We describe the application of a nested logit function for modeling brand choice using household transaction data from the Indian market. This is unique since it is one of the first attempts to integrate disparate consumer information sources available at various levels of aggregation towards developing a prediction model for brand market share. We further develop a methodology for brand market share decomposition into components that can be attributable to various explanatory variables. The implications are significant since this methodology helps in using behavioral tracking data towards developing a decision tool to evaluate marketing programs.