Response Modeling with Non-random Marketing Mix Variables
University of Michigan - Ross School of Business
Peter E. Rossi
UCLA-Anderson School of Management
Pradeep K. Chintagunta
University of Chicago
Sales response models are widely used as the basis for optimizing the marketing mix or for allocation of the sales force. Response models condition on the observed marketing mix variables and focus on the specification of the distribution of observed sales given marketing mix activities. These models fail to recognize that the levels of the marketing mix variables are often chosen with at least partial knowledge of the response parameters in the conditional model. This means that, contrary to standard assumptions, the marginal distribution of the marketing mix variables is not independent of response parameters. We expand on the standard conditional model to include a model for the determination of the marketing mix variables. We apply this modeling approach to the problem of gauging the effectiveness of sales calls (details) to induce greater prescribing of drugs by individual physicians. We do not assume, a priori, that details are set optimally but, instead, infer the extent to which sales force managers have knowledge of responsiveness and use this knowledge to set the level of sales force contact. We find that physicians are not detailed optimally; high volume physicians are detailed to a greater extent than low volume physicians without regard to responsiveness to detailing. In fact, it appears that unresponsive but high volume physicians are detailed the most.
Number of Pages in PDF File: 46
Keywords: Response Models, Salesforce Effectiveness, Micromarketing, Pharmaceutical Industry, Hierarchical Bayes Models, Metropolis-Hastings Algorithm, Gibbs Sampler, Markov Chain Monte Carlo Methods
JEL Classification: C0, C3working papers series
Date posted: March 31, 2003
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