Abstract
This paper outlines innovative techniques for the segmentation of consumer markets. It compares a new self-controlled growing neural network with a recent growing k-means algorithm. A critical issue is the identification of the “right” number of clusters, which is externally validated by the JUMP-criterion. The empirical application counters several objections recently raised against the use of cluster analysis for market segmentation.
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Decker, R., Scholz, S.W., Wagner, R. (2006). Growing Clustering Algorithms in Market Segmentation: Defining Target Groups and Related Marketing Communication. In: Zani, S., Cerioli, A., Riani, M., Vichi, M. (eds) Data Analysis, Classification and the Forward Search. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-35978-8_3
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DOI: https://doi.org/10.1007/3-540-35978-8_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35977-7
Online ISBN: 978-3-540-35978-4
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