Titlebook: Database Marketing; Analyzing and Managi Robert C. Blattberg,Byung-Do Kim,Scott A. Neslin Textbook 2008 Springer-Verlag New York 2008 CEO.D

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Customer Lifetime Value: Fundamentalso the firm. This chapter focuses on the fundamental methods for calculating lifetime value, centering on “simple retention models” and “migration models.” We present a general approach to calculating LTV using these models, and illustrate with specific examples. We also discuss the particular case o

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Customer Lifetime Value Applicationsions can LTV provide answers that traditional marketing analyses can not? This chapter will provide some answers to these questions. We will discuss how LTV models can be used in the real-world and describe some applications from the literature.

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Statistical Issues in Predictive Modelingment of missing data, and evaluation of models. Topics covered include stepwise selection and principal components methods of variable selection; imputation methods, missing variable dummies, and data fusion techniques for missing data; and validation techniques and metrics for evaluating predictive

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Market Basket Analysis or promoted together. The term arises from the shopping carts supermarket shoppers fill up during a shopping trip. The rise of the Internet has provided an entirely new venue for compiling and analyzing such data. This chapter discusses the key concepts of “confidence,” “support,” and “l(fā)ift” as app

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Collaborative Filtering for “recommendation engines.” We discuss the two major forms of collaborative filtering: memory-based and model-based. The classic memorybased method is “nearest neighbor,” where predictions of a target customer‘s preferences for a target product are based on customers who appear to have similar ta