Customer list segmentation using the combined response model

Abstract Response models such as RFM (Recency, Frequency, Monetary), Logistic Regression, and Neural Networks estimate a single response model in direct marketing for segmenting and targeting customers. However, if there is considerable customer heterogeneity in the database, the models can be potentially misleading. To reflect this heterogeneity, researchers have introduced ways to combine two or more methods. Suggesting the capability of the combined model using the low correlation coefficient between them, the previous research on the combined response model did not provide answers for two important questions: (1) What are the response models that have a low correlation coefficient between them when combined? (2) Does the low correlation coefficient ensure improved performance? In this paper, we propose RFM as a method that has a low correlation coefficient when combined with Logistic Regression or Neural Networks. Our case study also concludes that the low correlation coefficient does not always ensure improved performance.

[1]  Edward L. Nash.  Database Marketing: The Ultimate Marketing Tool , 1993 .

[2]  M. Ben-Akiva,et al.  Discrete choice analysis , 1989 .

[3]  Lichung Jen.  Measuring the value of customers: a hierarchical Bayes model of interpurchase time / , 1995 .

[4]  Wayne S. DeSarbo,et al.  CRISP: Customer response based iterative segmentation procedures for response modeling in direct marketing , 1994 .

[5]  Robert C. Blattberg.  Research opportunities in direct marketing , 1987 .

[6]  J. Shao,et al.  APPLICATION OF AN ARTIFICIAL NEURAL NETWORK TO IMPROVE SHORT-TERM ROAD ICE FORECASTS , 1998 .

[7]  Jae Kyeong Kim,et al.  Modeling a class of decision problems using artificial neural networks , 1997 .

[8]  Joanna R. Baker,et al.  Procedures to improve the house list segment tests , 1996 .

[9]  Sung Ho Ha,et al.  Application of data mining tools to hotel data mart on the Intranet for database marketing , 1998 .

[10]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[11]  Nissan Levin,et al.  Issues and problems in applying neural computing to target marketing , 1997 .

[12]  W. DeSarbo,et al.  An Empirical Pooling Approach for Estimating Marketing Mix Elasticities with PIMS Data , 1993 .

[13]  Bharat A. Jain,et al.  Performance Evaluation of Neural Network Decision Models , 1997, J. Manag. Inf. Syst..

[14]  Jay Magidson,et al.  Improved statistical techniques for response modeling , 1988 .

[15]  Paul D. Berger,et al.  The effect of sample size and proportion of buyers in the sample on the performance of list segmentation equations generated by regression analysis , 1992 .

[16]  Bernard Widrow,et al.  Neural networks: applications in industry, business and science , 1994, CACM.

[17]  Nissan Levin,et al.  Applying neural computing to target marketing , 1997 .