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Sensory analysis in the food industry as a tool for marketing decisions

Author

Listed:
  • Maria Iannario
  • Marica Manisera
  • Domenico Piccolo
  • Paola Zuccolotto

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. Copyright Springer-Verlag Berlin Heidelberg 2012

Suggested Citation

  • Maria Iannario & Marica Manisera & Domenico Piccolo & Paola Zuccolotto, 2012. "Sensory analysis in the food industry as a tool for marketing decisions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 6(4), pages 303-321, December.
    • Handle: RePEc:spr:advdac:v:6:y:2012:i:4:p:303-321
    DOI: 10.1007/s11634-012-0120-4
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    References listed on IDEAS

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    1. Strobl, Carolin & Boulesteix, Anne-Laure & Augustin, Thomas, 2007. "Unbiased split selection for classification trees based on the Gini Index," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 483-501, September.
    2. Cicia, Gianni & Corduas, Marcella & Del Giudice, Teresa & Piccolo, Domenico, 2010. "Valuing Consumer Preferences with the CUB Model: A Case Study of Fair Trade Coffee," International Journal on Food System Dynamics, International Center for Management, Communication, and Research, vol. 1(1), pages 1-12.
    3. Maria Iannario, 2010. "On the identifiability of a mixture model for ordinal data," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(1), pages 87-94.
    4. D'Elia, Angela & Piccolo, Domenico, 2005. "A mixture model for preferences data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 49(3), pages 917-934, June.
    5. Maria Iannario, 2012. "Modelling shelter choices in a class of mixture models for ordinal responses," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 21(1), pages 1-22, March.
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    Cited by:

    1. Manisera, Marica & Zuccolotto, Paola, 2015. "Identifiability of a model for discrete frequency distributions with a multidimensional parameter space," Journal of Multivariate Analysis, Elsevier, vol. 140(C), pages 302-316.
    2. Yanyu Zhang & Pafe Momoisea & Qixin Lin & Jiaqi Liang & Keegan Burrow & Luca Serventi, 2023. "Evaluation of Sensory and Physicochemical Characteristics of Vitamin B 12 Enriched Whole-Meal Sourdough Bread Fermented with Propionibacterium freudenreichii," Sustainability, MDPI, vol. 15(10), pages 1-15, May.
    3. Corduas, Marcella, 2015. "A statistical model for consumer preferences: the case of Italian extra virgin olive oil," 143rd Joint EAAE/AAEA Seminar, March 25-27, 2015, Naples, Italy 202701, European Association of Agricultural Economists.
    4. Jan Małecki & Konrad Terpiłowski & Maciej Nastaj & Bartosz G. Sołowiej, 2022. "Physicochemical, Nutritional, Microstructural, Surface and Sensory Properties of a Model High-Protein Bars Intended for Athletes Depending on the Type of Protein and Syrup Used," IJERPH, MDPI, vol. 19(7), pages 1-15, March.
    5. Amalia Vanacore & Maria Sole Pellegrino, 2019. "Checking quality of sensory data via an agreement-based approach," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(5), pages 2545-2556, September.
    6. Manisera, Marica & Zuccolotto, Paola, 2014. "Modeling rating data with Nonlinear CUB models," Computational Statistics & Data Analysis, Elsevier, vol. 78(C), pages 100-118.
    7. Maria Iannario & Marica Manisera & Domenico Piccolo & Paola Zuccolotto, 2020. "Ordinal Data Models for No-Opinion Responses in Attitude Survey," Sociological Methods & Research, , vol. 49(1), pages 250-276, February.

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