Applying and Evaluating Models to Predict Customer Attrition Using Data Mining Techniques

Authors

  • Tom Au AT&T, USA
  • Guangqin Ma AT&T, USA
  • Shaomin Li Old Dominion University, USA

Abstract

As competition intensifies, retaining customers becomes one of the most serious challenges facing customer service providers. Customer attrition prediction models hold great promise as powerful tools for enhancing customer retention. Several statistical methods have been applied to develop models predicting customer attrition. Yet little research is done on the relative performance of models developed by different methods. The lack of knowledge about the performance of various prediction models is more pronounced due to the nonlinear nature of the combined causes of attrition (such as switching to another provider or canceling a service). The development of data mining techniques has made the comparison of prediction power of different models more efficient and easier. In this article we demonstrate how to use data mining techniques and software to fit and compare different customer attrition prediction models, using data from a major telecom service provider.

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Published

2003-01-01

How to Cite

Au, T., Ma, G., & Li, S. (2003). Applying and Evaluating Models to Predict Customer Attrition Using Data Mining Techniques. Journal of Comparative International Management, 6(1). Retrieved from https://journals.lib.unb.ca/index.php/JCIM/article/view/442

Issue

Vol. 6 No. 1 (2003)

Section

RESEARCH ARTICLES

License

Papers accepted become the copyright of the journal, unless otherwise specifically agreed.