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

Author

Listed:
  • 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.

Suggested Citation

  • 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.
    • Handle: RePEc:rug:rugwps:05/298
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    File URL: http://wps-feb.ugent.be/Papers/wp_05_298.pdf
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. 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.
    3. Cui, Geng & Wong, Man Leung & Wan, Xiang, 2015. "Targeting High Value Customers While Under Resource Constraint: Partial Order Constrained Optimization with Genetic Algorithm," Journal of Interactive Marketing, Elsevier, vol. 29(C), pages 27-37.

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    More about this item

    Keywords

    constrained optimization; data mining; direct marketing; customer relationship management; targeting;
    All these keywords.

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    1. Mercadotecnia de bases de datos in Wikipedia Spanish
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