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Assessing the impact of derived behavior information on customer attrition in the financial service industry

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  • Tang, Leilei
  • Thomas, Lyn
  • Fletcher, Mary
  • Pan, Jiazhu
  • Marshall, Andrew

Abstract

The value of the customer has been widely recognized in terms of financial planning and efficient resource allocation including the financial service industry. Previous studies have shown that directly observable information can be used in order to make reasonable predictions of customer attrition probabilities. However, these studies do not take full account of customer behavior information. In this paper, we demonstrate that efficient use of information can add value to financial services industry and improve the prediction of customer attrition. To achieve this, we apply an orthogonal polynomial approximation analysis to derive unobservable information, which is then used as explanatory variables in a probit–hazard rate model. Our results show that derived information can help our understanding of customer attrition behavior and give better predictions. We conclude that both researchers and the financial service industry should gather and use derived financial information in addition to directly observable information.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:236:y:2014:i:2:p:624-633
    DOI: 10.1016/j.ejor.2014.01.004
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    References listed on IDEAS

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    Cited by:

    1. Arno de Caigny & Kristof Coussement & Koen W. de Bock & Stefan Lessmann, 2019. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," Post-Print hal-02275958, HAL.
    2. Born, Alexander & Kovachka, Nikoleta & Lessmann, Stefan & Seow, Hsin-Vonn, 2018. "Price Management in the Used-Car Market: An Evaluation of Survival Analysis," IRTG 1792 Discussion Papers 2018-065, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    3. De Caigny, Arno & Coussement, Kristof & De Bock, Koen W. & Lessmann, Stefan, 2020. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1563-1578.
    4. De Caigny, Arno & Coussement, Kristof & De Bock, Koen W., 2018. "A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees," European Journal of Operational Research, Elsevier, vol. 269(2), pages 760-772.

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