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Enhancing Customer Relationship Management Using Association Rule Mining and the Evolutionary Algorithm

Firms are forced to come up with innovative approaches to compete in today’s difficult global market circumstances. The churning of the customer is one of the issues that arise when a company’s customers stop buying from or interacting with them. In order to increase client awareness, customer relationship management (CRM) is a tool to help businesses better understand and serve their customers, estimating long-term relationships. Data mining is typically used for data analysis. In this paper, Associative Classification (AC) that contains Association Rule Mining (ARM) and classification as data mining techniques is used to uncover patterns in customer churn datasets using the KEEL Evolutionary Learning tool. These tools assist in anticipating, discovering, pruning, ranking, and evaluating rules. The AC methods were investigated using the default parameters and the feature selection and it compared by various measures such as ROC curve, accuracy, error rate, number of rules, and running time, which are employed in the evaluation.