ENBIS Spring Meeting 2018

4 – 6 June 2018; Florence, Italy Abstract submission: 17 November 2017 – 20 April 2018

Choice models with mixtures: an application to a cocktail experiment

5 June 2018, 11:30 – 11:55

Abstract

Submitted by
Hajar Hamidouche
Authors
Hajar Hamidouche (KU Leuven), Peter Goos (KU Leuven)
Abstract
Choice experiments are frequently used to perform systematic studies of consumer preferences. In these experiments, the properties of products are systematically varied according to an experimental design. After the experiment has been done, the data are analyzed using choice models. Proper modeling allows a perfect understanding of the consumer preferences, the identification of consumer segments and the optimization of
products, and, eventually, offers opportunities for competitive advantage. The literature about choice models is very extensive. However, in the analysis of choice data, products that are mixtures of ingredients have been largely overlooked. This is surprising as a great share of products, such as shampoos, cakes and cocktails, actually are mixtures. The literature on the modeling of data from traditional mixture experiments is also substantial. In this paper, we combine the theory about choice models and traditional mixture models. We will apply the resulting model to data from a real-life
experiment in which consumers made pair-wise comparisons between seven cocktails. More specifically, we will incorporate the Scheffé model, one of the most commonly used mixture models, in three choice models. For the choice models, we will first assume consumer homogeneity. Next, we will allow for heterogeneity among individuals. Therefore, we will discuss the multinomial logit model, the mixed logit model and the latent class model. For identifying segments, besides the latent class model, we will explore a two-stage approach in which subject gradients, Hierarchical Bayes estimates and Firth individual-level estimates are used as input for a cluster analysis.

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