Aneta Rybicka

Application of Discrete Choice Methods in Consumer Preference Analysis

Stated consumer preferences refer to hypothetical market behaviour of consumers. In this case the analytical methods are based on data collected a priori by means of surveys to register intentions stated by consumers at the moment of survey taking. The methods used for stated preference analysis include, for instance, discrete choice methods. The general concept of discrete choice methods results from random utility theory. The consumer preference analysis by means of discrete choice methods is based on probability regression models. In this paper a conditional logit model is used to analyse consumer preferences measured against a nominal scale. An example discussed is the result of analysing the preferences of light beer consumers on the basis of a sample of 235 respondents.

Ratings-/Rankings-Based Versus Choice-Based Conjoint Analysis for Predicting Choices

Nowadays, for market simulation in consumer markets with multi-attributed products, choice-based conjoint analysis (CBC) is most popular. The popularity stems—on one side—from the possibility to use online-panels for affordable data collection and—on the other side—from the possibility to estimate part worths at the respondent level using only few observations. However, a still open question is, whether this money- and time-saving approach provides the same or even better results than ratings-/rankings-based alternatives. An experiment with 787 students from Poland and Germany is used to answer this question: Cola preferences are measured using CBC as well as ratings-/rankings-based alternatives. The results are compared using the Multitrait-Multimethod Matrix for the estimated part worths and first choice hit rates for holdout choice sets. The experiment shows a superiority of CBC, but also important differences between Polish and German cola consumers that outweigh methodological differences.