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

Author

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

Abstract

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
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    References listed on IDEAS

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    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.

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