TY - JOUR ID - 11734 TI - Customer behavior mining based on RFM model to improve the customer relationship management JO - Journal of Industrial Engineering and Management Studies JA - JIEMS LA - en SN - 2476-308X AU - Bagheri, F. AU - Tarokh, M.J. AD - K. N. Toosi of Technology, Tehran, Iran. Y1 - 2014 PY - 2014 VL - 1 IS - 1 SP - 43 EP - 57 KW - Customer Relationship Management KW - K-means clustering algorithm KW - RFM model KW - Customer Lifetime Value KW - Analytical hierarchy process (AHP) DO - N2 - 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. UR - https://jiems.icms.ac.ir/article_11734.html L1 - https://jiems.icms.ac.ir/article_11734_d10d18d31b90d1e80ccd839fe3f8467f.pdf ER -