Bank CRM Optimization Using Predictive Classification Based on the Support Vector Machine Method

This paper proposes a predictive approach to segmenting credit card users, based on their value to the bank. The approach combines the Recency, Frequency and Monetary (RFM) method, clustering using the k-means method, and predictive classification by the Support Vector Machine (SVM) method. Clustering by non-encoded RFM attributes overcomes the subjectivity in selecting the number of segments and losing information (small differences in the values of these attributes) which are problems of classic RFM segmentation. In order to overcome the problem of class imbalance in predictive classification (which occurs due to the small number of valuable customers), the Support Vector Machine (SVM) method was applied as a pre-processor of data due to its extraordinary generalization capabilities. The end result of predictive classification should be a set of rules that describes the identified customer segments in order to tailor the offer to each segment individually. The extraction of rules from the SVM output was achieved using the Decision Tree (DT) classification method. Using a proposed approach that addresses the issue of the small class, marketing managers can more effectively target the most valuable customers, thereby increasing revenue, but also reducing unnecessary costs due to wrongly targeted valuable clients.