Customer Segmentation in Foreign Trade based on Clustering Algorithms Case Study: Trade Promotion Organization of Iran

The goal of this paper is to segment the countries based on the value of export from Iran during 14 years ending at 2005. To measure the dissimilarity among export baskets of different countries, we define Dissimilarity Export Basket (DEB) function and use this distance function in K-means algorithm. The DEB function is defined based on the concepts of the association rules and the value of export group-commodities. In this paper, clustering quality function and clusters intraclass inertia are defined to, respectively, calculate the optimum number of clusters and to compare the functionality of DEB versus Euclidean distance. We have also study the effects of importance weight in DEB function to improve clustering quality. Lastly when segmentation is completed, a designated RFM model is used to analyze the relative profitability of each cluster.

Authors:

• Samira Malekmohammadi Golsefid
• Mehdi Ghazanfari
• Somayeh Alizadeh

Keywords:

• Customers segmentation
• Customer relationship management
• Clustering
• Data Mining

References:

[1] Berson&Stephen Smith&Kurt Thearling , "Bulding Data Mining
Application For Crm" , Mcgraw-Hill, 2001, Ch 13.
[2] Nong Ye, "The Handbook of Data Mining", Lawrence Erlbaum Associates, Publishers Mahwah, New Jersey London, 2003, Ch2,10.
[3] Hsiao-Fan Wang & Wei-Kuo Hong, "Managing Customer Profitability
In A Competitive Market By Continuous Data Mining", Department Of Industrial Engineering And Engineering Management, National Tsing
Hua University, Hsinchu, Taiwan, Roc, 2005. [4] Chris Rygielski A & Jyun-Cheng Wang B, David C. Yen A, (2002), "Data Mining Techniques For Customer Relationship Management",
Technology In Society 24, 2002, Pp483-502.
[5] Hyunseok Hwang, Taesoo Jung, Euiho Suh, "An Ltv Model And
Customer Segmentation Based On Customer Value: A Case Study On
The Wireless Telecommunication Industry", Expert Systems With
Applications 26, 2004, Pp181-188.
[6] Su-Yeon Kim , Tae-Soo Jung, Eui-Ho Suh, Hyun-Seok Hwang,
"Customer Segmentation And Strategy Development Based On
Customer Lifetime Value: A Case Study", Expert Systems With Applications 31, 2006, Pp101-107.
[7] C.-Y. Tsai, C.-C. Chiu, "A Purchase-Based Market Segmentation
Methodology", Expert Systems With Applications 27, 2004, Pp265-276.
[8] H.W. Shina, S.Y. Sohnb, "Segmentation Of Stock Trading Customers
According To Potential Value",Expert Systems With Applications 27,
2004, Pp 27-33.
[9] Jedid-Jah Jonkera, Nanda Piersmab, Dirk Van Den Poelc, "Joint
Optimization Of Customer Segmentation And Marketing Policy To
Maximize Long-Term Profitability", Expert Systems With Applications
27, 2004, Pp159-168.
[10] Pauline A. Wilcox, Calin Gurau, "Business Modeling With Uml: The
Implementation Of Crm System For Online Retailing", Jornal Of
Retailing And Consumer Services 10, 2003, Pp181-191.
[11] Wagner A. Kamakura, Michel Wedel, Fernando De Rosa, Jose Afsonso
Mazzon, "Cross-Selling Through Database Marketing: A Mixed Data
Factor Analyzer For Data Augmentation And Prediction", Intern J Of
Research In Marketing 20, 2003, Pp45-65.
[12] Arindam Banerjee And Joydeep Ghosh, "Clickstream Clustering Using
Weighted Longest Common Subsequences" , Dep Of Electrical
Engineering University Of Texas At Austin, 2002.
[13] Jiawei Han And Micheline Kamber,"Data Mining: Cluster Analysis",
Department Of Computer Science ,University Of Illinois At Urbana-
Champaign, 2006.
[14] Pang-Ning Tan, Michael Steinbach, Vipin Kumar, "Introduction To Data
Mining", Pearson Addison Wesley, 2006.
[15] Nair, G. J., & Narendran, T. T., "Cluster Goodness: A New Measure Of
Performance For Cluster Formation In The Design Of Cellular
Manufacturing Systems", International Journal Of Production
Economics, 1997, Pp49-61.
[16] Hughes, A. M., "Strategic Database Marketing: The Masterplan For
Starting And Managing A Profitable, Customer-Based Marketing
Program", Probus Pub Co., 1994.