Abstract
In the food industry, sensory analysis can be useful to direct marketing decisions concerning not only products, for example product positioning with respect to competitors, but also market segmentation, customer relationship management, advertising strategies and price policies. In this paper we show how interesting information useful for marketing management can be obtained by combining the results from cub models and algorithmic data mining techniques (specifically, variable importance measurements from Random Forest). A case study on sensory evaluation of different varieties of Italian espresso is presented.
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Acknowledgments
The authors thank Luigi Odello (director of CSA) and Prof. Eugenio Brentari (University of Brescia) for making the data available. The first and third Authors have been partly supported by MIUR project PRIN2008: “Modelling latent variables for ordinal data: statistical methods and empirical evidence” (CUP E61J10000020001), Research Unit at University of Naples Federico II, and by FARO project 2011. M. Iannario benefits of a Fulbright scholarship at Department of Statistics and Actuarial Science, University of Iowa.
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Iannario, M., Manisera, M., Piccolo, D. et al. Sensory analysis in the food industry as a tool for marketing decisions. Adv Data Anal Classif 6, 303–321 (2012). https://doi.org/10.1007/s11634-012-0120-4
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DOI: https://doi.org/10.1007/s11634-012-0120-4