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Using Predictive Modeling to Improve Direct Marketing Performance

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  • Todor Krastevich

Abstract

Customer acquisition, retention, churns and winback are not new marketing paradigms. Its implementation in terms of FMCG markets and low brand switching barriers is still challenging. In many economic sectors recording, storage and use of marketing data bases with records identifying market players and their behavior is an essential and integral part of business operations. This study attempts to provide a comparative analysis of classification models and predictive techniques for extracting knowledge from customer databases and opportunities for planning direct marketing campaigns, in particular, by selecting the "optimal" list of target customers based on direct marketing response models.

Suggested Citation

  • Todor Krastevich, 2013. "Using Predictive Modeling to Improve Direct Marketing Performance," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 3, pages 25-55.
    • Handle: RePEc:bas:econst:y:2013:i:3:p:25-55
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    References listed on IDEAS

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    1. Van den Poel, Dirk & Buckinx, Wouter, 2005. "Predicting online-purchasing behaviour," European Journal of Operational Research, Elsevier, vol. 166(2), pages 557-575, October.
    2. van Wezel, M.C. & Potharst, R., 2005. "Improved customer choice predictions using ensemble methods," Econometric Institute Research Papers EI 2005-08, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    3. Jonker, J.-J. & Franses, Ph.H.B.F. & Piersma, N., 2002. "Evaluating Direct Marketing Campaigns: recent findings and future research topics," ERIM Report Series Research in Management ERS-2002-26-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    4. G. V. Kass, 1980. "An Exploratory Technique for Investigating Large Quantities of Categorical Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(2), pages 119-127, June.
    5. 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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    More about this item

    JEL classification:

    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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