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Random Forests for Uplift Modeling: An Insurance Customer Retention Case

  • Leo Guelman
  • Montserrat Guillén
  • Ana M. Pérez-Marín
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 115)

Abstract

Models of customer churn are based on historical data and are used to predict the probability that a client switches to another company. We address customer retention in insurance. Rather than concentrating on those customers with high probability of leaving, we propose a new procedure that can be used to identify the target customers who are likely to respond positively to a retention activity. Our approach is based on random forests and can be useful to anticipate the success of marketing actions aimed at reducing customer attrition. We also discuss the type of insurance portfolio database that can be used for this purpose.

Keywords

Random Forest Switching Cost Customer Loyalty Insurance Industry Loyalty Strategy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Leo Guelman
    • 1
  • Montserrat Guillén
    • 2
  • Ana M. Pérez-Marín
    • 2
  1. 1.RBC InsuranceRoyal Bank of CanadaMississaugaCanada
  2. 2.RiskcenterUniversity of BarcelonaBarcelonaSpain

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