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Constrained optimization of data-mining problems to improve model performance: A direct-marketing application

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  • A. PRINZIE

    ()

  • D. VAN DEN POEL

    ()

Abstract

Although most data mining (DM) models are complex and general in nature, the implementation of such models in specific environments us often subject to practical constraints (e.g. budget constraints) or thresholds (e.g. only mail to customers with an expected profit higher than the investment cost). Typically, the DM model is calibrated neglecting those constraints/thresholds. If the implementation constraints/thresholds are known in advance, this indirect approach delivers a sub-optimal model performance. Adopting a direct approach, i.e. estimating a DM model in knowledge of the constraints/thresholds, improves model performance as the model is optimized for the given implementation environment. We illustrate the relevance of this constrained optimization of DM models on a direct-marketing case, i.e., in the field of customer relationship management. We optimize an individual-level response model for specific mailing-depths (i.e. the percentage of customers of the house list that actually receives a mail given the mailing budget constraint) and compare its predictive performance with that of a traditional response model neglecting the mailing depth during estimation. The results are in favor of the constrained-optimization approach.

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File URL: http://www.feb.ugent.be/nl/Ondz/wp/Papers/wp_05_298.pdf
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Bibliographic Info

Paper provided by Ghent University, Faculty of Economics and Business Administration in its series Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium with number 05/298.

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Length: 36 pages
Date of creation: Mar 2005
Date of revision:
Handle: RePEc:rug:rugwps:05/298

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Related research

Keywords: constrained optimization; data mining; direct marketing; customer relationship management; targeting;

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References

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  1. 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.
  2. W. Buckinx & D. Van Den Poel, 2003. "Customer Base Analysis: Partial Defection of Behaviorally-Loyal Clients in a Non-Contractual FMCG Retail Setting," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 03/178, Ghent University, Faculty of Economics and Business Administration.
  3. Gabriel R. Bitran & Susana V. Mondschein, 1996. "Mailing Decisions in the Catalog Sales Industry," Management Science, INFORMS, vol. 42(9), pages 1364-1381, September.
  4. D. Van den Poel, 2003. "Predicting Mail-Order Repeat Buying. Which Variables Matter?," Review of Business and Economics, Katholieke Universiteit Leuven, Faculteit Economie en Bedrijfswetenschappen, vol. 0(3), pages 371-404.
  5. 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.
  6. Jan Roelf Bult & Tom Wansbeek, 1995. "Optimal Selection for Direct Mail," Marketing Science, INFORMS, vol. 14(4), pages 378-394.
  7. W. Buckinx & E. Moons & D. Van Den Poel & G. Wets, 2003. "Customer-Adapted Coupon Targeting Using Feature Selection," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 03/201, Ghent University, Faculty of Economics and Business Administration.
  8. Piersma, Nanda & Jonker, Jedid-Jah, 2004. "Determining the optimal direct mailing frequency," European Journal of Operational Research, Elsevier, vol. 158(1), pages 173-182, October.
  9. Vicki G. Morwitz & David C. Schmittlein, 1998. "Testing New Direct Marketing Offerings: The Interplay of Management Judgment and Statistical Models," Management Science, INFORMS, vol. 44(5), pages 610-628, May.
  10. Peter E. Rossi & Robert E. McCulloch & Greg M. Allenby, 1996. "The Value of Purchase History Data in Target Marketing," Marketing Science, INFORMS, vol. 15(4), pages 321-340.
  11. 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.
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Citations

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Cited by:
  1. A. Prinzie & D. Van Den Poel, 2007. "Predicting home-appliance acquisition sequences: Markov/Markov for Discrimination and survival analysis for modeling sequential information in NPTB models," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 07/442, Ghent University, Faculty of Economics and Business Administration.
  2. 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.

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