A simple model of dynamic brand choice that takes into account the uncertainty faced by the consumer and the consequent information value associated with choice is developed in this paper. The model is developed using the Neumann-Morgenstern expected utility framework. The consumer, on each occasion, chooses from the brands available so as to maximize his expected utility over a finite horizon based on preferences that are dynamically updated with consumption experience. The dependence of current choice on past choices is hypothesized as being due to learning effects. Brands are allowed to vary in their ability to influence long term preferences with consumption experience. The dynamic model is estimated on scanner panel data using the conditional choice probability (CCP) estimation procedure, proposed by Hotz and Miller [1993], that is computationally simple compared to the extant methods of estimating dynamic models. Results support the hypothesis that the consumer is not perfectly informed and is forward looking. Models that explicitly take such effects into account fit the data better than reduced form approaches.Â