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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
Registered author(s):

    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.

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    File URL: http://www.sciencedirect.com/science/article/pii/S0377221711008599
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    Article provided by Elsevier in its journal European Journal of Operational Research.

    Volume (Year): 218 (2012)
    Issue (Month): 1 ()
    Pages: 211-229

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    Handle: RePEc:eee:ejores:v:218:y:2012:i:1:p:211-229
    Contact details of provider: Web page: http://www.elsevier.com/locate/eor

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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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. repec:sae:ecolab:v:16:y:2006:i:2:p:1-2 is not listed on IDEAS
    7. Lemmens, A. & Croux, C., 2006. "Bagging and Boosting Classification Trees to Predict Churn," Other publications TiSEM d5cb664d-5859-44db-a621-e, School of Economics and Management.
    8. Mizerski, Richard W, 1982. " An Attribution Explanation of the Disproportionate Influence of Unfavorable Information," Journal of Consumer Research, University of Chicago Press, vol. 9(3), pages 301-10, December.
    9. 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.
    10. 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.
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