Assessing the impact of derived behavior information on customer attrition in the financial service industry |
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| Authors: | Leilei Tang Lyn Thomas Mary Fletcher Jiazhu Pan Andrew Marshall |
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| Institution: | 1. Business School, University of Strathclyde, Glasgow G4 0NL, UK;2. School of Management, University of Southampton, UK;3. Business School, University of Strathclyde, UK;4. Department of Mathematics and Statistics, University of Strathclyde, UK;5. Business School, University of Strathclyde, UK;6. Hong Kong Baptist University, Hong Kong |
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| 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. |
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| Keywords: | Customer attrition Data mining Derived behavior information Orthogonal polynomial approximation Probit&ndash hazard model |
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