Predicting purchase decisions with different conjoint analysis methods: a Monte Carlo simulation

Klaus Backhaus and Robert Wilken
Westphalian Wilhelms-University of Muenster

Thomas Hillig
Alstom Schweiz AG


Predicting purchase decisions is highly relevant for marketers who want to estimate the potential success of their products, or market shares. In marketing research and practice, conjoint analyses (CAs) are widely used in this context. During the last 20 years, not only in marketing, but also in other disciplines, numerous variants of conjoint models and parameter estimation methods have been developed (Green & Srinivasan 1978; Green & Srinivasan 1990; Moore et al. 1998), but only some of them have gained broad acceptance in practice (Carroll & Green 1995) and not all conjoint variants seem to be appropriate in order to predict purchase decisions.