Document Type : Original Research Paper

Authors

• M. Ameli Basiri ¹
• M. Gharekhani ²

¹ Department of Computer Engineering and Information Technology, Faculty of Engineering, University of Qom, Qom, Iran

² Department of Industrial Engineering, Faculty of Engineering, University of Qom, Qom, Iran

Abstract

Due to the recent competitive environment in the Iranian insurance industry, to maximize the customer profitability, the insurance companies not only should try to acquire new customers, but also retain their existing customers and add their respective values. One of the most widely used methods to increase customer value for the insurance companies is selling more products to the existing customers especially superior ones which is called also as Cross- Selling. In the current study, the RFM model has been utilized to analyze the customer value in one of the major insurance companies. The customers of the above mentioned company are divided into three categories based on the three variables of recency, frequency and monetary values. Calculating these variables, the customers were clustered using the k-means and Fuzzy C-Means Algorithms. The results of this quality clustering is evaluated based on the silhouet criteria. The weight of each variable can be different in various industry; therefore, the weight of each variable is tuned utilizing AHP method. The clusters are then ranked in terms of value and the most profitable customers were identified. Also, in the second phase, Association Rules Mining Technique has been utilize to map the customers'consumption patterns in each cluster.

Keywords

• Analytical hierarchy process
• Association rules
• Cross-selling
• Customer life time value
• Customer segmentation
• RFM model