Statistical Modeling for the Evaluation of Consumers’ Preferences

Abstract

In 2009 a survey was performed in Veneto, a region in the north-east of Italy, to study the demand for wine and specifically for Passito, a typical Italian wine. The main goal of the study consisted in analyzing how the preferences and consumption habits of Passito vary depending on consumers’ characteristics. Specifically two kinds of statistical methods were applied: Covariate Uniform Binomial (CUB) model, a statistical approach for ordinal data to study the feeling toward Passito and the uncertainty of the respondents; classical logistic regression analysis, to describe how the attitude toward passito can be modeled as function of consumers’ covariates. Gender and residence were the most important covariates, useful in defining segments of consumers with significant differences in terms of Passito's preferences and consumption behavior. The logistic regression analysis allowed to complete the statistical analysis based on CUB models validating the results of the CUB model and estimating a model useful to predict the attitude toward the considered product for specific sub-groups of consumers.