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 algo-
rithms 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.

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Researcher Information

	Olson, David L James and H.K. Stuart Chancellors Distinguished Chair Expertise: Information Systems Operations Management Management CBA 256 P.O. Box 880491 University of Nebraska-Lincoln Lincoln, NE 68588-0491, USA Phone: (402) 472-4521 Fax: (402) 472-5855 dolson3@unl.edu	Olson, David L	Management