Data mining application for customer segmentation based on loyalty: An iranian food industry case study

Author

Hajiha, Ali ; Radfar, Reza ; Malayeri, Samira Sarafi

Author_Institution

Dept. of Ind. Manage., Islamic Azad Univ., Tehran, Iran

fYear

2011

fDate

6-9 Dec. 2011

Firstpage

504

Lastpage

508

Abstract

Data Mining (DM) is a powerful new technique to help companies discover the patterns and trends in their customers´ preferences. It is also a well-known tool for customer relationship management (CRM). Data mining methodology has made a tremendous contribution for researchers wanting to extract hidden knowledge and information. This study has proposed a new procedure, based on an expanded RFM model, by including two additional parameters D and C. It constructs a model for clustering customer value based on RFMDC attributes and K-means algorithm. We evaluate the result and suggest suitable behavior policies for each cluster. The developed methodology has been implemented for Kalleh dairy company in Iran to illustrate the proposed procedure.

Keywords

customer satisfaction; data mining; food processing industry; pattern clustering; production engineering computing; Iranian food industry; K-means algorithm; Kalleh dairy company; customer loyalty; customer preferences; customer relationship management; customer segmentation; data mining application; expanded RFM model; hidden knowledge extraction; pattern discovery; Algorithm design and analysis; Clustering algorithms; Companies; Customer relationship management; Data mining; Databases; Customer loyalty; Customer relationship management; Data mining; K-means algorithm; RFM model;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial Engineering and Engineering Management (IEEM), 2011 IEEE International Conference on

Conference_Location

Singapore

ISSN

2157-3611

Print_ISBN

978-1-4577-0740-7

Electronic_ISBN

2157-3611

Type

conf

DOI

10.1109/IEEM.2011.6117968

Filename

6117968