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

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

Fatemeh Bagheri (K. N. Toosi University of Technology, Tehran, Iran) and Mohammad J. Tarokh (K. N. Toosi University of Technology, Tehran, Iran)
DOI: 10.4018/jcrmm.2011070105
OnDemand PDF Download:
$30.00
List Price: $37.50

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.
Article Preview

The first customer relationship management (CRM) software is launched with Siebel systems in 1998. The CRM has strongly grown in UK and USA (Das, 2009). Gartner defined the CRM as a business approach which maximizes the profitability, income, and loyalty of customers by organizing segments of customers, predicting the behaviors that satisfy customers, and implementation of customer-based processes (Bligh & Turk, 2004). CRM is the best approach in the business and information strategy with the aim of improving the relationship with customers and focusing on customers in organizations (Bull, 2003a, 2003b).

Companies are trying to execute models and build decision support tools to improve marketing activities (Gui & Wong, 2004).

Complete Article List

Search this Journal:
Reset
Open Access Articles: Forthcoming
Volume 8: 4 Issues (2017)
Volume 7: 4 Issues (2016)
Volume 6: 4 Issues (2015)
Volume 5: 4 Issues (2014)
Volume 4: 4 Issues (2013)
Volume 3: 4 Issues (2012)
Volume 2: 4 Issues (2011)
Volume 1: 4 Issues (2010)
View Complete Journal Contents Listing