Quantifying the Benefits of Individual-Level Targeting in the Presence of Firm Strategic Behavior

Journal of Marketing Research (JMR)
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Executive Summary
Targeting (i.e., setting marketing policy differentially for different customers or segments) is an important marketing practice. Previous literature has documented positive returns to targeting in various marketing domains. In many industries (e.g., business-to-business markets), firms already have targeting strategies in place and determine their levels of marketing actions using some knowledge about their customers’ responses and competitors’ actions (systematic or otherwise). As numerous studies have pointed out, if the data reflect such strategic behavior, ignoring the endogeneity of marketing actions will lead to incorrect estimates of response parameters and, consequently, to biased inferences regarding the benefits from targeting. In this article, the authors develop a method to quantify the benefits of targeting when the data reflect firm strategic behavior—that is, when firms (1) are already engaged in some form of targeting and (2) take into account actions of competing firms. This study focuses on detailing, the most important marketing instrument in the pharmaceutical industry. The pharmaceutical firm’s key decision is the allocation of detailing visits across individual physicians. As firms already use the information on how detailing affects individual physician behavior in setting their detailing allocations, the proposed approach is appropriate in this context. For this analysis, the authors develop, at the individual physician level, a model of prescriptions as a function of detailing and a model of detailing under the assumption that firms simultaneously maximize profits from a physician. They estimate the model using a novel physician panel data set from the proton pump inhibitor category. They estimate the model parameters jointly using full-information Bayesian methods to obtain efficient estimates of the parameters of both models at the individual physician level. The results suggest that accounting for firm strategic behavior improves profitability by 14%–23% relative to segment-level targeting. In addition, ignoring firm strategic behavior underestimates the benefit of individual-level targeting significantly. The authors provide reasons for this finding. They also carry out several robustness checks to test the validity of the modeling assumptions.

This article contributes to the literature along the following dimensions. First, it is the first empirical study to consider competitive responses in evaluating targeting schemes. Second, the theoretical literature on targeting of advertising has found that the ability to target advertising increases the equilibrium profits of firms. The results not only provide empirical support for these theoretical findings but also quantify the increases in such profits (using data from the pharmaceutical industry). Third, this study extends and complements the current literature on the analysis of consumer responses while incorporating firm strategic behavior. In this regard, the study can be viewed as an early attempt to estimate a system of demand and firm behavior at the micro level to address an issue (i.e., targeting) that is relevant for that level of aggregation.

Xiaojing Dong is Assistant Professor of Marketing in the Leavey School of Business at Santa Clara University. She received her PhD from Northwestern University in 2006. Her dissertation received the Alden G. Clayton Award given by the Marketing Science Institute. Her research interests are in the areas of individual-level modeling, targeting, and empirical industrial organization, especially in pharmaceutical marketing and retailing. Methodologically, she is interested in empirical analysis of marketing problems using Bayesian econometrics.

Puneet Manchanda is Associate Professor of Marketing at the University of Michigan’s Ross School of Business. He received his MPhil and PhD in Marketing from Columbia University. His research interests are in models of social interactions, micromarketing and targeting, advertising, new media, multicategory choice, and learning models. He uses data from various domains, such as pharmaceuticals, packaged goods, high technology, gaming, insurance, network marketing, and Internet marketing in his research. His methodological interests are empirical industrial organization methods and Bayesian econometrics.

Pradeep K. Chintagunta is Robert Law Professor of Marketing in the Graduate School of Business at the University of Chicago. He is interested in studying strategic interactions among firms in vertical and horizontal relationships. His research also includes measuring the effectiveness of marketing activities in pharmaceutical markets, investigating aspects of technology product markets, and analyzing household purchase behavior.

J Marketing Research, Volume 46, Number 2, April 2009
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