Title: Machine learning methods for the market segmentation of the performing arts audiences

Authors: Maria M. Abad-Grau, Maria Tajtakova, Daniel Arias-Aranda

Addresses: ETS Ingenieria Informatica, Departamento de Lenguajes y Sistemas Informaticos, Universidad de Granada, Granada 18071, Spain. ' Faculty of Commerce, Department of Marketing, University of Economics in Bratislava, Dolnozemska cesta 1, 852 35 Bratislava, Slovakia. ' Facultad de Ciencias Economicas y Empresariales, Departamento de Organizacion de Empresas, Campus de Cartuja s/n, Universidad de Granada, Granada 18071, Spain

Abstract: The interaction of human experts with machine learning and data mining tools leads to improved results in decision-making support systems. In marketing decisions related to market segmentation, the use of only one technique does not guarantee an optimal solution, as such a solution may not even be achievable. In this paper, we analyse the market segmentation decisions in the performing arts through a combination of expert opinions and machine learning algorithms in order to obtain a consensual model that allows a better understanding of market preferences together with a deep knowledge about reliability in the obtained results. The results and data were applied to build a model of market segmentation of students based on their attendance in, attitudes towards, and intentions in attending opera and ballet performances.

Keywords: market segmentation; machine learning; data mining; performing arts; opera; ballet; business environment; decision support systems; DSS; human experts; market preferences; data reliability.

DOI: 10.1504/IJBE.2009.023796

International Journal of Business Environment, 2009 Vol.2 No.3, pp.356 - 375

Published online: 11 Mar 2009 *

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