Companies are collecting increasing amounts of information about their customers. This effort is based on the assumption that more information is better and that this information can be leveraged to predict customers' behavior in a variety of situations and product categories. For example, information about a customer's purchase behavior in one category can be helpful in predicting his potential behavior in a related category, which in turn could help a firm in its cross-selling efforts. In this paper, we present a model to better understand and predict a consumer's purchases and preferences when we may have limited or no information about him in one or more product categories. Conceptually this involves leveraging information from purchases of other consumers in multiple categories as well as partial information (e.g., purchase in one of the categories) of the target consumer. Our approach builds on the pioneering work of (Rossi et al. (1996)) who demonstrate the value of purchase information in the context of a single product category. We present results from an extensive simulation as well as an application on scanner panel data. Our simulation shows many interesting and somewhat surprising results. Specifically, we find that compared to a single-category analysis, a cross-category analysis does not lead to any significant improvement in data likelihood in most cases. Therefore, the single-category analysis of (Rossi et al. (1996)) is even more powerful than previously thought. However, we also find that a cross-category analysis does improve parameter recovery in many situations as compared to a single-category analysis. It is in these conditions that retailers can use cross-category information to better implement micro marketing programs. We demonstrate the transfer of information across categories in an application of two grocery products--Breakfast Foods and Table Syrup. In spite of a reasonable correlation (0.21) in the price parameter across these two categories, our simulation guidelines predict very little benefit of cross-category analysis over single-category analysis. Our empirical results confirm this prediction.
Iyengar, Raghuram, Asim Ansari, and Sunil Gupta. "Leveraging Information Across Categories." Quantitative Marketing and Economics 1, no. 4 (December 2003): 425-65.
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