Mining Customers Behavior Based on RFM Model to Improve the Customer Satisfaction

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Author(s): Fatemeh Bagheri (K. N. Toosi University of Technology, Tehran, Iran)and Mohammad J. Tarokh (K. N. Toosi University of Technology, Tehran, Iran)
Copyright: 2013
Pages: 13
Source title: Managing Customer Trust, Satisfaction, and Loyalty through Information Communication Technologies
Source Author(s)/Editor(s): Riyad Eid (United Arab Emirates University, UAE)
DOI: 10.4018/978-1-4666-3631-6.ch015

Keywords: Business & Management / Business Information Systems / Business Science Reference / Consumer Management

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Abstract

Organizations use data mining to improve their customer relationship management processes. Data mining is a new and well-known technique, which can be used to extract hidden knowledge and information about customers’ behaviors. In this paper, a model is proposed to enhance the premium calculation policies in an automobile insurance company. This method is based on customer clustering. K-means algorithm is used for clustering based on RFM models. Customers of the insurance company are categorized into some groups, which are ranked based on the RFM model. A number of rules are proposed to calculate the premiums and insurance charges based on the insurance manner of customers. These rules can improve the customers’ satisfaction and loyalty as well as the company profitability.

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