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Random Forests for Uplift Modeling: An Insurance Customer Retention Case

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Modeling and Simulation in Engineering, Economics and Management (MS 2012)

Part of the book series: Lecture Notes in Business Information Processing ((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.

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Author information

Authors and Affiliations

  1. RBC Insurance, Royal Bank of Canada, 6880 Financial Drive, Mississauga, Ontario, L5N 7Y5, Canada

    Leo Guelman

  2. Riskcenter, University of Barcelona, Diagonal, 690, E-08034, Barcelona, Spain

    Montserrat Guillén & Ana M. Pérez-Marín

Authors
  1. Leo Guelman
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  2. Montserrat Guillén
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  3. Ana M. Pérez-Marín
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Editor information

Editors and Affiliations

  1. Hagan School of Business, Iona College, New Rochelle, NY, USA

    Kurt J. Engemann

  2. Department of Business Administration, University of Barcelona, Barcelona, Spain

    Anna M. Gil-Lafuente  & José M. Merigó  & 

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© 2012 Springer-Verlag Berlin Heidelberg

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Guelman, L., Guillén, M., Pérez-Marín, A.M. (2012). Random Forests for Uplift Modeling: An Insurance Customer Retention Case. In: Engemann, K.J., Gil-Lafuente, A.M., Merigó, J.M. (eds) Modeling and Simulation in Engineering, Economics and Management. MS 2012. Lecture Notes in Business Information Processing, vol 115. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30433-0_13

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  • DOI: https://doi.org/10.1007/978-3-642-30433-0_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-30432-3

  • Online ISBN: 978-3-642-30433-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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