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Mature market segmentation: a comparison of artificial neural networks and traditional methods

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Abstract

The need for in-depth knowledge of mature market segments and the need to overcome the limitations of using traditional methods to segment them motivate this study. The research objectives are (1) to examine neural networks, specifically Kohonen’s self-organising maps (SOM), as an alternative to traditional statistical segmentation methods (hierarchical and non-hierarchical cluster analysis) and (2) to identify segments in the mature market which may direct its targeting. The results show the superiority of non-hierarchical clustering and SOM over hierarchical clustering, and demonstrate their complementary nature. In addition, significant segments with particular characteristics are found in the mature market.

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Acknowledgments

Joaquin Aldas-Manzano acknowledges the financial support of the research project of the Spanish Ministry of Education and Sciences, FEDER (SEC2008-03813/ECON).

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Authors and Affiliations

  1. Facultat d’Economia, Universitat de València, Avda. de los Naranjos s/n, 46022, Valencia, Spain

    Enrique Bigné, Inés Küster & Natalia Vila

  2. Facultat d’Economia, Universitat de València and IVIE, Avda. de los Naranjos s/n, 46022, Valencia, Spain

    Joaquin Aldas-Manzano

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  1. Enrique Bigné
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  3. Inés Küster
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Correspondence to Inés Küster.

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Bigné, E., Aldas-Manzano, J., Küster, I. et al. Mature market segmentation: a comparison of artificial neural networks and traditional methods. Neural Comput & Applic 19, 1–11 (2010). https://doi.org/10.1007/s00521-008-0226-y

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