Investigating the drivers of consumer cross-category learning for new products using multiple data sets

Consumer new product adoption and preference evolution or learning may be influenced by intrinsic or internal factors (e.g., usage experiences, personal characteristics), external influences (e.g., social effects, media), and marketing activities of the firm. Moreover, the preference evolution in a certain category can spill over to other categories; i.e., consumers can exhibit cross-category learning. In this paper, we develop a multicategory framework to analyze the role of the above elements in the formation and evolution of consumer preferences across categories. We analyze these elements by employing multiple data sets, i.e., by combining revealed preference data (from scanner panel), stated data (from surveys measuring consumer lifestyle variables and demographics), and external influences (e.g., media mentions) in a completely heterogeneous framework while considering other facets of the learning process. By jointly estimating the model for organic purchases in six distinct food categories, we also explore the role of category differences. Results show that consumer new product adoption and learning is indeed impacted significantly and to various degrees by the aforementioned factors. We show how, by selectively encouraging purchases under various scenarios, firms can accelerate the learning process, not only for the focal category but also for other categories, thereby realizing considerable incremental profits. These results can be used by both manufacturers and retailers for more efficient allocation of marketing budgets across (new) products.