Hybrid soft computing approach based on clustering, rule mining, and decision tree analysis for customer segmentation problem: Real case of customer-centric industries

Abstract This paper proposes a hybrid soft computing approach on the basis of clustering, rule extraction, and decision tree methodology to predict the segment of the new customers in customer-centric companies. In the first module, K-means algorithm is applied to cluster the past customers of company on the basis of their purchase behavior. In the second module, a hybrid feature selection method based on filtering and a multi-attribute decision making method is proposed. Finally, On the basis of customers’ characteristics and using decision tree analysis, IF–THEN rules are mined. The proposed approach is applied in two case studies in the field of insurance and telecommunication in order to predict potentially profitable leads and outline the most influential features available to customers in order to perform this prediction. The results validate the efficacy and applicability of proposed approach to handle real-life cases.

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