Consumer Personality and Research Methods 2005 Conference
Dubrovnik, Croatia, September 20-24, 2005
http://www.cpr2005.info
Abstract
Data-mining in direct marketing: A comparison of RFM, CHAID, and logistic regression
John A. McCarty
The College of New Jersey, School of Business, NJ, USA
mccarty@tcnj.edu
Manoj Hastak
American University, Kogod College of Business, Wash., DC , USA
Keywords:
data-mining, statistical techniques, RFM
Type of contribution:
Talk / Paper presentation
Abstract:
The field of direct marketing has become more efficient in recent years because of the development of database marketing
techniques. These data-mining approaches have allowed the direct marketer to better segment their current customers and
develop marketing strategies tailored to particular segments and/or individuals. Over the recent years, database marketing
techniques have evolved from simple RFM (recency, frequency, and monetary value) models to statistical techniques such as
chi-square automatic interaction detection (CHAID) and logistic regression.
In spite of recent statistical advances in data-mining, marketers continue to employ RFM, primarily because of its ease of
implementation and the ability of managers to understand the results of the RFM analysis. Therefore, it has been argued that
the simplicity of RFM has been emphasized and its efficiency, relative to statistical techniques, has not been considered to the
extent that it should be.
Although the efficiency of RFM has been questioned, little research has documented its ability relative to newer statistical
techniques. The current study evaluates RFM, comparing it to CHAID and logistic regression, in an effort to understand its
capabilities as a database marketing analytical tool. The analysis involves two customer data sets, both with approximately
100,000 customer records.
We test one RFM procedure, which involves dividing the customers into cells (or nodes) as a function of their recency of
purchase, frequency of purchase, and monetary value (amount of money they have spent). These variables are evaluated in
terms of their ability to predict customer response. The study compares the lift in customer response using RFM to the lift
provided by CHAID and logistic regression.
Using a catalog marketer’s database and a nonprofit marketer’s database, the study shows that RFM performed well, compared
to the statistical techniques. The results are considered in light of the distribution free nature of RFM, while statistical
techniques assume linearity of recency, frequency, and monetary value to response.