration Model
  29. 5.1 Migration models: spreadsheet approach
  30. 5.2 Migration model: matrix approach
  31. 5.3 Estimating transition probabilities
  32. 5.4 Chapter summary
  33. Chapter 6 Data-Mining Approaches to Lifetime Value
  34. 6.1 The data-mining approach to predicting future behaviors
  35. 6.2 Regression models for highly skewed data
  36. 6.3 Evaluating data-mining models
  37. 6.4 Accounting for the long-term effects of a marketing contact
  38. 6.5 Chapter summary
  39. References
  40. Index