Journal
SCANDINAVIAN ACTUARIAL JOURNAL
Volume 2014, Issue 1, Pages 58-71Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/03461238.2011.636502
Keywords
generalised additive models; logistic regression; longitudinal data; CRM; non-life insurance
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Funding
- Norwegian Center for Research-based Innovation Statistics for innovation' [970141669]
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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.
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