IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Login to save this article or follow this journal

How to Compute Optimal Catalog Mailing Decisions

  • Füsun F. Gönül

    ()

    (401 E. Waldheim Road, Pittsburgh, Pennsylvania 15215-1936)

  • Frenkel Ter Hofstede

    ()

    (Department of Marketing, McCombs School of Business, University of Texas at Austin, CBA 7.234/B6700, Austin, Texas 78712)

Registered author(s):

    We develop, estimate, and test a response model of order timing and order volume decisions of catalog customers and derive a Bayes rule for optimal mailing strategies. The model integrates the and components of the response; incorporates the of the firm; and uses a Bayesian framework to determine the optimal mailing rule for each catalog customer. The we propose for optimal mailing strategy allows for a broad set of objectives to be realized across the time horizon, such as profit maximization, customer retention, and utility maximization with or without risk aversion. We find that optimizing the objective function over multiple periods as opposed to a single period leads to higher expected profits and expected utility. Our results indicate that the cataloguer is well advised to send fewer catalogs than its current practice in order to maximize expected profits and utility.

    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.1050.0136
    Download Restriction: no

    Article provided by INFORMS in its journal Marketing Science.

    Volume (Year): 25 (2006)
    Issue (Month): 1 (01-02)
    Pages: 65-74

    as
    in new window

    Handle: RePEc:inm:ormksc:v:25:y:2006:i:1:p:65-74
    Contact details of provider: Postal:
    7240 Parkway Drive, Suite 300, Hanover, MD 21076 USA

    Phone: +1-443-757-3500
    Fax: 443-757-3515
    Web page: http://www.informs.org/
    Email:


    More information through EDIRC

    References listed on IDEAS
    Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

    as in new window
    1. Eric T. Anderson & Duncan I. Simester, 2004. "Long-Run Effects of Promotion Depth on New Versus Established Customers: Three Field Studies," Marketing Science, INFORMS, vol. 23(1), pages 4-20, February.
    2. Jeongwen Chiang, 1991. "A Simultaneous Approach to the Whether, What and How Much to Buy Questions," Marketing Science, INFORMS, vol. 10(4), pages 297-315.
    3. Keeney,Ralph L. & Raiffa,Howard, 1993. "Decisions with Multiple Objectives," Cambridge Books, Cambridge University Press, number 9780521438834.
    4. Füsun Gönül & Meng Ze Shi, 1998. "Optimal Mailing of Catalogs: A New Methodology Using Estimable Structural Dynamic Programming Models," Management Science, INFORMS, vol. 44(9), pages 1249-1262, September.
    5. David C. Schmittlein & Donald G. Morrison & Richard Colombo, 1987. "Counting Your Customers: Who-Are They and What Will They Do Next?," Management Science, INFORMS, vol. 33(1), pages 1-24, January.
    6. Kiefer, Nicholas M, 1988. "Economic Duration Data and Hazard Functions," Journal of Economic Literature, American Economic Association, vol. 26(2), pages 646-79, June.
    7. Füsun Gönül & Kannan Srinivasan, 1996. "Estimating the Impact of Consumer Expectations of Coupons on Purchase Behavior: A Dynamic Structural Model," Marketing Science, INFORMS, vol. 15(3), pages 262-279.
    8. Peter M. Guadagni & John D. C. Little, 1983. "A Logit Model of Brand Choice Calibrated on Scanner Data," Marketing Science, INFORMS, vol. 2(3), pages 203-238.
    9. David C. Schmittlein & Robert A. Peterson, 1994. "Customer Base Analysis: An Industrial Purchase Process Application," Marketing Science, INFORMS, vol. 13(1), pages 41-67.
    10. Jie Zhang & Lakshman Krishnamurthi, 2004. "Customizing Promotions in Online Stores," Marketing Science, INFORMS, vol. 23(4), pages 561-578, June.
    11. ter Hofstede, Frenkel & Wedel, Michel, 1998. "A Monte Carlo study of time aggregation in continuous-time and discrete-time parametric hazard models," Economics Letters, Elsevier, vol. 58(2), pages 149-156, February.
    12. Jan Roelf Bult & Tom Wansbeek, 1995. "Optimal Selection for Direct Mail," Marketing Science, INFORMS, vol. 14(4), pages 378-394.
    Full references (including those not matched with items on IDEAS)

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

    When requesting a correction, please mention this item's handle: RePEc:inm:ormksc:v:25:y:2006:i:1:p:65-74. 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.

    This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.