Data accuracy's impact on segmentation performance: Benchmarking RFM analysis, logistic regression, and decision trees

Abstract

Companies greatly benefit from knowing how problems with data quality influence the performance of segmentation techniques and which techniques are more robust to these problems than others. This study investigates the influence of problems with data accuracy – an important dimension of data quality – on three prominent segmentation techniques for direct marketing: RFM (recency, frequency, and monetary value) analysis, logistic regression, and decision trees. For two real-life direct marketing data sets analyzed, the results demonstrate that (1) under optimal data accuracy, decision trees are preferred over RFM analysis and logistic regression; (2) the introduction of data accuracy problems deteriorates the performance of all three segmentation techniques; and (3) as data becomes less accurate, decision trees retain superior to logistic regression and RFM analysis. Overall, this study recommends the use of decision trees in the context of customer segmentation for direct marketing, even under the suspicion of data accuracy problems.