Advanced Search
MyIDEAS: Login to save this article or follow this journal

Dynamic Allocation of Pharmaceutical Detailing and Sampling for Long-Term Profitability

Contents:

Author Info

  • Ricardo Montoya

    ()
    (Industrial Engineering Department, University of Chile, Santiago, Chile)

  • Oded Netzer

    ()
    (Marketing Division, Columbia Business School, Columbia University, New York, New York 10027)

  • Kamel Jedidi

    ()
    (Marketing Division, Columbia Business School, Columbia University, New York, New York 10027)

Abstract

The U.S. pharmaceutical industry spent upwards of $18 billion on marketing drugs in 2005; detailing and drug sampling activities accounted for the bulk of this spending. To stay competitive, pharmaceutical managers need to maximize the return on these marketing investments by determining which physicians to target as well as when and how to target them. In this paper, we present a two-stage approach for dynamically allocating detailing and sampling activities across physicians to maximize long-run profitability. In the first stage, we estimate a hierarchical Bayesian, nonhomogeneous hidden Markov model to assess the short- and long-term effects of pharmaceutical marketing activities. The model captures physicians' heterogeneity and dynamics in prescription behavior. In the second stage, we formulate a partially observable Markov decision process that integrates over the posterior distribution of the hidden Markov model parameters to derive a dynamic marketing resource allocation policy across physicians. We apply the proposed approach in the context of a new drug introduction by a major pharmaceutical firm. We identify three prescription-behavior states, a high degree of physicians' dynamics, and substantial long-term effects for detailing and sampling. We find that detailing is most effective as an acquisition tool, whereas sampling is most effective as a retention tool. The optimization results suggest that the firm could increase its profits substantially while decreasing its marketing spending. Our suggested framework provides important implications for dynamically managing customers and maximizing long-run profitability.

Download Info

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
File URL: http://dx.doi.org/10.1287/mksc.1100.0570
Download Restriction: no

Bibliographic Info

Article provided by INFORMS in its journal Marketing Science.

Volume (Year): 29 (2010)
Issue (Month): 5 (09-10)
Pages: 909-924

as in new window
Handle: RePEc:inm:ormksc:v:29:y:2010:i:5:p:909-924

Contact details of provider:
Postal: 7240 Parkway Drive, Suite 300, Hanover, MD 21076 USA
Phone: +1-443-757-3500
Fax: 443-757-3515
Email:
Web page: http://www.informs.org/
More information through EDIRC

Related research

Keywords: pharmaceutical marketing; marketing resource allocation; long-term effect of marketing activities; hidden Markov model; Bayesian estimation; dynamic programming;

References

No references listed on IDEAS
You can help add them by filling out this form.

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as in new window

Cited by:
  1. Asim Ansari & Ricardo Montoya & Oded Netzer, 2012. "Dynamic learning in behavioral games: A hidden Markov mixture of experts approach," Quantitative Marketing and Economics, Springer, vol. 10(4), pages 475-503, December.

Lists

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

Statistics

Access and download statistics

Corrections

When requesting a correction, please mention this item's handle: RePEc:inm:ormksc:v:29:y:2010:i:5:p:909-924. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Mirko Janc).

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.