IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v3y1984i2p169-178.html
   My bibliography  Save this article

Technical Note—A Pure Birth Model of Optimal Advertising with Word-of-Mouth

Author

Listed:
  • George E. Monahan

    (Washington University in St. Louis)

Abstract

A stochastic, dynamic model of advertising, which incorporates both advertising and word-of-mouth effects, is formulated. The time between the acquisition of new customers is assumed to be random. The distribution of the time until the firm obtains a new customer depends upon the rate of advertising expenditures and upon a word-of-mouth parameter. The problem of choosing the rate of advertising expenditures so as to maximize long-run expected profit is formulated as a continuous-time Markov decision chain. The impact of changes in various parameters of the model on optimal advertising decisions is studied.

Suggested Citation

  • George E. Monahan, 1984. "Technical Note—A Pure Birth Model of Optimal Advertising with Word-of-Mouth," Marketing Science, INFORMS, vol. 3(2), pages 169-178.
  • Handle: RePEc:inm:ormksc:v:3:y:1984:i:2:p:169-178
    DOI: 10.1287/mksc.3.2.169
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mksc.3.2.169
    Download Restriction: no

    File URL: https://libkey.io/10.1287/mksc.3.2.169?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

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


    Cited by:

    1. Kamrad, Bardia & Lele, Shreevardhan S. & Siddique, Akhtar & Thomas, Robert J., 2005. "Innovation diffusion uncertainty, advertising and pricing policies," European Journal of Operational Research, Elsevier, vol. 164(3), pages 829-850, August.
    2. Fruchter, Gila E. & Van den Bulte, Christophe, 2011. "Why the Generalized Bass Model leads to odd optimal advertising policies," International Journal of Research in Marketing, Elsevier, vol. 28(3), pages 218-230.
    3. Mariusz Górajski & Dominika Machowska, 2017. "Optimal double control problem for a PDE model of goodwill dynamics," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 85(3), pages 425-452, June.
    4. Dominika Bogusz & Mariusz Gorajski, 2014. "Optimal Goodwill Model with Consumer Recommendations and Market Segmentation," Lodz Economics Working Papers 1/2014, University of Lodz, Faculty of Economics and Sociology, revised Oct 2014.
    5. Eryn Juan He & Joel Goh, 2022. "Profit or Growth? Dynamic Order Allocation in a Hybrid Workforce," Management Science, INFORMS, vol. 68(8), pages 5891-5906, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ormksc:v:3:y:1984:i:2:p:169-178. See general information about how to correct material in RePEc.

    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.

    We have no bibliographic references for this item. You can help adding them by using 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 RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.