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An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction

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  • K. W. DE BOCK
  • D. VAN DEN POEL

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Abstract

Several studies have demonstrated the superior performance of ensemble classification algorithms, whereby multiple member classifiers are combined into one aggregated and powerful classification model, over single models. In this paper, two rotation-based ensemble classifiers are proposed as modeling techniques for customer churn prediction. In Rotation Forests, feature extraction is applied to feature subsets in order to rotate the input data for training base classifiers, while RotBoost combines Rotation Forest with AdaBoost. In an experimental validation based on data sets from four real-life customer churn prediction projects, Rotation Forest and RotBoost are compared to a set of well-known benchmark classifiers. Moreover, variations of Rotation Forest and RotBoost are compared, implementing three alternative feature extraction algorithms: Principal Component Analysis (PCA), Independent Component Analysis (ICA) and Sparse Random Projections (SRP). The performance of rotation-based ensemble classifier is found to depend upon (i) the performance criterion used to measure classification performance, and (ii) the implemented feature extraction algorithm. In terms of accuracy, RotBoost outperforms Rotation Forest, but none of the considered variations offers a clear advantage over the benchmark algorithms. However, in terms of AUC and top-decile lift, results clearly demonstrate the competitive performance of Rotation Forests compared to the benchmark algorithms. Moreover, ICA3 based Rotation Forests outperform all other considered classifiers and are therefore recommended as a well-suited alternative classification technique for the prediction of customer churn that allows for improved marketing decision making.

Suggested Citation

  • 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.
  • Handle: RePEc:rug:rugwps:11/717
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    References listed on IDEAS

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    1. 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.
    2. J. Burez & D. Van Den Poel, 2008. "Handling class imbalance in customer churn prediction," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 08/517, Ghent University, Faculty of Economics and Business Administration.
    3. K. Coussement & D. Van Den Poel, 2006. "Churn Prediction in Subscription Services: an Application of Support Vector Machines While Comparing Two Parameter-Selection Techniques," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 06/412, Ghent University, Faculty of Economics and Business Administration.
    4. Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, pages 471-474.
    5. B. Larivière & D. Van Den Poel, 2004. "Predicting Customer Retention and Profitability by Using Random Forests and Regression Forests Techniques," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/282, Ghent University, Faculty of Economics and Business Administration.
    6. De Bock, Koen W. & Coussement, Kristof & Van den Poel, Dirk, 2010. "Ensemble classification based on generalized additive models," Computational Statistics & Data Analysis, Elsevier, pages 1535-1546.
    7. J. Burez & D. Van Den Poel, 2005. "CRM at a Pay-TV Company: Using Analytical Models to Reduce Customer Attrition by Targeted Marketing for Subscription Services," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/348, Ghent University, Faculty of Economics and Business Administration.
    8. De Bock, Koen W. & Coussement, Kristof & Van den Poel, Dirk, 2010. "Ensemble classification based on generalized additive models," Computational Statistics & Data Analysis, Elsevier, pages 1535-1546.
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    12. Van den Poel, Dirk & Lariviere, Bart, 2004. "Customer attrition analysis for financial services using proportional hazard models," European Journal of Operational Research, Elsevier, vol. 157(1), pages 196-217, August.
    13. J. Burez & D. Van Den Poel, 2007. "Separating Financial From Commercial Customer Churn: A Modeling Step Towards Resolving The Conflict Between The Sales And Credit Department," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 07/476, Ghent University, Faculty of Economics and Business Administration.
    14. 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.
    15. K. Coussement & D. Van Den Poel, 2008. "Integrating the Voice of Customers through Call Center Emails into a Decision Support System for Churn Prediction," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 08/502, Ghent University, Faculty of Economics and Business Administration.
    16. K. Coussement & D. Van Den Poel, 2008. "Improving Customer Attrition Prediction by Integrating Emotions from Client/Company Interaction Emails and Evaluating Multiple Classifiers," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 08/527, Ghent University, Faculty of Economics and Business Administration.
    17. A. Prinzie & D. Van Den Poel, 2007. "Random Forrests for Multiclass classification: Random Multinomial Logit," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 07/435, Ghent University, Faculty of Economics and Business Administration.
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    19. K. Coussement & D.F. Benoît & D. Van den Poel, 2010. "Improved marketing decision making in a customer churn prediction context using generalized additive models," Post-Print halshs-00581701, HAL.
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    Citations

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    Cited by:

    1. 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.
    2. V L Miguéis & D F Benoit & D Van den Poel, 2013. "Enhanced decision support in credit scoring using Bayesian binary quantile regression," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 64(9), pages 1374-1383, September.
    3. Adebiyi Sulaimon Olanrewaju & Oyatoye Emmanuel Olateju & Mojekwu Joseph Nnamdi, 2015. "Predicting Customer Churn and Retention Rates in Nigeria’s Mobile Telecommunication Industry Using Markov Chain Modelling," Acta Universitatis Sapientiae, Economics and Business, De Gruyter Open, pages 67-80.
    4. 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.
    5. Aimée Backiel & Bart Baesens & Gerda Claeskens, 2016. "Predicting time-to-churn of prepaid mobile telephone customers using social network analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, pages 1135-1145.
    6. repec:spr:telsys:v:66:y:2017:i:4:d:10.1007_s11235-017-0310-7 is not listed on IDEAS

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    Keywords

    CRM; database marketing; customer churn prediction; ensemble classification; rotation-based ensemble classifiers; RotBoost; Rotation Forest; ICA; AUC; lift;

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