A two-stage disaggregate attribute choice model is proposed and empirically implemented. The first stage of the model is attribute processing to screen the number of alternatives down to a lesser number. The second stage is brand (alternative) processing which considers the attributes simultaneously while allowing for tradeoffs among the attributes. This two-stage approach is then applied to the same real world data set as two single stage disaggregate models, logit and Maximum-Likelihood-Hierarchical (MLH) which are state of the art models representing the alternative and attribute processing approaches, respectively. The predictive accuracy of the two-stage approach compares favorably to the single stage models. In addition, it seems to offer diagnostic information that can provide managerial insights not found in the output of the single stage model.
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