If you need an accessible version of this item please contact JSTOR User Support

Response Modeling with Nonrandom Marketing-Mix Variables

Puneet Manchanda, Peter E. Rossi and Pradeep K. Chintagunta
Journal of Marketing Research
Vol. 41, No. 4 (Nov., 2004), pp. 467-478
Stable URL: http://www.jstor.org/stable/30164711
Page Count: 12
  • Download PDF
  • Cite this Item

You are not currently logged in.

Access your personal account or get JSTOR access through your library or other institution:

login

Log in to your personal account or through your institution.

If you need an accessible version of this item please contact JSTOR User Support
Response Modeling with Nonrandom Marketing-Mix Variables
Preview not available

Abstract

Sales response models are widely used as the basis for optimizing the marketing mix. Response models condition on the observed marketing-mix variables and focus on the specification of the distribution of observed sales given marketing-mix activities. The models usually 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. The authors expand on the standard conditional model to include a model for the determination of the marketing-mix variables. They apply this modeling approach to the problem of gauging the effectiveness of sales calls (details) to induce greater prescribing of drugs by individual physicians. They do not assume a priori that details are set optimally, but instead they infer the extent to which sales force managers have knowledge of responsiveness, and they use this knowledge to set the level of sales force contact. The authors find that their modeling approach improves the precision of the physician-specific response parameters significantly. They also 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. It appears that unresponsive but high-volume physicians are detailed the most. Finally, the authors illustrate how their approach provides a general framework.

Page Thumbnails

  • Thumbnail: Page 
467
    467
  • Thumbnail: Page 
468
    468
  • Thumbnail: Page 
469
    469
  • Thumbnail: Page 
470
    470
  • Thumbnail: Page 
471
    471
  • Thumbnail: Page 
472
    472
  • Thumbnail: Page 
473
    473
  • Thumbnail: Page 
474
    474
  • Thumbnail: Page 
475
    475
  • Thumbnail: Page 
476
    476
  • Thumbnail: Page 
477
    477
  • Thumbnail: Page 
478
    478