Developing a Model for Measuring Customers Loyalty and Value with RFM Technique and Clustering Algorithms
-
10728
Downloads
-
8311
Views
Authors
Razieh Qiasi
- Department of Information Technology, University of Qom, Qom, Iran
Malihe Baqeri-Dehnavi
- Department of Information Technology, University of Qom, Qom, Iran
Behrooz Minaei-Bidgoli
- Department of Computer Engineering, University of Science and Technology, Tehran, Iran
Golriz Amooee
- Department of Information Technology, University of Qom, Qom, Iran
Abstract
In today’s competitive world, moving toward customer-oriented markets with increased access to customer’s transaction data, identifying loyal customers and estimating their lifetime value makes crucial. Since knowledge of customer value provides targeted data for personalized markets, implementing customer relationship management strategy helps organizations to identify and segment customers and create long-term relationships with them, and as a result, they can maximize customer lifetime value. Data mining techniques are known as a powerful tool for this purpose.
The purpose of this paper is customer segmentation using RFM technique and clustering algorithms based on customer’s value, to specify loyal and profitable customers. We also used classification algorithms to obtain useful rules for implementing effective customer relationship management. This paper used a combination of behavioral and demographical characteristics of individuals to estimate loyalty. Finally, the proposed model has been implemented on a grocery store’s data, during 1997 to 1998 in Singapore, to measure customer’s loyalty during these two years.
Share and Cite
ISRP Style
Razieh Qiasi, Malihe Baqeri-Dehnavi, Behrooz Minaei-Bidgoli, Golriz Amooee, Developing a Model for Measuring Customers Loyalty and Value with RFM Technique and Clustering Algorithms, Journal of Mathematics and Computer Science, 4 (2012), no. 2, 172--181
AMA Style
Qiasi Razieh, Baqeri-Dehnavi Malihe, Minaei-Bidgoli Behrooz, Amooee Golriz, Developing a Model for Measuring Customers Loyalty and Value with RFM Technique and Clustering Algorithms. J Math Comput SCI-JM. (2012); 4(2):172--181
Chicago/Turabian Style
Qiasi, Razieh, Baqeri-Dehnavi, Malihe, Minaei-Bidgoli, Behrooz, Amooee, Golriz. "Developing a Model for Measuring Customers Loyalty and Value with RFM Technique and Clustering Algorithms." Journal of Mathematics and Computer Science, 4, no. 2 (2012): 172--181
Keywords
- customer value
- RFM model
- K-means algorithm
- customer relationship management
- loyalty.
MSC
References
-
[1]
H. M. Chuang, C. C. Shen, A study on the application of data mining techniques to enhance customer lifetime value based on the department store industry, the seventh international conference on machine learning and cybernetics, International Conference on Machine Learning and Cybernetics, 2008 (2008), 168--173
-
[2]
M. Khajvand, K. Zolfaghar, S. Ashoori, S. Alizadeh, Estimating customer lifetime value based on RFM analysis of customer purchase behavior: case study, Procedia Computer Science, 3 (2011), 57--63
-
[3]
A. Bhatnagar, S. Ghose, A latent class segmentation analysis of e-shoppers, Journal of Business Research, 57 (2004), 758--767
-
[4]
C. H. Cheng, Y. S. Chen, Classifying the segmentation of customer value via RFM model and RS theory, Expert Systems with Applications, 36 (2009), 4176--4184
-
[5]
V. Ravi, Advances in Banking Technology and Management: Impacts of ICT and CRM, Information science reference, New York (2007)
-
[6]
I. J. Chen, K. Popovich, Understanding customer relationship management (CRM), People, process and technology, Business process management journal, Vol. 9, 672--688, (2003)
-
[7]
P. Kotler, S. Saliba, B. Wrenn, Marketing management: Analysis, planning, and control: Instructor's Manual, Prentice-Hall, New York (2000)
-
[8]
S. Y. Kim, T. S. Jung, E. H. Suh, H. S. Hwang, Customer segmentation and strategy development based on customer lifetime value: A case study, Expert Systems with Applications, 31 (2006), 101--107
-
[9]
N. Glady, B. Baesens, C. Croux, A modified Pareto/NBD approach for predicting customer lifetime value, Expert Systems with Applications, 36 (2009), 2062--207
-
[10]
S. M. Seyed Hosseini, A. Maleki, M. R. Gholamian, Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty, Expert Systems with Applications, 37 (2010), 259--5264
-
[11]
J. A. McCarty, M. Hastak, Segmentation approaches in data-mining: A comparison of RFM, CHAID, and logistic regression, Journal of Business Research, 60 (2007), 656--662
-
[12]
J. Han, M. Kamber, Data mining: Concepts and techniques, Morgan Kaufmann Publishers, San Francisco (2001)
-
[13]
R. S. Wu, P. H. Chou, Customer segmentation of multiple category data in e-commerce using a soft-clustering approach, Electronic Commerce Research and Applications, Vol. 10, 331--341, (2011)
-
[14]
M. L. Roberts, Expanding the role of the direct marketing database, Journal of Direct Marketing, 6 (1992), 51--60
-
[15]
W. Y. Chiang, To mine association rules of customer values via a data mining procedure with improved model: An empirical case study, Expert Systems with Applications, 38 (2011), 1716--1722
-
[16]
U. Kaymak , Fuzzy target selection using RFM variables, IFSA World congress and 20th NAFIPS international conference, 2001 (2001), 1038--1043
-
[17]
R. Kahan, Using database marketing techniques to enhance your one-to-one marketing initiatives, Journal of Consumer Marketing, 15 (1998), 491--493
-
[18]
J. M. C. Schijns, G. J. Schroder, Segment selection by relationship strength, Journal of Direct Marketing, 10 (1996), 69--79
-
[19]
J. Goodman, Leveraging the customer database to your competitive advantage, Journal of Direct Marketing, 55 (1992), 26--27
-
[20]
C. M. Chao, C. T. Yang, Applying data mining on prediction of customer value, Conference of an approach to analyze and implement commercial database (Taipei, Taiwan), 2003 (2003), 159--183
-
[21]
C. S. Lin, Y. Q. Tang, Application of incremental mining and customer’s value analysis to collaborative music recommendations, Journal of Information, Technology and Society, 6 (2006), 1-–26
-
[22]
C. F. Lo, H. H. Wu, E. C. Chang, Y. Y. Cheng, Applying data mining to an outfitter's customer loyalty, and value analysis, Journal of Quality, 15 (2008), 293--303
-
[23]
S. C. Huang, E. C. Chang, H. H. Wu, A case study of applying data mining techniques in an outfitter’s customer value analysis, Expert System with Applications, 36 (2009), 5909--5915