A non-parametric model of residual brand equity in hierarchical branding structures with application to US beer data

By Sudhir Voleti, Pulak Ghosh
Journal of the Royal Statistical Society, Series A | January 2014

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http://as.wiley.com/WileyCDA/WileyTitle/productCd-RSSA.html

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Voleti, Sudhir., Ghosh, Pulak. (2012). A non-parametric model of residual brand equity in hierarchical branding structures with application to US beer data Journal of the Royal Statistical Society, Series A http://as.wiley.com/WileyCDA/WileyTitle/productCd-RSSA.html.

Journal of the Royal Statistical Society, Series A, 2012

Abstract

Product offerings in many grocery product categories in supermarkets display varied branding structures built around a discernable branding hierarchy typically comprising brands, sub-brands and stock-keeping units (or SKUs). Firms often want to know (a) what contribution each layer in the brand hierarchy brings to overall product value, and (b) precisely how much of this contribution comes from unique branding associations (we term this value contribution the ‘residual equity’ of that branding layer). We make the economic argument that in mature product categories, profit maximizing firms would retain the upper levels of the branding structure only if they were value-enhancing. We develop a Semiparametric Bayesian method for a market response model to jointly estimate the residual equity of each layer in the branding structure while accommodating certain a priori restrictions on the equity values, and using only aggregate sales and product data. Our proposed model is simple yet flexible and avoids common drawbacks in extant approaches. We implement our model on AC Nielsen beer category data from US supermarkets. We find that residual equity exists, is sizeable in magnitude and sales impact, is heterogeneous in occurrence across the branding structure, yields realistic brand valuations and bears managerially relevant insights and implications. Keywords: Brand Equity, Brand Valuation, Dirichlet process priors, Nonparametric Bayesian Statistics.

Sudhir Voleti is an Associate Professor of Marketing at the Indian School of Business (ISB), where he is also a distinguished faculty member in Business Analytics. A renowned researcher in the fields of marketing research and business analytics, he has previously served as Associate Dean of Faculty Alignment and the Registrar's Office (FARO) at ISB.

Professor Voleti holds a PhD in Marketing and an MS in Applied Statistics from the University of Rochester, a PGDM from Indian Institute of Management (IIM) Calcutta, and a BE from the Birla Institute of Technology, Ranchi, along with years of industry experience.

Professor Voleti is recognised as one of India's leading data science academicians. His research focuses on combining data with econometric and statistical methods to explain phenomena of marketing interest such as evolution in the equity of brands across time, valuation of brands using secondary sales data, the sales impact of geographic and abstract distances between products and markets, and the performance, productivity, and benchmarking of salesforce organisations.

Professor Voleti has published numerous research articles in leading academic journals such as Management Science, Journal of Marketing, Journal of the Royal Statistical Society, the International Journal of Research in Marketing, and the Journal of Retailing, as well as book chapters and articles in the popular media. He also serves on the editorial review boards of numerous journals. Some of his significant works include "Impact of Reference Prices on Product Positioning and Profits", "The role of big data and predictive analytics in retailing", "Why the Dynamics of Competition Matter for Category Profitability", "A Bayesian non-parametric model of residual brand equity in hierarchical branding structures", and "An Approach to Improve the Predictive power of Choice - Based Conjoint Analysis".

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