A Bayesian Nonparametric Approach to Residual Brand Equity Estimation

Product offerings in many consumer packaged goods (CPG) categories come in a variety of complex branding structures built around some discernable branding hierarchy. We develop a nonparametric statistical method in the context of a market response model to estimate the residual equity of each hierarchical level in a typical CPG branding structure, consistent with certain economic restrictions on the equity values. Our proposed model uses readily accessible aggregate sales and product data and exploits structure inherent in the set of brand and product relations to estimate its effects on market response. We propose that established brands in mature categories must be value-enhancing and that this translates into bounds on the domain of possible brand equity values. Our model, based on a set of independent Dirichlet process priors, avoids the drawbacks inherent in alternative approaches such as fixed effects, parametric random effects and finite mixtures of continuous densities. We examine the value contribution at different levels of the branding structure and derive insights therein. We demonstrate a brand valuation procedure using a dollar metric transformation of the residual equity estimates obtained. Finally, we validate our brand valuation results with those from independent, external sources. We test our model using AC Nielsen data on aggregate beer sales in US grocery stores. We find substantial heterogeneity in residual equity at different hierarchical levels in the branding structure, substantial differences between residual equity and more aggregate notions of brand equity and external validation of our residual equity estimates in terms of agreement with intuition, theory and previous financial data based brand equity valuations