Neural networks in business: techniques and applications for the operations researcher
Computers and Operations Research - Neural networks in business
Knowledge management and data mining for marketing
Decision Support Systems - Knowledge management support of decision making
Admediation: New Horizons in Effective Email Advertising
Communications of the ACM
Mining association rules on significant rare data using relative support
Journal of Systems and Software
Decision Support Systems and Intelligent Systems (7th Edition)
Decision Support Systems and Intelligent Systems (7th Edition)
An intelligent system for customer targeting: a data mining approach
Decision Support Systems
Strategic Database Marketing
Learning multicriteria fuzzy classification method PROAFTN from data
Computers and Operations Research
A decision support system for direct mailing decisions
Decision Support Systems
Expert Systems with Applications: An International Journal
Classifying the segmentation of customer value via RFM model and RS theory
Expert Systems with Applications: An International Journal
A method for improving the accuracy of data mining classification algorithms
Computers and Operations Research
Advanced Data Mining Techniques
Advanced Data Mining Techniques
Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions
Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions
A comparative analysis of machine learning techniques for student retention management
Decision Support Systems
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Category role aided market segmentation approach to convenience store chain category management
Decision Support Systems
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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.