IDEAS home Printed from
   My bibliography  Save this article

New insights into churn prediction in the telecommunication sector: A profit driven data mining approach


  • Verbeke, Wouter
  • Dejaeger, Karel
  • Martens, David
  • Hur, Joon
  • Baesens, Bart


Customer churn prediction models aim to indicate the customers with the highest propensity to attrite, allowing to improve the efficiency of customer retention campaigns and to reduce the costs associated with churn. Although cost reduction is their prime objective, churn prediction models are typically evaluated using statistically based performance measures, resulting in suboptimal model selection. Therefore, in the first part of this paper, a novel, profit centric performance measure is developed, by calculating the maximum profit that can be generated by including the optimal fraction of customers with the highest predicted probabilities to attrite in a retention campaign. The novel measure selects the optimal model and fraction of customers to include, yielding a significant increase in profits compared to statistical measures.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:ejores:v:218:y:2012:i:1:p:211-229
    DOI: 10.1016/j.ejor.2011.09.031

    Download full text from publisher

    File URL:
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    1. 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.
    2. repec:sae:ecolab:v:16:y:2006:i:2:p:1-2 is not listed on IDEAS
    3. Ruth N. Bolton & Katherine N. Lemon & Matthew D. Bramlett, 2006. "The Effect of Service Experiences over Time on a Supplier's Retention of Business Customers," Management Science, INFORMS, vol. 52(12), pages 1811-1823, December.
    4. Nikolay Nenovsky & S. Statev, 2006. "Introduction," Post-Print halshs-00260898, HAL.
    5. M. Ruth & K. Donaghy & P. Kirshen, 2006. "Introduction," Chapters,in: Regional Climate Change and Variability, chapter 1 Edward Elgar Publishing.
    6. 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.
    7. Piramuthu, Selwyn, 2004. "Evaluating feature selection methods for learning in data mining applications," European Journal of Operational Research, Elsevier, vol. 156(2), pages 483-494, July.
    8. Bart Baesens & Rudy Setiono & Christophe Mues & Jan Vanthienen, 2003. "Using Neural Network Rule Extraction and Decision Tables for Credit-Risk Evaluation," Management Science, INFORMS, vol. 49(3), pages 312-329, March.
    9. 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.
    10. 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.
    11. Dudyala Anil Kumar & V. Ravi, 2008. "Predicting credit card customer churn in banks using data mining," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 1(1), pages 4-28.
    12. K. Coussement & D. Van den Poel, 2008. "Churn prediction in subscription services: an application of support vector machines while comparing two parameter-selection techniques," Post-Print hal-00788096, HAL.
    Full references (including those not matched with items on IDEAS)


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

    Cited by:

    1. repec:pal:jorsoc:v:68:y:2017:i:11:d:10.1057_s41274-016-0013-6 is not listed on IDEAS
    2. repec:kap:netnom:v:18:y:2017:i:1:d:10.1007_s11066-017-9114-x is not listed on IDEAS
    3. Fan, Zhi-Ping & Sun, Minghe, 2015. "Behavior-aware user response modeling in social media: Learning from diverse heterogeneous dataAuthor-Name: Chen, Zhen-Yu," European Journal of Operational Research, Elsevier, vol. 241(2), pages 422-434.
    4. Clemente-Císcar, M. & San Matías, S. & Giner-Bosch, V., 2014. "A methodology based on profitability criteria for defining the partial defection of customers in non-contractual settings," European Journal of Operational Research, Elsevier, vol. 239(1), pages 276-285.
    5. Lessmann, Stefan & Baesens, Bart & Seow, Hsin-Vonn & Thomas, Lyn C., 2015. "Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research," European Journal of Operational Research, Elsevier, vol. 247(1), pages 124-136.
    6. Uroš Droftina & Mitja Å tular & Andrej Košir, 2015. "A diffusion model for churn prediction based on sociometric theory," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 9(3), pages 341-365, September.
    7. Álvaro Julio Cuadros & Victoria Eugenia Domínguez, 2014. "Customer segmentation model based on value generation for marketing strategies formulation," ESTUDIOS GERENCIALES, UNIVERSIDAD ICESI, March.
    8. Dejaeger, Karel & Goethals, Frank & Giangreco, Antonio & Mola, Lapo & Baesens, Bart, 2012. "Gaining insight into student satisfaction using comprehensible data mining techniques," European Journal of Operational Research, Elsevier, vol. 218(2), pages 548-562.
    9. Martin-Barragan, Belen & Lillo, Rosa & Romo, Juan, 2014. "Interpretable support vector machines for functional data," European Journal of Operational Research, Elsevier, vol. 232(1), pages 146-155.
    10. Verbraken, Thomas & Bravo, Cristián & Weber, Richard & Baesens, Bart, 2014. "Development and application of consumer credit scoring models using profit-based classification measures," European Journal of Operational Research, Elsevier, vol. 238(2), pages 505-513.
    11. Tang, Leilei & Thomas, Lyn & Fletcher, Mary & Pan, Jiazhu & Marshall, Andrew, 2014. "Assessing the impact of derived behavior information on customer attrition in the financial service industry," European Journal of Operational Research, Elsevier, vol. 236(2), pages 624-633.
    12. Gaivoronski, Alexei A. & Nesse, Per-Jonny & Østerbo, Olav-Norvald & Lønsethagen, Håkon, 2016. "Risk-balanced dimensioning and pricing of End-to-End differentiated services," European Journal of Operational Research, Elsevier, vol. 254(2), pages 644-655.
    13. 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, vol. 67(9), pages 1135-1145, September.
    14. Todor Krastevich, 2013. "Predicting Consumer Choices Through Analysis of Interactions in Social Networks," Economic Alternatives, University of National and World Economy, Sofia, Bulgaria, issue 3, pages 24-40, September.
    15. repec:spr:telsys:v:66:y:2017:i:4:d:10.1007_s11235-017-0310-7 is not listed on IDEAS
    16. Arturo Basaure & Varadharajan Sridhar & Heikki Hämmäinen, 2016. "Adoption of dynamic spectrum access technologies: a system dynamics approach," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 63(2), pages 169-190, October.
    17. Brandner, Hubertus & Lessmann, Stefan & Voß, Stefan, 2013. "A memetic approach to construct transductive discrete support vector machines," European Journal of Operational Research, Elsevier, vol. 230(3), pages 581-595.


    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:eee:ejores:v:218:y:2012:i:1:p:211-229. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu). General contact details of provider: .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.