Using Data Mining in Customer Relationship Management
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Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa
Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty
Seyed Mohammad Seyed Hosseini *, Anahita Maleki, Mohammad Reza Gholamian
Industrial Engineering Department, Iran University of Science and Technology, Tehran, Iran
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Data mining (DM) methodology has a tremendous contribution for researchers to extract the hidden knowledge and information which have been inherited in the data used by researchers. This study has proposed a new procedure, based on expanded RFM model by including one additional parameter, joining WRFM-based method to K-means algorithm applied in DM with K-optimum according to Davies– Bouldin Index, and then classifying customer product loyalty in under B2B concept. The developed methodology has been implemented for SAPCO Co. in Iran. The result shows a tremendous capability to the ﬁrm to assess his customer loyalty in marketing strategy designed by this company in comparing with random selection commonly used by most companies in Iran. Ó 2009 Elsevier Ltd. All rights reserved.
Keywords: Customer relationship management Customer loyalty K-Means algorithm RFM model
1. Introduction In a B2B environment, suppliers and/or service providers usually need to understand the nature and characteristics of their customers. As customer attraction and satisfaction are the main objectives of any leading company, so the main objective of this article is to provide an effective and efﬁcient methodology to be used for implementing the ﬁrm’s objective to the best of possible. This part mainly reviews the studies related to customer relationship management, customer loyalty, RFM model, K-means algorithm. 1.1. Customer relationship management Since the early 1980s, the concept of customer
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