Identifying the drivers of overall customer satisfaction assumes that the component scores can be uniquely recalled and reported from memory. If the component scores are a reflection of an overall measure, such as with haloed responses, instead of containing independent information on its formation, then they should not be used in a driver analysis. There is likely a mixture of formed and haloed responses in all surveys of satisfaction, which potentially distorts inferences about the relationship between the component scores and the overall measure of satisfaction. In this paper we develop a Bayesian mixture model that effectively separates out the haloed responses and apply it to two customer satisfaction datasets. The proposed model results in improved fit to the data, stronger driver effects, and more reasonable inferences.
Keywords: Bayesian inference; Mixture models; Ordinal data; Customer satisfaction
Biography: Thomas Otter is professor of marketing at Goethe University Frankfurt. His research focuses on Bayesian modeling with application to marketing. He uses Bayesian statistics and MCMC techniques to develop and refine quantitative marketing models by incorporating psychological and economic theory. His research has been published in Journal of Marketing Research, Marketing Science, Quantitative Marketing and Economics, Journal of Business & Economic Statistics, International Journal of Research in Marketing, Psychometrika and Marketing Letters.