Response Modeling with Non-Random Marketing Mix Variables

46 Pages Posted: 31 Mar 2003

See all articles by Puneet Manchanda

Puneet Manchanda

University of Michigan, Stephen M. Ross School of Business

Peter E. Rossi

University of California, Los Angeles (UCLA) - Anderson School of Management

Pradeep K. Chintagunta

University of Chicago

Date Written: January 2003

Abstract

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.

Keywords: Response Models, Salesforce Effectiveness, Micromarketing, Pharmaceutical Industry, Hierarchical Bayes Models, Metropolis-Hastings Algorithm, Gibbs Sampler, Markov Chain Monte Carlo Methods

JEL Classification: C0, C3

Suggested Citation

Manchanda, Puneet and Rossi, Peter E. and Chintagunta, Pradeep K., Response Modeling with Non-Random Marketing Mix Variables (January 2003). Available at SSRN: https://ssrn.com/abstract=371360 or http://dx.doi.org/10.2139/ssrn.371360

Puneet Manchanda

University of Michigan, Stephen M. Ross School of Business ( email )

701 Tappan Street
Ann Arbor, MI MI 48109
United States
734-936-2445 (Phone)
734-936-8716 (Fax)

Peter E. Rossi (Contact Author)

University of California, Los Angeles (UCLA) - Anderson School of Management ( email )

110 Westwood Plaza
Los Angeles, CA 90095-1481
United States
773-294-8616 (Phone)

Pradeep K. Chintagunta

University of Chicago ( email )

5807 S. Woodlawn Avenue
Chicago, IL 60637
United States
773-702-8015 (Phone)
773-702-0458 (Fax)

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