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Investigating the effects of mailing variables and endogeneity on mailing decisions


  • Schröder, Nadine
  • Hruschka, Harald


Determining the optimal amount of mailings being sent to customers is crucial. However, this decision depends on various aspects. First, it is important to specify relevant mailing variables. By distinguishing different types of mailings and considering their sizes, we set our study apart from the majority of existing studies. To deal with unobserved heterogeneity we estimate a Mixture of Dirichlet Processes (MDP) whose components are Tobit-2 models. A policy function approach is used to take endogeneity into account. We investigate whether and how consideration of endogeneity leads to different managerial implications. To this end, we determine mailings by dynamic optimization for three individual customers which are prototypical for the segments discovered by the MDP model. We find out that mailings should be avoided altogether more frequently for all three customer types according to the model which takes endogeneity into account.

Suggested Citation

  • Schröder, Nadine & Hruschka, Harald, 2016. "Investigating the effects of mailing variables and endogeneity on mailing decisions," European Journal of Operational Research, Elsevier, vol. 250(2), pages 579-589.
  • Handle: RePEc:eee:ejores:v:250:y:2016:i:2:p:579-589
    DOI: 10.1016/j.ejor.2015.09.046

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    References listed on IDEAS

    1. Baumgartner, Bernhard & Hruschka, Harald, 2005. "Allocation of catalogs to collective customers based on semiparametric response models," European Journal of Operational Research, Elsevier, vol. 162(3), pages 839-849, May.
    2. McCulloch, Robert E. & Polson, Nicholas G. & Rossi, Peter E., 2000. "A Bayesian analysis of the multinomial probit model with fully identified parameters," Journal of Econometrics, Elsevier, vol. 99(1), pages 173-193, November.
    3. Jacoby, Jacob & Speller, Donald E & Berning, Carol A Kohn, 1974. " Brand Choice Behavior as a Function of Information Load: Replication and Extension," Journal of Consumer Research, Oxford University Press, vol. 1(1), pages 33-42, June.
    4. 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.
    5. Hruschka, Harald, 2010. "Considering endogeneity for optimal catalog allocation in direct marketing," European Journal of Operational Research, Elsevier, vol. 206(1), pages 239-247, October.
    6. Bose, Indranil & Chen, Xi, 2009. "Quantitative models for direct marketing: A review from systems perspective," European Journal of Operational Research, Elsevier, vol. 195(1), pages 1-16, May.
    7. Burda, Martin & Harding, Matthew & Hausman, Jerry, 2008. "A Bayesian mixed logit-probit model for multinomial choice," Journal of Econometrics, Elsevier, vol. 147(2), pages 232-246, December.
    8. Pradeep Chintagunta & Tülin Erdem & Peter E. Rossi & Michel Wedel, 2006. "Structural Modeling in Marketing: Review and Assessment," Marketing Science, INFORMS, vol. 25(6), pages 604-616, 11-12.
    9. 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.
    10. Chun, Young H., 2012. "Monte Carlo analysis of estimation methods for the prediction of customer response patterns in direct marketing," European Journal of Operational Research, Elsevier, vol. 217(3), pages 673-678.
    11. Naik, P. & Piersma, N., 2002. "Understanding the role of marketing communications in direct marketing," Econometric Institute Research Papers EI 2002-13, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
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