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

Author

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  • 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

Suggested Citation

  • Bas Donkers & Peter Verhoef & Martijn Jong, 2007. "Modeling CLV: A test of competing models in the insurance industry," Quantitative Marketing and Economics (QME), Springer, vol. 5(2), pages 163-190, June.
    • Handle: RePEc:kap:qmktec:v:5:y:2007:i:2:p:163-190
    DOI: 10.1007/s11129-006-9016-y
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    1. Peter E. Rossi & Robert E. McCulloch & Greg M. Allenby, 1996. "The Value of Purchase History Data in Target Marketing," Marketing Science, INFORMS, vol. 15(4), pages 321-340.
    2. Meyer, Bruce D, 1990. "Unemployment Insurance and Unemployment Spells," Econometrica, Econometric Society, vol. 58(4), pages 757-782, July.
    3. Bolton, R.N. & Lemo, K.N. & Verhoef, P.C., 2002. "The Theoretical Underpinnings of Customer Asset Management," ERIM Report Series Research in Management ERS-2002-80-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 University Rotterdam.
    4. Dipak C. Jain & Naufel J. Vilcassim, 1991. "Investigating Household Purchase Timing Decisions: A Conditional Hazard Function Approach," Marketing Science, INFORMS, vol. 10(1), pages 1-23.
    5. 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 University Rotterdam.
    6. Stern, Steven, 1992. "A Method for Smoothing Simulated Moments of Discrete Probabilities in Multinomial Probit Models," Econometrica, Econometric Society, vol. 60(4), pages 943-952, July.
    7. Wagner Kamakura & Carl Mela & Asim Ansari & Anand Bodapati & Pete Fader & Raghuram Iyengar & Prasad Naik & Scott Neslin & Baohong Sun & Peter Verhoef & Michel Wedel & Ron Wilcox, 2005. "Choice Models and Customer Relationship Management," Marketing Letters, Springer, vol. 16(3), pages 279-291, December.
    8. Peter S. Fader & Bruce G. S. Hardie, 2001. "Forecasting Repeat Sales at CDNOW: A Case Study," Interfaces, INFORMS, vol. 31(3_supplem), pages 94-107, June.
    9. Lemmens, A. & Croux, C., 2006. "Bagging and boosting classification trees to predict churn," Other publications TiSEM d5cb664d-5859-44db-a621-e, Tilburg University, School of Economics and Management.
    10. Ruth N. Bolton, 1998. "A Dynamic Model of the Duration of the Customer's Relationship with a Continuous Service Provider: The Role of Satisfaction," Marketing Science, INFORMS, vol. 17(1), pages 45-65.
    11. Roland T. Rust & Peter C. Verhoef, 2005. "Optimizing the Marketing Interventions Mix in Intermediate-Term CRM," Marketing Science, INFORMS, vol. 24(3), pages 477-489, December.
    12. David C. Schmittlein & Donald G. Morrison & Richard Colombo, 1987. "Counting Your Customers: Who-Are They and What Will They Do Next?," Management Science, INFORMS, vol. 33(1), pages 1-24, January.
    13. Wagner A. Kamakura & Bruce S. Kossar & Michel Wedel, 2004. "Identifying Innovators for the Cross-Selling of New Products," Management Science, INFORMS, vol. 50(8), pages 1120-1133, August.
    14. 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.
    15. Peter S. Fader & Bruce G. S. Hardie & Chun-Yao Huang, 2004. "A Dynamic Changepoint Model for New Product Sales Forecasting," Marketing Science, INFORMS, vol. 23(1), pages 50-65, October.
    16. Franses,Philip Hans & Paap,Richard, 2010. "Quantitative Models in Marketing Research," Cambridge Books, Cambridge University Press, number 9780521143653.
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    More about this item

    Keywords

    Customer lifetime value; CLV-models; Forecasting; Database marketing; M30; C53; C35;
    All these keywords.

    JEL classification:

    • M30 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions

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