Recovery of Price-Cost Margins from Store-level Data: Estimation and Validation using generalized linear models

By Sudhir Voleti
Quantitative Marketing and Economics

Citation

Voleti, Sudhir. Recovery of Price-Cost Margins from Store-level Data: Estimation and Validation using generalized linear models Quantitative Marketing and Economics .

Quantitative Marketing and Economics

Abstract

The paper considers the well-known problem of estimating price-cost margins (PCMs) from aggregate sales data. As part of the solution approach, it addresses two related empirical questions, namely, (i) Which general endogeneity-correction approach (instrumental variables versus modeling the price setting process in the supply side) better recovers PCMs. (ii) Which general estimation procedure among fully parametric, Bayesian semi-parametric and fixed non-parametric better recovers PCMs. Together, the analysis yields six distinct modeling specifications for empirical evaluation. These six specifications are implemented on store-level sales data in the beer category, separately for low, middle and high price-tier stores, from the Dominick's Finer Foods database. Data on upstream (or wholesale) prices are used to validate PCM recovery across the six specifications for each store type. The results show that modeling the price setting process or applying a Bayesian semiparametric estimation method individually outperform alternative approaches in PCM recovery. Their combination seems most conducive (relative to alternatives evaluated) for estimating PCMs in empirical datasets on CPG grocery categories.

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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Sudhir Voleti