Nonparametric Bayesian methods have proved successful in analyzing many, otherwise intractable, statistical problems. We use two of them to examine how different consumer preference determinants of perceived risk help to explain the choice between store and national brands. Dirichlet Process Mixture Models are shown to enhance and extend the usual regression methodology by: (I) distinguishing product category heterogeneity between consumers; (II) using this assessed heterogeneity to make coherent, informative predictions about individual consumers and (homogeneous) groups of consumers; and (III) supporting the fitting of such models through the efficient selection of hyperparameters. Nonparametric Bayesian methodology, which treats some missing data as ?censored?, is shown to enhance our results. A further improvement involves modeling error using another nonparametric Bayesian method: Polya Tree Urn Priors. Joint work with Indrajit Sinha.