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How to Compute Optimal Catalog Mailing Decisions

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  • 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)

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    Abstract

    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.

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    File URL: http://dx.doi.org/10.1287/mksc.1050.0136
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    Bibliographic Info

    Article provided by INFORMS in its journal Marketing Science.

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

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    Handle: RePEc:inm:ormksc:v:25:y:2006:i:1:p:65-74

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    Related research

    Keywords: catalog mailing; database marketing; econometric models; hierarchical Bayes;

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    Cited by:
    1. Lemieux, James & Peterson, Robert A., 2011. "Purchase deadline as a moderator of the effects of price uncertainty on search duration," Journal of Economic Psychology, Elsevier, vol. 32(1), pages 33-44, February.
    2. Durango-Cohen, Elizabeth J., 2013. "Modeling contribution behavior in fundraising: Segmentation analysis for a public broadcasting station," European Journal of Operational Research, Elsevier, vol. 227(3), pages 538-551.
    3. Mercedes Esteban Bravo & José M. Vidal-Sanz & Gökhan Yildirim, 2012. "Valuing customer portfolios with endogenous mass-and-direct-marketing interventions using a stochastic dynamic programming decomposition," Business Economics Working Papers wb121304, Universidad Carlos III, Departamento de Economía de la Empresa.

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