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Domain knowledge integration in data mining using decision tables: case studies in churn prediction

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Listed:
  • E Lima

    (University of Southampton)

  • C Mues

    (University of Southampton)

  • B Baesens

    (University of Southampton)

Abstract

Companies' interest in customer relationship modelling and key issues such as customer lifetime value and churn has substantially increased over the years. However, the complexity of building, interpreting and applying these models creates obstacles for their implementation. The main contribution of this paper is to show how domain knowledge can be incorporated in the data mining process for churn prediction, viz. through the evaluation of coefficient signs in a logistic regression model, and secondly, by analysing a decision table (DT) extracted from a decision tree or rule-based classifier. An algorithm to check DTs for violations of monotonicity constraints is presented, which involves the repeated application of condition reordering and table contraction to detect counter-intuitive patterns. Both approaches are applied to two telecom data sets to empirically demonstrate how domain knowledge can be used to ensure the interpretability of the resulting models.

Suggested Citation

  • E Lima & C Mues & B Baesens, 2009. "Domain knowledge integration in data mining using decision tables: case studies in churn prediction," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(8), pages 1096-1106, August.
    • Handle: RePEc:pal:jorsoc:v:60:y:2009:i:8:d:10.1057_jors.2008.161
    DOI: 10.1057/jors.2008.161
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    References listed on IDEAS

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    1. Van Gestel, Tony & Martens, David & Baesens, Bart & Feremans, Daniel & Huysmans, Johan & Vanthienen, Jan, 2007. "Forecasting and analyzing insurance companies' ratings," International Journal of Forecasting, Elsevier, vol. 23(3), pages 513-529.
    2. 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.
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    4. 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.
    5. B Baesens & T Van Gestel & S Viaene & M Stepanova & J Suykens & J Vanthienen, 2003. "Benchmarking state-of-the-art classification algorithms for credit scoring," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(6), pages 627-635, June.
    6. Marina Velikova & Hennie Daniels, 2004. "Decision trees for monotone price models," Computational Management Science, Springer, vol. 1(3), pages 231-244, October.
    7. 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.
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    Cited by:

    1. Gerpott, Torsten J. & Meinert, Phil, 2018. "Termination notice of mobile network operator customers after a tariff switch: An empirical study of postpaid subscribers in Germany," Telecommunications Policy, Elsevier, vol. 42(3), pages 212-226.
    2. 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.
    3. Gerpott, Torsten J. & Ahmadi, Nima & Weimar, Daniel, 2015. "Who is (not) convinced to withdraw a contract termination announcement? – A discriminant analysis of mobile communications customers in Germany," Telecommunications Policy, Elsevier, vol. 39(1), pages 38-52.
    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.

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