Customer behavior mining based on RFM model to improve the customer relationship management

Companies’ managers are very enthusiastic to extract the hidden and valuable knowledge from their organization data. Data mining is a new and well known technique, which can be implemented on customers data and discover the hidden knowledge and information from customers behaviors. Organizations use data mining to improve their customer relationship management processes. In this paper R, F, and M variables for each customer are defined and extracted. Customers are clustered by using K mean algorithm based on their calculated R, F and M values. The best number of clusters is calculated by Davies Bouldin index. The clusters are ranked based on their eligibility values. By analyzing the clustering results, we propose some offers to the company to calculate the premiums and insurance charges.