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

Maria M. Abad-Grau, Maria Tajtakova and Daniel Arias-Aranda

International Journal of Business Environment, 2009, vol. 2, issue 3, 356-375

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. (search for similar items in EconPapers)
Date: 2009
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Persistent link: https://EconPapers.repec.org/RePEc:ids:ijbenv:v:2:y:2009:i:3:p:356-375

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