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Modelling and predicting customer churn from an insurance company. (English) Zbl 1401.91144

Summary: Within a company’s customer relationship management strategy, finding the customers most likely to leave is a central aspect. We present a dynamic modelling approach for predicting individual customers’ risk of leaving an insurance company. A logistic longitudinal regression model that incorporates time-dynamic explanatory variables and interactions is fitted to the data. As an intermediate step in the modelling procedure, we apply generalised additive models to identify non-linear relationships between the logit and the explanatory variables. Both out-of-sample and out-of-time prediction indicate that the model performs well in terms of identifying customers likely to leave the company each month. Our approach is general and may be applied to other industries as well.

MSC:

91B30 Risk theory, insurance (MSC2010)
62J05 Linear regression; mixed models
62P05 Applications of statistics to actuarial sciences and financial mathematics

Software:

R
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References:

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