Comparison of customer response models

Segmentation of customers by likelihood of repeating business is a very important tool in marketing management. A number of approaches have been developed to support this activity. This article reviews basic recency, frequency, and monetary (RFM) methods on a set of data involving the sale of beef products. Variants of RFM are demonstrated. Classical data mining techniques of logistic regression, decision trees, and neural networks are also demonstrated. Results indicate a spectrum of tradeoffs. RFM methods are simpler, but less accurate. Considerations of balancing cell sizes as well as compressing data are examined. Both balancing expected cell densities as well as compressing RFM variables into a value function were found to provide more accurate models. Data mining algorithms were all found to provide a noticeable increase in predictive accuracy. Relative tradeoffs among these data mining algorithms in the context of customer segmentation are discussed.

[1]  J. R. Bult,et al.  Optimal Selection for Direct Mail , 1995 .

[2]  Peter C. Verhoef,et al.  The commercial use of segmentation and predictive modeling techniques for database marketing in the Netherlands , 2003, Decis. Support Syst..

[3]  Tom Breur,et al.  Merits of interactive decision tree building: Part 1 , 2007 .

[4]  Donald F. Specht,et al.  Probabilistic neural networks , 1990, Neural Networks.

[5]  Duen-Ren Liu,et al.  Hybrid approaches to product recommendation based on customer lifetime value and purchase preferences , 2005, J. Syst. Softw..

[6]  David L. Olson,et al.  Introduction to Business Data Mining , 2005 .

[7]  Victor S. Y. Lo.  The true lift model: a novel data mining approach to response modeling in database marketing , 2002, SKDD.

[8]  Amoy X Yang,et al.  How to develop new approaches to RFM segmentation , 2004 .

[9]  Xin Jin,et al.  Classification of freeway traffic patterns for incident detection using constructive probabilistic neural networks , 2001, IEEE Trans. Neural Networks.

[10]  Arnd Huchzermeier,et al.  Special Issue: 2002 Franz Edelman Award for Achievement in Operations Research and the Management Sciences: Optimizing Rhenania's Mail-Order Business Through Dynamic Multilevel Modeling (DMLM) , 2003, Interfaces.

[11]  Rosskyn D'Souza,et al.  Implementation study: Using decision tree induction to discover profitable locations to sell pet insurance for a startup company , 2007 .

[12]  Sven F. Crone,et al.  The impact of preprocessing on data mining: An evaluation of classifier sensitivity in direct marketing , 2006, Eur. J. Oper. Res..

[13]  Bart Baesens,et al.  Bayesian neural network learning for repeat purchase modelling in direct marketing , 2002, Eur. J. Oper. Res..

[14]  S Y LoVictor.  The true lift model , 2002 .

[15]  Sung-Shun Weng,et al.  The Study and Verification of Mathematical Modeling for Customer Purchasing Behavior , 2006, J. Comput. Inf. Syst..

[16]  Keun Ho Ryu,et al.  Mining association rules on significant rare data using relative support , 2003, J. Syst. Softw..

[17]  Tetsuya Hoya,et al.  On the capability of accommodating new classes within probabilistic neural networks , 2003, IEEE Trans. Neural Networks.

[18]  Tom Breur,et al.  Merits of interactive decision tree building — Part 2: How to do it , 2007 .

[19]  Duen-Ren Liu,et al.  Integrating AHP and data mining for product recommendation based on customer lifetime value , 2005, Inf. Manag..

[20]  Hon-Kwong Lui,et al.  Machine Learning for Direct Marketing Response Models: Bayesian Networks with Evolutionary Programming , 2006, Manag. Sci..

[21]  Ya-Yueh Shih,et al.  A method for customer lifetime value ranking — Combining the analytic hierarchy process and clustering analysis , 2003 .

[22]  David A. Koonce,et al.  A data mining tool for learning from manufacturing systems , 1997 .

[23]  John R. Miglautsch.  Application of RFM principles: What to do with 1–1–1 customers? , 2002 .

[24]  John A. McCarty,et al.  SEGMENTATION APPROACHES IN DATA MINING: A COMPARISON OF RFM, CHAID, AND LOGISTIC REGRESSION , 2007 .