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Survival Analysis of a Household Portfolio of Insurance Policies: How Much Time Do You Have to Stop Total Customer Defection?

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Listed:
  • Patrick L. Brockett
  • Linda L. Golden
  • Montserrat Guillen
  • Jens Perch Nielsen
  • Jan Parner
  • Ana Maria Perez‐Marin

Abstract

Customer‐side influences on insurance have been relatively ignored in the literature. Using the household as the unit of analysis, this article focuses on the behavior of households having multiple policies of different types with the same insurance company, and who cancel their first policy. How long after the household's cancellation of the first policy does the insurer have to retain the customer and avoid customer defection on all policies to the competition? And, what customer characteristics are associated with customer loyalty? Using logistic regression and survival analysis techniques, an assessment is made of the probability of total customer withdrawal, and the length of time between first cancellation and subsequent customer withdrawal. Using a European database spanning 54 months of household multiple policyholder behavior, the results show that cancellation of one policy is a very strong indicator that other household policies will be canceled. Further, the insurer can have time to react to retain the customer after the first cancellation, however, this time is significantly dependent on the method used to contact the company, household demographics, and the nature of the household's insurance policy portfolio. Surprisingly, core customers having three or more policies in addition to the canceled policy are more vulnerable to total defection on all policies than noncore customers. Further, the potential customer repelling effects of premium increases seem to wear out after 12 months. Strategic implications of the results are presented.

Suggested Citation

  • Patrick L. Brockett & Linda L. Golden & Montserrat Guillen & Jens Perch Nielsen & Jan Parner & Ana Maria Perez‐Marin, 2008. "Survival Analysis of a Household Portfolio of Insurance Policies: How Much Time Do You Have to Stop Total Customer Defection?," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 75(3), pages 713-737, September.
  • Handle: RePEc:bla:jrinsu:v:75:y:2008:i:3:p:713-737
    DOI: 10.1111/j.1539-6975.2008.00281.x
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    References listed on IDEAS

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    1. Weiyu Kuo & Chenghsien Tsai & Wei‐Kuang Chen, 2003. "An Empirical Study on the Lapse Rate: The Cointegration Approach," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 70(3), pages 489-508, September.
    2. Mayers, David & Smith, Clifford W, Jr, 1983. "The Interdependence of Individual Portfolio Decisions and the Demand for Insurance," Journal of Political Economy, University of Chicago Press, vol. 91(2), pages 304-311, April.
    3. Doherty, Neil A & Schlesinger, Harris, 1983. "Optimal Insurance in Incomplete Markets," Journal of Political Economy, University of Chicago Press, vol. 91(6), pages 1045-1054, December.
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    Cited by:

    1. Pechon, Florian & Denuit, Michel & Trufin, Julien, 2019. "Home and Motor insurance joined at a household level using multivariate credibility," LIDAM Discussion Papers ISBA 2019013, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    2. Dorothea Diers & Martin Eling & Christian Kraus & Andreas Reuß, 2012. "Market-consistent embedded value in non-life insurance: how to measure it and why," Journal of Risk Finance, Emerald Group Publishing, vol. 13(4), pages 320-346, August.
    3. Ramon Alemany & Catalina Bolance & Montserrat Guillen, 2014. "Accounting for severity of risk when pricing insurance products," Working Papers 2014-05, Universitat de Barcelona, UB Riskcenter.
    4. Piotr Bialowolski & Jing Jian Xiao & Dorota Weziak-Bialowolska, 2024. "Do All Savings Matter Equally? Saving Types and Emotional Well-Being Among Older Adults: Evidence from Panel Data," Journal of Family and Economic Issues, Springer, vol. 45(1), pages 88-105, March.
    5. Montserrat Guillén & Ana María Pérez-Marín & Montserrat Guillén, 2011. "A logistic regression approach to estimating customer profit loss due to lapses in insurance," Working Papers XREAP2011-13, Xarxa de Referència en Economia Aplicada (XREAP), revised Oct 2011.
    6. Bolancé, Catalina & Vernic, Raluca, 2019. "Multivariate count data generalized linear models: Three approaches based on the Sarmanov distribution," Insurance: Mathematics and Economics, Elsevier, vol. 85(C), pages 89-103.
    7. Christophe Dutang, 2012. "The customer, the insurer and the market," Post-Print hal-01616152, HAL.
    8. Manuel Leiria & Nelson Matos & Efigénio Rebelo, 2021. "Non-life insurance cancellation: a systematic quantitative literature review," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 46(4), pages 593-613, October.
    9. Abdul-Fatawu Majeed, 2020. "Accelerated Failure Time Models: An Application in Insurance Attrition [Modèles de temps de défaillance accéléré: une application dans l'attrition de l'assurance]," Post-Print hal-02953269, HAL.
    10. Catalina Bolancé & Raluca Vernic, 2017. "“Multivariate count data generalized linear models: Three approaches based on the Sarmanov distribution”," IREA Working Papers 201718, University of Barcelona, Research Institute of Applied Economics, revised Oct 2017.
    11. Edward W. Frees & Gee Lee & Lu Yang, 2016. "Multivariate Frequency-Severity Regression Models in Insurance," Risks, MDPI, vol. 4(1), pages 1-36, February.

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