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Integrating the Voice of Customers through Call Center Emails into a Decision Support System for Churn Prediction

Author

Listed:
  • K. COUSSEMENT
  • D. VAN DEN POEL

    ()

Abstract

We studied the problem of optimizing the performance of a DSS for churn prediction. In particular, we investigated the beneficial effect of adding the voice of customers through call center emails – i.e. textual information - to a churn prediction system that only uses traditional marketing information. We found that adding unstructured, textual information into a conventional churn prediction model resulted in a significant increase in predictive performance. From a managerial point of view, this integrated framework helps marketing-decision makers to identify customers most prone to switch. Consequently, their customer retention campaigns can be targeted effectively because the prediction method is better at detecting those customers who are likely to leave.

Suggested Citation

  • K. Coussement & D. Van Den Poel, 2008. "Integrating the Voice of Customers through Call Center Emails into a Decision Support System for Churn Prediction," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 08/502, Ghent University, Faculty of Economics and Business Administration.
  • Handle: RePEc:rug:rugwps:08/502
    as

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    File URL: http://wps-feb.ugent.be/Papers/wp_08_502.pdf
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    Citations

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    Cited by:

    1. M. Ballings & D. Van Den Poel & E. Verhagen, 2013. "Evaluating the Added Value of Pictorial Data for Customer Churn Prediction," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/869, Ghent University, Faculty of Economics and Business Administration.
    2. repec:eee:tefoso:v:123:y:2017:i:c:p:381-388 is not listed on IDEAS
    3. Coussement, Kristof & Van den Bossche, Filip A.M. & De Bock, Koen W., 2014. "Data accuracy's impact on segmentation performance: Benchmarking RFM analysis, logistic regression, and decision trees," Journal of Business Research, Elsevier, vol. 67(1), pages 2751-2758.
    4. Chen, Zhen-Yu & Fan, Zhi-Ping & Sun, Minghe, 2012. "A hierarchical multiple kernel support vector machine for customer churn prediction using longitudinal behavioral data," European Journal of Operational Research, Elsevier, vol. 223(2), pages 461-472.
    5. D. Thorleuchter & D. Van Den Poel & A. Prinzie, 2011. "Analyzing existing customers’ websites to improve the customer acquisition process as well as the profitability prediction in B-to-B marketing," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 11/733, Ghent University, Faculty of Economics and Business Administration.
    6. K. W. De Bock & D. Van Den Poel, 2011. "An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 11/717, Ghent University, Faculty of Economics and Business Administration.
    7. repec:eee:techno:v:60-61:y:2017:i::p:15-28 is not listed on IDEAS
    8. repec:eee:proeco:v:191:y:2017:i:c:p:97-112 is not listed on IDEAS
    9. D. Thorleuchter & D. Van Den Poel & A. Prinzie & -, 2010. "A compared R&D-based and patent-based cross impact analysis for identifying relationships between technologies," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 10/632, Ghent University, Faculty of Economics and Business Administration.

    More about this item

    Keywords

    customer relationship management (CRM); data mining; churn prediction; text mining; call center email; voice of customers (VOC); binary classification modeling;

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    1. Text mining in Wikipedia English ne '')
    2. Testu-meatzaritza in Wikipedia Basque ne '')
    3. تنقيب في النصوص in Wikipedia Arabic ne '')

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