Estimating demand heterogeneity using aggregated data: An application to the frozen pizza category

This paper combines different aggregate-level data sets to identify new product demand in consumer packaged goods (CPG) categories. Our approach augments market-level time-series data with widely available summaries of household purchase behavior, i.e., brand penetration and purchase set size data. We show that this augmentation is helpful in the estimation of consumer heterogeneity. For instance, observing a brand with relatively large shares and low penetration typically indicates that preferences are dispersed, with relatively few customers liking the brand a lot. Whereas the combination of share and penetration is informative about heterogeneity with realistic sample sizes, in isolation neither variable may lead to precise estimates of heterogeneity. In addition, other widely available data, e.g., category penetration, is helpful in estimating the size of the total market. Using a large Monte Carlo study, the paper demonstrates the benefits of the proposed approach in estimating model parameters, price elasticities, and brand switching. Empirically, the approach is used to evaluate the launch of a new national brand, DiGiorno, in the frozen pizza category. The new brand is inferred to be very successful at expanding the category, while avoiding cannibalization of existing company share. Using only standard information, i.e., market shares, to estimate the demand model leads, in our data, to poor estimates of the degree of consumer taste variation and of switching to a new brand