CFP last date
20 May 2024
Reseach Article

Combination of Adaptive Resonance Theory 2 and RFM Model for Customer Segmentation in Retail Company

by I Ketut Gede Darma Putra, A. A. Kt. Agung Cahyawan, Dian Shavitri H.
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 48 - Number 2
Year of Publication: 2012
Authors: I Ketut Gede Darma Putra, A. A. Kt. Agung Cahyawan, Dian Shavitri H.
10.5120/7320-0110

I Ketut Gede Darma Putra, A. A. Kt. Agung Cahyawan, Dian Shavitri H. . Combination of Adaptive Resonance Theory 2 and RFM Model for Customer Segmentation in Retail Company. International Journal of Computer Applications. 48, 2 ( June 2012), 18-23. DOI=10.5120/7320-0110

@article{ 10.5120/7320-0110,
author = { I Ketut Gede Darma Putra, A. A. Kt. Agung Cahyawan, Dian Shavitri H. },
title = { Combination of Adaptive Resonance Theory 2 and RFM Model for Customer Segmentation in Retail Company },
journal = { International Journal of Computer Applications },
issue_date = { June 2012 },
volume = { 48 },
number = { 2 },
month = { June },
year = { 2012 },
issn = { 0975-8887 },
pages = { 18-23 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume48/number2/7320-0110/ },
doi = { 10.5120/7320-0110 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:43:03.874858+05:30
%A I Ketut Gede Darma Putra
%A A. A. Kt. Agung Cahyawan
%A Dian Shavitri H.
%T Combination of Adaptive Resonance Theory 2 and RFM Model for Customer Segmentation in Retail Company
%J International Journal of Computer Applications
%@ 0975-8887
%V 48
%N 2
%P 18-23
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Customer segmentation is one of the important issues in Customer Relationship Management (CRM). This paper demonstrated the fusion of ART 2 algorithm and RFM (Recency, Frequency, and Monetary) model to cluster the customers in Retail Company. Each cluster is validated by searching the overall average value of silhouette index. In this paper used 17. 999 rows of data transaction then was modeled into RFM model and become 499 rows of RFM data. Experiments were done by forming 2 to 6 clusters by changing the value of vigilance parameter (?) and noise suppression (?). In this paper, all clusters formed have overall average Silhouette index value more than 0, especially for 2, 3, 4 clusters formed have overall average Silhouette index value close to 1, indicates most of the silhouette value in all clusters were formed have a positive value or above zero, its means ART 2 clustering algorithm which produces two to six clusters have been able clustering well.

References
  1. Tsiptsis, Kontantinos. Chorianopoulos, Antonios. 2009. Data Mining Techniques in CRM: Inside Customer Segmentation. United Kingdom: John Wiley & Sons, Ltd
  2. Zumstein, D. , 2007, "Customer Performance Measurement: Analysis of the Benefit of a Fuzzy Classification Approach in Customer Relationship Management" (Thesis), Switzerland: University of Fribourg
  3. Shashidhar, H. V. , Subramanian V. , 2011, Customer Segmentation of Bank based on Data Mining – Security Value based Heuristic Approach as a Replacement to K-means Segmentation, International Journal of Computer Applications, Vol. 19, No. 8
  4. Shankar S. , Dr. Purusothaman T. , Kannimuthu, S. , Vishnu, P. K. , 2010, A Novel Utility and Frequency Based Itemset Mining Approach for Improving CRM in Retail Business. International Journal of Computer Applications, Vol. 1, No. 16
  5. Cheng, C. H, Chen, Y. S, 2009, classifying the segmentation of customer value via RFM model and RS theory, Expert Systems with Applications, 36, 4176–4184
  6. Yohana, N. , 2011, Data mining dengan metode fuzzy untuk Customer Relationship Management (CRM) pada perusahaan retail, Master Thesis, Graduate Program, Udayana University, Bali, Indonesia.
  7. Gemala, Y. , 2011, Segmentasi pelanggan dengan algoritma K-Means dan analisa RFM untuk mendukung strategi pengelolaan pelanggan di PT. Indosat Mega Media, Undergraduate Thesis, Sepuluh November Institute of Technology
  8. Chen, C. H. , Khoo, L. P. , Yan, W. , 2002, A strategy for acquiring customer requirement patterns using laddering technique and ART2 neural network, Advanced Engineering Informatics, 16, 229–240
  9. Hughes, A. M. , 1994, Strategic database marketing, Chicago: Probus Publishing Company.
  10. Carpenter, G. , Grossberg, S. , 1987, ART2: stable self-organization of pattern recognition codes for analog input patterns. Appl Optics 26: 4919–30.
  11. Fausset, L. , 1993. Fundamental of Neural Network: Architecture, Algorithms, and Application, Precentice Hall
  12. Carpenter, G. , Grossberg, S. , The art of adaptive recognition by a self-organizing neural network, Computer 1988, 21, 77-88
  13. Rousseeuw, P. J. , "Silhouettes: a graphical aid to the interpretation and validation of cluster analysis", J. Comp App. Math, Vol. 20, 1987, pp. 53-65
Index Terms

Computer Science
Information Sciences

Keywords

Customer Segmentation Rfm Model Art 2 Algorithm Silhouette Index