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Bagging and boosting classification trees to predict churn

Author

Listed:
  • Lemmens, A.

    (Tilburg University, School of Economics and Management)

  • Croux, C.

    (Tilburg University, School of Economics and Management)

Abstract

No abstract is available for this item.

Suggested Citation

  • Lemmens, A. & Croux, C., 2006. "Bagging and boosting classification trees to predict churn," Other publications TiSEM d5cb664d-5859-44db-a621-e, Tilburg University, School of Economics and Management.
  • Handle: RePEc:tiu:tiutis:d5cb664d-5859-44db-a621-e37c0dc6cbfe
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    File URL: https://pure.uvt.nl/portal/files/1425373/lemmens_bagging.pdf
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    References listed on IDEAS

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    1. Athanassopoulos, Antreas D., 2000. "Customer Satisfaction Cues To Support Market Segmentation and Explain Switching Behavior," Journal of Business Research, Elsevier, vol. 47(3), pages 191-207, March.
    2. Franses,Philip Hans & Paap,Richard, 2010. "Quantitative Models in Marketing Research," Cambridge Books, Cambridge University Press, number 9780521143653.
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    Cited by:

    1. repec:eee:joinma:v:24:y:2010:i:3:p:198-208 is not listed on IDEAS
    2. K. W. De Bock & D. Van Den Poel, 2012. "Reconciling Performance and Interpretability in Customer Churn Prediction using Ensemble Learning based on Generalized Additive Models," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/805, Ghent University, Faculty of Economics and Business Administration.
    3. Coussement, Kristof & Benoit, Dries Frederik & Van den Poel, Dirk, 2009. "Improved Marketing Decision Making in a Customer Churn Prediction Context Using Generalized Additive Models," Working Papers 2009/18, Hogeschool-Universiteit Brussel, Faculteit Economie en Management.
    4. repec:eee:ijrema:v:31:y:2014:i:4:p:356-367 is not listed on IDEAS
    5. repec:eee:ijrema:v:33:y:2016:i:2:p:392-408 is not listed on IDEAS
    6. M. Ballings & D. Van Den Poel & E. Verhagen, 2013. "Evaluating the Added Value of Pictorial Data for Customer Churn Prediction," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/869, Ghent University, Faculty of Economics and Business Administration.
    7. repec:eee:joinma:v:25:y:2011:i:4:p:201-214 is not listed on IDEAS
    8. Ascarza, & Neslin, & Netzer, & Lemmens, Aurélie & Anderson, Zachery & Fader, Peter S. & Gupta, S. & Hardie, B.G.S. & Libai, Barak & Neal, David & Provost, Foster, 2017. "In pursuit of enhanced customer retention management : Review, key issues, and future directions," Other publications TiSEM 28a90d28-6daf-42f1-bd8e-e, Tilburg University, School of Economics and Management.
    9. repec:eee:ijrema:v:32:y:2015:i:2:p:195-206 is not listed on IDEAS
    10. Bas Donkers & Peter Verhoef & Martijn Jong, 2007. "Modeling CLV: A test of competing models in the insurance industry," Quantitative Marketing and Economics (QME), Springer, vol. 5(2), pages 163-190, June.
    11. Ballings, Michel & Van den Poel, Dirk, 2015. "CRM in social media: Predicting increases in Facebook usage frequency," European Journal of Operational Research, Elsevier, vol. 244(1), pages 248-260.
    12. Glady, Nicolas & Baesens, Bart & Croux, Christophe, 2009. "Modeling churn using customer lifetime value," European Journal of Operational Research, Elsevier, vol. 197(1), pages 402-411, August.
    13. D. F. Benoit & D. Van Den Poel, 2012. "Improving Customer Retention In Financial Services Using Kinship Network Information," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/786, Ghent University, Faculty of Economics and Business Administration.
    14. repec:eee:ijrema:v:34:y:2017:i:1:p:154-172 is not listed on IDEAS
    15. Croux, Christophe & Joossens, Kristel & Lemmens, Aurelie, 2007. "Trimmed bagging," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 362-368, September.
    16. repec:eee:joinma:v:24:y:2010:i:1:p:42-54 is not listed on IDEAS
    17. repec:eee:ijrema:v:30:y:2013:i:3:p:236-248 is not listed on IDEAS
    18. Coussement, Kristof & De Bock, Koen W., 2013. "Customer churn prediction in the online gambling industry: The beneficial effect of ensemble learning," Journal of Business Research, Elsevier, vol. 66(9), pages 1629-1636.
    19. van Wezel, Michiel & Potharst, Rob, 2007. "Improved customer choice predictions using ensemble methods," European Journal of Operational Research, Elsevier, vol. 181(1), pages 436-452, August.
    20. K. W. De Bock & D. Van Den Poel, 2011. "An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 11/717, Ghent University, Faculty of Economics and Business Administration.
    21. M. Ballings & D. Van Den Poel, 2012. "The Relevant Length of Customer Event History for Churn Prediction: How long is long enough?," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/804, Ghent University, Faculty of Economics and Business Administration.
    22. Stefan Lessmann & Stefan Voß, 2010. "Customer-Centric Decision Support," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 2(2), pages 79-93, April.
    23. repec:eee:ejores:v:269:y:2018:i:2:p:760-772 is not listed on IDEAS
    24. Nima Y. Jalali & Purushottam Papatla, 2016. "The palette that stands out: Color compositions of online curated visual UGC that attracts higher consumer interaction," Quantitative Marketing and Economics (QME), Springer, vol. 14(4), pages 353-384, December.
    25. Verbeke, Wouter & Dejaeger, Karel & Martens, David & Hur, Joon & Baesens, Bart, 2012. "New insights into churn prediction in the telecommunication sector: A profit driven data mining approach," European Journal of Operational Research, Elsevier, vol. 218(1), pages 211-229.

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