Abstract
The number of channels available for companies and customers to communicate with one another has increased dramatically over the past several decades. Although some market segmentation efforts utilize high-level customer interaction statistics, in-depth information regarding customers’ use of different communication channels is often ignored. Detailed customer interaction information can help companies improve the way that they market to customers by taking into consideration customers’ behaviour patterns and preferences. However, a key challenge of interpreting customer contact information is that many channels have only been in existence for a relatively short period of time, and thus, there is limited understanding and historical data to support analysis and classification. Cluster analysis techniques are well suited to this problem because they group data objects without requiring advance knowledge of the data’s structure. This chapter explores the use of various cluster analysis techniques to identify common characteristics and segment customers based on interaction information obtained from multiple channels. A complex synthetic data set is used to assess the effectiveness of k-means, fuzzy c-means, genetic k-means, and neural gas algorithms, and identify practical concerns with their application.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Allred, C., Hite, K., Fonzone, S., Greenspan, J., Larew, J.: Modeling and data analysis in the credit card industry: bankruptcy, fraud, and collections. In: IEEE Systems and Information Design Symposium (2002)
Balakrishnan, P.V., Cooper, M.C., Jacob, V.S., Lewis, P.A.: A study of the classification capabilities of neural networks using unsupervised learning - A comparison with K-means clustering. Psychometrika 59(4), 509–525 (1994)
Balakrishnan, P.V., Cooper, M.C., Jacob, V.S., Lewis, P.A.: Comparative performance of the FSCL neural net and K-means algorithm for market segmentation. European Journal of Operational Research 93(2), 346–357 (1996)
Bezdek, J.C.: Cluster validity with fuzzy set. Journal of Cybernet 3, 58–72 (1974)
Calinski, T., Harabasz, J.: A dendrite method for cluster analysis. Communication in statistics 3, 1–27 (1974)
Campbell, D., Frei, F.: The persistence of customer profitability: empirical evidence and implications from a financial services firm. Journal of Service Research 7(2), 107–123 (2004)
Campello, R.J.G.B.: A fuzzy extension of the Rand index and other related indexes for clustering and classification assessment. Pattern Recognition Letters 7(28), 833–841 (2007)
Dunn, J.C.: Well separated clusters and optimal fuzzy partitions. Journal of Cybernetica 4, 95–104 (1974)
Edelman, D.B.: An application of cluster analysis in credit control. IMA Journal of Mathematics Applied in Business and Industry 4, 81–87 (1992)
Fritzke, B. (1997), Some competitive learning methods, http://www.neuroinformatik.ruhr-uni-bochum.de/ini/VDM/research/gsn/JavaPaper/ (accessed July 24, 2009)
Goldberg, R.: Proc. Factor: How to interpret the output of a real-world example. In: SESUG 1997 (1997)
Gordon, A.D.: Classification, 2nd edn. Chapman and Hall, Boca Raton (1999)
Halkidi, M., Batistakis, Y., Vazirgiannis, M.: On clustering validation techniques. Journal of Intelligent Information Systems 17, 107–145 (2001)
Halkidi, M., Batistakis, Y., Vazirgiannis, M.: Cluster validity methods. SIGMOD 31, 40–45 (2002)
Han, J.W., Kamber, M.: Data mining: concepts and techniques. Morgan Kaufmann Publishers, San Francisco (2001)
Hartigan, J.A.: Clustering algorithms. Wiley, New York (1975)
Hartigan, J.A., Wong, M.A.: A K-means clustering algorithm. Applied Statistics 28, 100–108 (1979)
Hruschka, E.R., Campello, R.J.G.B., Freitas, A.A., De Carvalho, A.C.P.L.F.: A survey of evolutionary algorithms for clustering. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 39(2), 133–155 (2009)
Hitt, L., Frei, F.: Do better customers utilize electronic distribution channels? The case of PC banking. Management Science 48(6), 732–748 (2002)
Kaufman, L., Rousseeuw, P.J.: Finding groups in data. In: An Introduction to cluster analysis. Wiley, New York (1990)
Kohonen, T.: Self-organizing maps. Springer, New York (2001)
MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proc. of the Fifth Berkeley Symposium on Math., pp. 281–297 (1967)
Martinetz, T., Berkovich, S., Schulten, K.: ‘Neural-Gas’ network for vector quantization and its application to time-series prediction. IEEE Transactions on Neural Networks 4(4), 558–569 (1993)
Nargundkar, S., Olzer, T.J.: An application of cluster analysis in the financial services industry. Case Study (2000)
Nikhil, R.P., James, C.B., Richard, J.H.: Sequential competitive learning and the fuzzy c-means clustering algorithms. Neural Networks 9(5), 787–796 (1996)
Peng, Y., Kou, G., Shi, Y., Chen, Z.: Improving clustering analysis for credit card accounts classification. In: International Conference on Computational Science, pp. 548–553 (2005)
Pison, G., Struyf, A., Rousseeuw, P.J.: Displaying a clustering with CLUSPLOT. Computational Statistics & Data Analysis 30(4), 381–392 (1999)
Rho, J.J., Moon, B.J., Kim, Y.J., Yang, D.H.: Internet customer segmentation using web log data. Journal of Business & Economics Research 2(11), 59–74 (2004)
Sinisalo, J., Salo, J., Karjaluoto, H., Leppaniemi, M.: Mobile customer relationship management: underlying issues and challenges. Business Process Management Journal 13(6), 771–787 (2007)
Tan, P.N., Steinbach, M., Kumar, V.: Introduction to data mining. Addison Wesley, Reading (2005)
Xie, X.L., Beni, G.: A validity measure for fuzzy clustering. IEEE Trans. Pattern Anal. Mach. Intell. 13(8), 841–847 (1991)
Zakrzewska, D.: On integrating unsupervised and supervised classification for credit risk evaluation. Information Technology and Control 36(1A), 98–102 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Duran, R.E., Zhang, L., Hayhurst, T. (2010). Applying Soft Cluster Analysis Techniques to Customer Interaction Information. In: Casillas, J., Martínez-López, F.J. (eds) Marketing Intelligent Systems Using Soft Computing. Studies in Fuzziness and Soft Computing, vol 258. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15606-9_9
Download citation
DOI: https://doi.org/10.1007/978-3-642-15606-9_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15605-2
Online ISBN: 978-3-642-15606-9
eBook Packages: EngineeringEngineering (R0)