Quantifying the Benefits of Individual Level Targeting in the Presence of Firm Strategic Behavior
Santa Clara University - Marketing
University of Michigan, Stephen M. Ross School of Business
Pradeep K. Chintagunta
University of Chicago
Targeting - setting marketing policy differentially for different customers or segments - is an important marketing practice. Previous approaches to quantifying the benefits from targeting have typically calibrated a response model and used the variation in response parameter estimates to compare the firm's profits under targeting schemes at different levels of aggregation. Implicit in this approach is the assumption that the data do not reflect any strategic behavior that the firm may be engaged in vis-à-vis the marketing variables being used for targeting. In this paper, we develop a method to quantify the benefits of targeting when the data reflect firm strategic behavior, i.e., when firms are i) already engaged in some form of targeting; and ii) taking into account actions of competing firms. In particular, we are interested in quantifying the improvement in profits to a firm from targeting its activities at the individual customer level as compared to the allocation of marketing resources at a more aggregate level (e.g., segment or market level).
We focus 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 allocation, our proposed approach is appropriate in this context. For our analysis, we 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. We estimate our model on a novel physician panel dataset from the Proton Pump Inhibitor category. Estimation of the model parameters is carried out jointly using full-information Bayesian methods to obtain efficient estimates of the parameters of both models at the individual physician level. Our results suggest that accounting for firm strategic behavior improves profitability by 23% relative to segment level targeting. In addition, ignoring firm strategic behavior underestimates the benefit of individual level targeting significantly. We provide reasons for this finding. We also carry out several robustness checks to test the validity of modeling assumptions.
Number of Pages in PDF File: 50
Keywords: Targeted Marketing, Response Models, Firm Strategic Behavior, Pharmaceutical Industry, Detailing, MCMC Methods
JEL Classification: M30, M31, C33, M37, L65, L13, C11, C15
Date posted: October 17, 2006
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