IDEAS home Printed from https://ideas.repec.org/p/rug/rugwps/05-298.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    File URL: http://wps-feb.ugent.be/Papers/wp_05_298.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Buckinx, Wouter & Van den Poel, Dirk, 2005. "Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting," European Journal of Operational Research, Elsevier, vol. 164(1), pages 252-268, July.
    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. 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.
    5. B Baesens & T Van Gestel & S Viaene & M Stepanova & J Suykens & J Vanthienen, 2003. "Benchmarking state-of-the-art classification algorithms for credit scoring," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(6), pages 627-635, June.
    6. 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.
    7. D. Van den Poel, 2003. "Predicting Mail-Order Repeat Buying. Which Variables Matter?," Review of Business and Economic Literature, KU Leuven, Faculty of Economics and Business (FEB), Review of Business and Economic Literature, vol. 0(3), pages 371-404.
    8. Jan Roelf Bult & Tom Wansbeek, 1995. "Optimal Selection for Direct Mail," Marketing Science, INFORMS, vol. 14(4), pages 378-394.
    9. 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.
    10. Ralf Elsner & Manfred Krafft & Arnd Huchzermeier, 2003. "Optimizing Rhenania's Mail-Order Business Through Dynamic Multilevel Modeling (DMLM)," Interfaces, INFORMS, vol. 33(1), pages 50-66, February.
    11. 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.
    12. 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.
    13. Siddhartha Bhattacharyya, 1999. "Direct Marketing Performance Modeling Using Genetic Algorithms," INFORMS Journal on Computing, INFORMS, vol. 11(3), pages 248-257, August.
    14. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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. De Cannière, Marie Hélène & De Pelsmacker, Patrick & Geuens, Maggie, 2009. "Relationship Quality and the Theory of Planned Behavior models of behavioral intentions and purchase behavior," Journal of Business Research, Elsevier, vol. 62(1), pages 82-92, January.
    3. Durango-Cohen, Elizabeth J., 2013. "Modeling contribution behavior in fundraising: Segmentation analysis for a public broadcasting station," European Journal of Operational Research, Elsevier, vol. 227(3), pages 538-551.
    4. Duncan I. Simester & Peng Sun & John N. Tsitsiklis, 2006. "Dynamic Catalog Mailing Policies," Management Science, INFORMS, vol. 52(5), pages 683-696, May.
    5. Roland T. Rust & Tuck Siong Chung, 2006. "Marketing Models of Service and Relationships," Marketing Science, INFORMS, vol. 25(6), pages 560-580, 11-12.
    6. Andrés Musalem & Yogesh V. Joshi, 2009. "—How Much Should You Invest in Each Customer Relationship? A Competitive Strategic Approach," Marketing Science, INFORMS, vol. 28(3), pages 555-565, 05-06.
    7. Verhoef, Peter C. & Venkatesan, Rajkumar & McAlister, Leigh & Malthouse, Edward C. & Krafft, Manfred & Ganesan, Shankar, 2010. "CRM in Data-Rich Multichannel Retailing Environments: A Review and Future Research Directions," Journal of Interactive Marketing, Elsevier, vol. 24(2), pages 121-137.
    8. Romana Khan & Michael Lewis & Vishal Singh, 2009. "Dynamic Customer Management and the Value of One-to-One Marketing," Marketing Science, INFORMS, vol. 28(6), pages 1063-1079, 11-12.
    9. Dimitris Bertsimas & Adam J. Mersereau, 2007. "A Learning Approach for Interactive Marketing to a Customer Segment," Operations Research, INFORMS, vol. 55(6), pages 1120-1135, December.
    10. Alan L. Montgomery, 2001. "Applying Quantitative Marketing Techniques to the Internet," Interfaces, INFORMS, vol. 31(2), pages 90-108, April.
    11. 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.
    12. Roland T. Rust & Peter C. Verhoef, 2005. "Optimizing the Marketing Interventions Mix in Intermediate-Term CRM," Marketing Science, INFORMS, vol. 24(3), pages 477-489, December.
    13. Bas Donkers & Richard Paap & Jedid‐Jah Jonker & Philip Hans Franses, 2006. "Deriving target selection rules from endogenously selected samples," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(5), pages 549-562, July.
    14. Mercedes Esteban-Bravo & Jose M. Vidal-Sanz & Gökhan Yildirim, 2014. "Valuing Customer Portfolios with Endogenous Mass and Direct Marketing Interventions Using a Stochastic Dynamic Programming Decomposition," Marketing Science, INFORMS, vol. 33(5), pages 621-640, September.
    15. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.
    16. 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.
    17. Füsun F. Gönül & Frenkel Ter Hofstede, 2006. "How to Compute Optimal Catalog Mailing Decisions," Marketing Science, INFORMS, vol. 25(1), pages 65-74, 01-02.
    18. Rocío G. Martínez & Ramon A. Carrasco & Cristina Sanchez-Figueroa & Diana Gavilan, 2021. "An RFM Model Customizable to Product Catalogues and Marketing Criteria Using Fuzzy Linguistic Models: Case Study of a Retail Business," Mathematics, MDPI, vol. 9(16), pages 1-31, August.
    19. André Bonfrer & Xavier Drèze, 2009. "Real-Time Evaluation of E-mail Campaign Performance," Marketing Science, INFORMS, vol. 28(2), pages 251-263, 03-04.
    20. 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.

    More about this item

    Keywords

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

    Lists

    This item is featured on the following reading lists, Wikipedia, or ReplicationWiki pages:
    1. Mercadotecnia de bases de datos in Wikipedia Spanish
    2. Database marketing in Wikipedia English

    Statistics

    Access and download statistics

    Corrections

    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:rug:rugwps:05/298. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Nathalie Verhaeghe (email available below). General contact details of provider: https://edirc.repec.org/data/ferugbe.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.