Recovering SKU-level Preferences and Response Sensitivities from Market Share Models Estimated on Item Aggregates

Abstract

We propose an alternative approach to obtaining SKU-level preferences and response sensitivities. An attribute-level model in which the unit of analysis is the market share for an alternative created by aggregation e.g., Colgate toothpaste) is distinguished from a truly disaggregate SKU-level model and develop an analytical relationship between parameters obtained from these two models is established. We show that the researcher can recover SKU-level parameters via calculation from estimated attribute-level parameters, circumventing the need for direct estimation of the more complex true SKU-level model. Our market share model is calibrated using 98 weeks of data for 10 brands and 168 SKUs in the toothpaste category. We show that instead of estimating 168 preference parameters (when we have an outside alternative in addition to the 168 inside ones), one need only estimate 10 brand preference parameters from which the 168 parameters can be computed as long as share and marketing mix data are available at the SKU level. Marketing mix response parameters can be recovered in a similar fashion. Estimation on holdout samples demonstrates superior predictive performance compared with other available methods. Implications for the derivation of SKU-level elasticities and forecasts of market share and response sensitivity for new items introduced to the category are discussed.