Direct marketing decision support through predictive customer response modeling

a r t i c l e i n f o Keywords: Customer response predictive model Knowledge-based marketing RFM Neural networks Decision tree models Logistic regression Decision support techniques and models for marketing decisions are critical to retail success. Among different marketing domains, customer segmentation or profiling is recognized as an important area in research and industry practice. Various data mining techniques can be useful for efficient customer segmentation and targeted marketing. One such technique is the RFM method. Recency, frequency, and monetary methods provide a simple means to categorize retail customers. We identify two sets of data involving catalog sales and donor contributions. Variants of RFM-based predictive models are constructed and compared to classical data mining techniques of logistic regression, decision trees, and neural networks. The spectrum of tradeoffs is analyzed. RFM methods are simpler, but less accurate. The effect of balancing cells, of the value function, and classical data mining algorithms (decision tree, logistic regression, neural networks) are also applied to the data. Both balancing expected cell densities and compressing RFM variables into a value function were found to provide models similar in accuracy to the basic RFM model, with slight improvement obtained by increasing the cutoff rate for classification. Classical data mining algorithms were found to yield better prediction , as expected, in terms of both prediction accuracy and cumulative gains. Relative tradeoffs among these data mining algorithms in the context of customer segmentation are presented. Finally we discuss practical implications based on the empirical results. The role of decision support techniques and models for marketing decisions has been important since the inception of decision support systems (DSSs) [25]. Diverse techniques and models (e.g., optimization, knowledge-based systems, simulation) have emerged over the last five decades. Many marketing domains, including pricing, new product development , and advertising, have benefited from these techniques and models [16]. Among these marketing domains, customer segmentation or profiling is recognized as an important area [18,19,26,43]. There are at least two reasons for this. First, the marketing paradigm is becoming customer-centric [41] and targeted marketing and service are suitable. Second, unsolicited marketing is costly and ineffective (e.g., low response rate) [15,30]. Along with these reasons, there are increasing efforts on collecting and analyzing customer data for better marketing decisions [9,26,30]. The advancement of online shopping technologies and database systems has accelerated this trend. Data mining has been a valuable tool in this regard. Various …