Visualizing market segmentation using self-organizing maps and Fuzzy Delphi method - ADSL market of a telecommunication company

A common problem for marketing strategists is how to appropriately segment the market and select segment-specific marketing strategies. This paper presents a novel approach which integrates Fuzzy Delphi method, self-organizing maps (SOM) and a visualization technique to cluster customers according to their various characteristic variables and visualize segments by producing colorful market maps. These market maps not only help the managers to see fully visualized clusters of market but also reveal mutual non-linear correlations between different customers' characteristic variables. In this research we studied ADSL service market of an Iranian Telecommunication Company. SOM algorithm and visualizing technique were implemented in MATLAB environment to produce market maps of data set.

[1]  Chih-Fong Tsai,et al.  Market segmentation based on hierarchical self-organizing map for markets of multimedia on demand , 2008, Expert Syst. Appl..

[2]  Yiu-ming Cheung,et al.  k*-Means: A new generalized k-means clustering algorithm , 2003, Pattern Recognit. Lett..

[3]  Jonathan Z. Bloom,et al.  Tourist market segmentation with linear and non-linear techniques , 2004 .

[4]  R. J. Kuo,et al.  Integration of ART2 neural network and genetic K-means algorithm for analyzing Web browsing paths in electronic commerce , 2005, Decis. Support Syst..

[5]  Alastair M. Morrison,et al.  Benefit segmentation of Japanese pleasure travelers to the USA and Canada: selecting target markets based on the profitability and risk of individual market segments. , 2002 .

[6]  Melody Y. Kiang,et al.  The effect of sample size on the extended self-organizing map network - A market segmentation application , 2007, Comput. Stat. Data Anal..

[7]  R. J. Kuo,et al.  Integration of self-organizing feature maps neural network and genetic K-means algorithm for market segmentation , 2006, Expert Syst. Appl..

[8]  M. Roubens Fuzzy clustering algorithms and their cluster validity , 1982 .

[9]  K. Chang,et al.  Integration of Self-Organizing Feature Maps and Genetic-Algorithm-Based Clustering Method for Market Segmentation , 2004, J. Organ. Comput. Electron. Commer..

[10]  R. J. Kuo,et al.  A decision support system for sales forecasting through fuzzy neural networks with asymmetric fuzzy weights , 1998, Decis. Support Syst..

[11]  Richard C. T. Lee,et al.  A Triangulation Method for the Sequential Mapping of Points from N-Space to Two-Space , 1977, IEEE Transactions on Computers.

[12]  Samuel Kaski,et al.  Visualizing the Clusters on the Self-Organizing Map , 1994 .

[13]  P. Kotler,et al.  Marketing management : analysis, planning, and control , 1973 .

[14]  Erkki Oja,et al.  Engineering applications of the self-organizing map , 1996, Proc. IEEE.

[15]  Kyoung-jae Kim,et al.  A recommender system using GA K-means clustering in an online shopping market , 2008, Expert Syst. Appl..

[16]  Ian H. Witten,et al.  Data mining - practical machine learning tools and techniques, Second Edition , 2005, The Morgan Kaufmann series in data management systems.

[17]  Ian Witten,et al.  Data Mining , 2000 .

[18]  Alfredo Vellido,et al.  Neural networks in business: a survey of applications (1992–1998) , 1999 .

[19]  J. Carroll,et al.  A Feature-Based Approach to Market Segmentation via Overlapping K-Centroids Clustering , 1997 .

[20]  Melody Y. Kiang,et al.  An extended self-organizing map network for market segmentation - a telecommunication example , 2006, Decis. Support Syst..

[21]  Kenneth C. Gehrt,et al.  A Shopping Orientation Segmentation of French Consumers: Implications for Catalog Marketing , 1998 .

[22]  Walter Baets,et al.  Neural Networks and Statistical Techniques in Marketing Research , 1994 .

[23]  Juha Vesanto,et al.  SOM-based data visualization methods , 1999, Intell. Data Anal..

[24]  Suzanne D. Pawlowski,et al.  The Delphi method as a research tool: an example, design considerations and applications , 2004, Inf. Manag..

[25]  Ying-Feng Kuo,et al.  Constructing performance appraisal indicators for mobility of the service industries using Fuzzy Delphi Method , 2008, Expert Syst. Appl..

[26]  T. Kohonen,et al.  Bibliography of Self-Organizing Map SOM) Papers: 1998-2001 Addendum , 2003 .

[27]  William R. Dillon,et al.  Capturing Individual Differences in Paired Comparisons: An Extended BTL Model Incorporating Descriptor Variables , 1993 .

[28]  N. Dalkey,et al.  An Experimental Application of the Delphi Method to the Use of Experts , 1963 .

[29]  D. T. Cadden Neural networks and the mathematics of chaos-an investigation of these methodologies as accurate predictors of corporate bankruptcy , 1991, Proceedings First International Conference on Artificial Intelligence Applications on Wall Street.

[30]  Jillian Anable,et al.  'Complacent Car Addicts' or 'Aspiring Environmentalists'? Identifying travel behaviour segments using attitude theory , 2005 .

[31]  H. L. Le Roy,et al.  Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability; Vol. IV , 1969 .

[32]  M. Wedel,et al.  Market Segmentation: Conceptual and Methodological Foundations , 1997 .

[33]  Leo L. Pipino,et al.  A pilot study of fuzzy set modification of Delphi , 1985 .

[34]  Harald Hruschka,et al.  Comparing performance of feedforward neural nets and K-means for cluster-based market segmentation , 1999, Eur. J. Oper. Res..

[35]  G. W. Milligan,et al.  An examination of the effect of six types of error perturbation on fifteen clustering algorithms , 1980 .

[36]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[37]  K. Seltman Marketing for management. , 2004, Marketing health services.

[38]  C. E. Pykett Improving the efficiency of Sammon's nonlinear mapping by using clustering archetypes , 1978 .

[39]  J. MacQueen Some methods for classification and analysis of multivariate observations , 1967 .

[40]  Sungsoon Hwang,et al.  Using fuzzy clustering methods for delineating urban housing submarkets , 2007, GIS.