Abstract
Direct marketing has become more efficient in recent years because of the use of data-mining techniques that allow marketers to better segment their customer databases. RFM (recency, frequency, and monetary value) has been available for many years as an analytical technique. In recent years, more sophisticated methods have been developed; however, RFM continues to be used because of its simplicity. This study investigates RFM, CHAID, and logistic regression as analytical methods for direct marketing segmentation, using two different datasets. It is found that CHAID tends to be superior to RFM when the response rate to a mailing is low and the mailing would be to a relatively small portion of the database, however, RFM is an acceptable procedure in other circumstances. The present article addresses the broader issue that RFM may focus too much attention on transaction information and ignore individual difference information (e.g., values, motivations, lifestyles) that may help a firm to better market to their customers.
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