IDEAS home Printed from
   My bibliography  Save this paper

Joint optimization of customer segmentation and marketing policy to maximize long-term profitability


  • Jonker, J-J.
  • Piersma, N.
  • Van den Poel, D.


With the advent of one-to-one marketing media, e.g. targeted direct mail or internet marketing, the opportunities to develop targeted marketing campaigns are enhanced in such a way that it is now both organizationally and economically feasible to profitably support a substantially larger number of marketing segments. However, the problem of what segments to distinguish, and what actions to take towards the different segments increases substantially in such an environment. A systematic analytic procedure optimizing both steps would be very welcome.In this study, we present a joint optimization approach addressing two issues: (1) the segmentation of customers into homogeneous groups of customers, (2) determining the optimal policy (i.e., what action to take from a set of available actions) towards each segment. We implement this joint optimization framework in a direct-mail setting for a charitable organization. Many previous studies in this area highlighted the importance of the following variables: R(ecency), F(requency), and M(onetary value). We use these variables to segment customers. In a second step, we determine which marketing policy is optimal using markov decision processes, following similar previous applications. The attractiveness of this stochastic dynamic programming procedure is based on the long-run maximization of expected average profit. Our contribution lies in the combination of both steps into one optimization framework to obtain an optimal allocation of marketing expenditures. Moreover, we control segment stability and policy performance by a bootstrap procedure. Our framework is illustrated by a real-life application. The results show that the proposed model outperforms a CHAID segmentation.

Suggested Citation

  • Jonker, J-J. & Piersma, N. & Van den Poel, D., 2002. "Joint optimization of customer segmentation and marketing policy to maximize long-term profitability," Econometric Institute Research Papers EI 2002-18, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  • Handle: RePEc:ems:eureir:562

    Download full text from publisher

    File URL:
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    1. Baesens, Bart & Verstraeten, Geert & Van den Poel, Dirk & Egmont-Petersen, Michael & Van Kenhove, Patrick & Vanthienen, Jan, 2004. "Bayesian network classifiers for identifying the slope of the customer lifecycle of long-life customers," European Journal of Operational Research, Elsevier, vol. 156(2), pages 508-523, July.
    2. Gabriel R. Bitran & Susana V. Mondschein, 1996. "Mailing Decisions in the Catalog Sales Industry," Management Science, INFORMS, vol. 42(9), pages 1364-1381, September.
    3. Maurice W. Sasieni, 1989. "Optimal Advertising Strategies," Marketing Science, INFORMS, vol. 8(4), pages 358-370.
    4. Frank M. Bass & Jerry Wind, 1995. "Introduction to the Special Issue: Empirical Generalizations in Marketing," Marketing Science, INFORMS, vol. 14(3_supplem), pages 1-5.
    5. 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.
    6. P. V. (Sundar) Balakrishnan & Varghese S. Jacob, 1996. "Genetic Algorithms for Product Design," Management Science, INFORMS, vol. 42(8), pages 1105-1117, August.
    7. Richard Paap & Philip Hans Franses & Bas Donkers & Jedid-Jah Jonker, 2006. "Deriving target selection rules from endogenously selected samples," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(5), pages 549-562.
    8. Hruschka, Harald, 2002. "Market share analysis using semi-parametric attraction models," European Journal of Operational Research, Elsevier, vol. 138(1), pages 212-225, April.
    Full references (including those not matched with items on IDEAS)


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

    Cited by:

    1. A. Prinzie & D. Van Den Poel, 2005. "Constrained optimization of data-mining problems to improve model performance: A direct-marketing application," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/298, Ghent University, Faculty of Economics and Business Administration.
    2. Verhaert, Griet A. & Van den Poel, Dirk, 2011. "Empathy as added value in predicting donation behavior," Journal of Business Research, Elsevier, vol. 64(12), pages 1288-1295.
    3. G. A. Verhaert & D. Van Den Poel, 2012. "The Role of Seed Money and Threshold Size in Optimizing Fundraising Campaigns: Past Behavior Matters!," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/815, Ghent University, Faculty of Economics and Business Administration.


    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:ems:eureir:562. 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: (RePub). General contact details of provider: .

    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 CitEc recognized a reference but did not link an item in RePEc 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 RePEc Author Service 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.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.