Skip to main content

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

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.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

References

  • Agresti A (2010) Analysis of ordinal categorical data, 2nd edn. Wiley, NY

    Book  Google Scholar 

  • Bogue J, Ritson C (2004) Understanding consumers perceptions of product quality for lighter dairy products through the integration of marketing and sensory information. Acta Agr Scand C-Econ 1:67–77

    Google Scholar 

  • Breiman L (1996) Bagging predictors. Mach Learn 24:123–140

    MathSciNet  MATH  Google Scholar 

  • Breiman L (2001) Random forest. Mach Learn 45:5–32

    MATH  Article  Google Scholar 

  • Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Chapman & Hall, NY

    MATH  Google Scholar 

  • Brentari E, Carpita M, Vezzoli M (2012) CRAGGING: a novel approach for inspecting Italian wine quality. In: Proceedings of the 12th European symposium on statistical methods for the food industry. Agrostat 2012, Paris, pp 343–350

  • Brentari E, Levaggi R, Zuccolotto P (2011) Pricing strategies for Italian red wine. Food Qual Prefer 22:725–732

    Article  Google Scholar 

  • Brentari E, Zuccolotto P (2010) The implicit value of chemical and sensorial quality in the hedonic analysis of low-priced Italian red wines. In: Proceedings of the 11th European symposium on statistical methods for the food industry. Agrostat 2010, Benevento, pp 269–276

  • Brentari E, Zuccolotto P (2011) The impact of chemical and sensory characteristics on the market price of Italian red wines. Electron J Appl Stat An 4:265–276

    Google Scholar 

  • Cicia G, Corduas M, Del Giudice T, Piccolo D (2010) Valuing consumer preferences with the CUB model: a case study of fair trade coffee. Int J Food System Dynamics 1:82–93

    Google Scholar 

  • Corduas M, Iannario M, Piccolo D (2009) A class of statistical models for evaluating services and performances. In: Bini M (ed) Statistical methods for the evaluation of educational services and quality of products. Springer, Berlin, pp 99–117

    Chapter  Google Scholar 

  • Corduas M, Cinquanta L, Ievoli C (2012) A statistical analysis of consumer perception of wine attributes. Quad Stat 14:77–80

    Google Scholar 

  • D’Elia A, Piccolo D (2005) A mixture model for preference data analysis. Comput Stat Data An 49:917–934

    MathSciNet  MATH  Article  Google Scholar 

  • Everitt BS, Hand DJ (1981) Finite mixture distributions. Chapman & Hall, London

    MATH  Book  Google Scholar 

  • Friedman JH, Popescu BE (2005) Predictive learning via rule ensembles. Technical report, Stanford University, Department of Statistics

  • Iannario M (2007) A statistical approach for modelling urban audit perception surveys. Quad Stat 9:149–172

    Google Scholar 

  • Iannario M (2009) Fitting measures for ordinal data models. Quad Stat 11:39–72

    Google Scholar 

  • Iannario M (2010) On the identifiability of a mixture model for ordinal data. Metron LXVIII:87–94

    Google Scholar 

  • Iannario M (2012a) Modelling shelter choices in a class of mixture models for ordinal responses. Stat Method Appl 21:1–22

    MathSciNet  Article  Google Scholar 

  • Iannario M (2012b) Preliminary estimators for a mixture model of ordinal data. Adv Data Anal Classif 6:163–184

    Google Scholar 

  • Iannario M, Piccolo D (2009) A program in cub models inference, Version 2.0. http://www.dipstat.unina.it/CUBmodels1/

  • Iannario M, Piccolo D (2010) A new statistical model for the analysis of customer satisfaction. Qual Technol Quantit Manag 7:149–168

    Google Scholar 

  • Iannario M, Piccolo D (2012) CUB models: statistical methods and empirical evidence. In: Kenett RS, Salini S (eds) Modern analysis of customer surveys. Wiley, NY, pp 231–254

    Google Scholar 

  • Köster EP (2003) The psychology of food choice: some often encountered fallacies. Food Qual Prefer 14:359–373

    Article  Google Scholar 

  • Köster EP (2009) Diversity in the determinants of food choice: a psychological perspective. Food Qual Prefer 20:70–82

    Article  Google Scholar 

  • McCullagh P (1980) Regression models for ordinal data (with discussion). J Roy Stat Soc B 42:109–142

    MathSciNet  MATH  Google Scholar 

  • McCullagh P, Nelder JA (1989) Generalized linear models. Chapman & Hall, London

    MATH  Google Scholar 

  • McLachlan G, Krishnan T (2008) The EM algorithm and extensions. Wiley, NY

    MATH  Book  Google Scholar 

  • McLachlan G, Peel GJ (2000) Finite mixture models. Wiley, NY

    MATH  Book  Google Scholar 

  • Manisera M, Piccolo D, Zuccolotto P (2011) Analyzing and modelling rating data for sensory analysis in food industry. Quad Stat 13:69–82

    Google Scholar 

  • Philippe F, Schacher L, Adolphe DC, Dacremont C (2003) The sensory panel applied to textile goods a new marketing tool. J Fash Mark Manag 7:235–248

    Google Scholar 

  • Piccolo D (2003) On the moments of a mixture of uniform and shifted binomial random variables. Quad Stat 5:85–104

    Google Scholar 

  • Piccolo D (2006) Observed information matrix for MUB models. Quad Stat 8:33–78

    Google Scholar 

  • Piccolo D, D’Elia A (2008) A new approach for modelling consumers’ preferences. Food Qual Prefer 19:247–259

    Article  Google Scholar 

  • Piccolo D, Iannario M (2010) A new approach for modelling consumers’ preferences. In: Proceedings of the 11th European symposium on statistical methods for the food industry. University of Sannio, Benevento, Academy School, Afragola, pp 139–148

  • Sandri M, Zuccolotto P (2008) A bias correction algorithm for the Gini measure of variable importance. J Comput Graph Stat 17:1–18

    MathSciNet  Article  Google Scholar 

  • Sandri M, Zuccolotto P (2009) Analysis and correction of bias in total decrease in node impurity measures for tree-based algorithms. Stat Comput 20:393–407

    MathSciNet  Article  Google Scholar 

  • Strobl C, Boulesteix A-L, Augustin T (2007) Unbiased split selection for classification trees based on the Gini Index. Comput Stat Data An 52:483–501

    MathSciNet  MATH  Article  Google Scholar 

  • Van Trijp HCM, Schifferstein HNJ (1995) Sensory analysis in marketing practice: comparison and integration. J Sens Stud 10:127–147

    Article  Google Scholar 

  • Zironi R, Odello L, Brentari E (2003) Un nuovo indice per misurare la qualità edonica del vino. Il Sommelier 19:15–17

    Google Scholar 

Download references

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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Maria Iannario.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

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

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11634-012-0120-4

Keywords

  • Sensory analysis
  • Ordinal data
  • cub models
  • Italian coffee