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

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Author Info
A. PRINZIE ()
D. VAN DEN POEL ()

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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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Publisher 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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This paper has been announced in the following NEP Reports: 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.:
  1. 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. [Downloadable!]
  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. [Downloadable!]
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  3. J.J. Jonker & N. Piersma & D. Van den Poel, 2002. "Joint optimization of customer segmentation and marketing policy to maximize long-term profitability," Econometric Institute Report 271, Erasmus University Rotterdam, Econometric Institute. [Downloadable!]
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  4. 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. [Downloadable!] (restricted)
  5. D. Van Den Poel, 2003. "Predicting Mail-Order Repeat Buying: Which Variables Matter?," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 03/191, Ghent University, Faculty of Economics and Business Administration. [Downloadable!]
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  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. [Downloadable!]
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