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Modeling CLV: A test of competing models in the insurance industry

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Author Info

  • Bas Donkers

    ()

  • Peter Verhoef

    ()

  • Martijn Jong

    ()

Abstract

Customer Lifetime Value (CLV) is one of the key metrics in marketing and is considered an important segmentation base. This paper studies the capabilities of a range of models to predict CLV in the insurance industry. The simplest models can be constructed at the customer relationship level, i.e. aggregated across all services. The more complex models focus on the individual services, paying explicit attention to cross buying, but also retention. The models build on a plethora of approaches used in the existing literature and include a status quo model, a Tobit II model, univariate and multivariate choice models, and duration models. For all models, CLV for each customer is computed for a four-year time horizon. We find that the simple models perform well. The more complex models are expected to better capture the richness of relationship development. Surprisingly, this does not lead to substantially better CLV predictions. Copyright Springer Science+Business Media, LLC 2007

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Bibliographic Info

Article provided by Springer in its journal Quantitative Marketing and Economics.

Volume (Year): 5 (2007)
Issue (Month): 2 (June)
Pages: 163-190

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Handle: RePEc:kap:qmktec:v:5:y:2007:i:2:p:163-190

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Web page: http://www.springerlink.com/link.asp?id=111240

Related research

Keywords: Customer lifetime value; CLV-models; Forecasting; Database marketing; M30; C53; C35;

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References

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  9. Kristiaan Helsen & David C. Schmittlein, 1993. "Analyzing Duration Times in Marketing: Evidence for the Effectiveness of Hazard Rate Models," Marketing Science, INFORMS, vol. 12(4), pages 395-414.
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  12. Donkers, A.C.D. & Franses, Ph.H.B.F. & Verhoef, P.C., 2001. "Using Selective Sampling for Binary Choice Models to Reduce Survey Costs," ERIM Report Series Research in Management ERS-2001-67-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus Uni.
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Citations

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Cited by:
  1. D. F. Benoit & D. Van Den Poel, 2009. "Benefits of Quantile Regression for the Analysis of Customer Lifetime Value in a Contractual Setting: An Application in Financial Services," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 09/551, Ghent University, Faculty of Economics and Business Administration.
  2. Petr Čermák, 2013. "Analysis of customer lifetime value model: Literature review," Český finanční a účetní časopis, University of Economics, Prague, vol. 2013(4), pages 84-95.
  3. 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.
  4. Audzeyeva, Alena & Summers, Barbara & Schenk-Hoppé, Klaus Reiner, 2012. "Forecasting customer behaviour in a multi-service financial organisation: A profitability perspective," International Journal of Forecasting, Elsevier, vol. 28(2), pages 507-518.

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