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Modelling and predicting customer churn from an insurance company

Author

Listed:
  • Clara-Cecilie Günther
  • Ingunn Tvete
  • Kjersti Aas
  • Geir Sandnes
  • Ørnulf Borgan

Abstract

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.

Suggested Citation

  • Clara-Cecilie Günther & Ingunn Tvete & Kjersti Aas & Geir Sandnes & Ørnulf Borgan, 2014. "Modelling and predicting customer churn from an insurance company," Scandinavian Actuarial Journal, Taylor & Francis Journals, vol. 2014(1), pages 58-71.
    • Handle: RePEc:taf:sactxx:v:2014:y:2014:i:1:p:58-71
    DOI: 10.1080/03461238.2011.636502
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    Cited by:

    1. López-Díaz, María Concepción & López-Díaz, Miguel & Martínez-Fernández, Sergio, 2023. "On the optimal binary classifier with an application," Computational Statistics & Data Analysis, Elsevier, vol. 181(C).

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