IDEAS home Printed from https://ideas.repec.org/a/eee/insuma/v124y2025ics0167668725000794.html

A usage-based insurance (UBI) pricing model considering customer retention

Author

Listed:
  • Li, Hong-Jie
  • Luo, Xing-Gang
  • Zhang, Zhong-Liang
  • Huang, Shen-Wei
  • Jiang, Wei

Abstract

Usage-based insurance (UBI) charges drivers differently through telematics-based driving risk assessments. While current UBI pricing models differentiate driving risks, their overly discriminative prices may expel risky drivers, whose driving behaviors could have been modified, thereby incurring insurers' losses in profits. We propose a new UBI pricing model to address this problem by incorporating customer retention into the conventional UBI framework. Specifically, our model offers targeted discounts based on drivers' price sensitivity to retain those who may terminate the insurance contract, as well as provides concrete suggestions to help them modify unsafe driving behaviors. Using empirical data from a major Chinese auto insurer, we confirm that our model yields higher profits for insurers over the UBI pricing model that does not account for customer retention, and exemplify how suggestions for drivers can be drawn from driving profiles.

Suggested Citation

  • Li, Hong-Jie & Luo, Xing-Gang & Zhang, Zhong-Liang & Huang, Shen-Wei & Jiang, Wei, 2025. "A usage-based insurance (UBI) pricing model considering customer retention," Insurance: Mathematics and Economics, Elsevier, vol. 124(C).
    • Handle: RePEc:eee:insuma:v:124:y:2025:i:c:s0167668725000794
    DOI: 10.1016/j.insmatheco.2025.103132
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167668725000794
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.insmatheco.2025.103132?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Xiao Liu, 2023. "Dynamic Coupon Targeting Using Batch Deep Reinforcement Learning: An Application to Livestream Shopping," Marketing Science, INFORMS, vol. 42(4), pages 637-658, July.
    2. Achim Zeileis & Kurt Hornik, 2007. "Generalized M‐fluctuation tests for parameter instability," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 61(4), pages 488-508, November.
    3. Paefgen, Johannes & Staake, Thorsten & Fleisch, Elgar, 2014. "Multivariate exposure modeling of accident risk: Insights from Pay-as-you-drive insurance data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 61(C), pages 27-40.
    4. Gerpott, Torsten J. & Rams, Wolfgang & Schindler, Andreas, 2001. "Customer retention, loyalty, and satisfaction in the German mobile cellular telecommunications market," Telecommunications Policy, Elsevier, vol. 25(4), pages 249-269, May.
    5. Gao, Guangyuan & Meng, Shengwang & Wüthrich, Mario V., 2022. "What can we learn from telematics car driving data: A survey," Insurance: Mathematics and Economics, Elsevier, vol. 104(C), pages 185-199.
    6. Amini, Mostafa & Bagheri, Ali & Delen, Dursun, 2022. "Discovering injury severity risk factors in automobile crashes: A hybrid explainable AI framework for decision support," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    7. Bian, Yiyang & Yang, Chen & Zhao, J. Leon & Liang, Liang, 2018. "Good drivers pay less: A study of usage-based vehicle insurance models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 107(C), pages 20-34.
    8. Mercedes Ayuso & Montserrat Guillen & Jens Perch Nielsen, 2019. "Improving automobile insurance ratemaking using telematics: incorporating mileage and driver behaviour data," Transportation, Springer, vol. 46(3), pages 735-752, June.
    9. Miremad Soleymanian & Charles B. Weinberg & Ting Zhu, 2019. "Sensor Data and Behavioral Tracking: Does Usage-Based Auto Insurance Benefit Drivers?," Marketing Science, INFORMS, vol. 38(1), pages 21-43, January.
    10. Montserrat Guillen & Jens Perch Nielsen & Mercedes Ayuso & Ana M. Pérez‐Marín, 2019. "The Use of Telematics Devices to Improve Automobile Insurance Rates," Risk Analysis, John Wiley & Sons, vol. 39(3), pages 662-672, March.
    11. Zhenkun Liu & Ping Jiang & Koen W. de Bock & Jianzhou Wang & Lifang Zhang & Xinsong Niu, 2024. "Extreme gradient boosting trees with efficient Bayesian optimization for profit-driven customer churn prediction," Post-Print hal-04273578, HAL.
    12. Ma, Yu-Luen & Zhu, Xiaoyu & Hu, Xianbiao & Chiu, Yi-Chang, 2018. "The use of context-sensitive insurance telematics data in auto insurance rate making," Transportation Research Part A: Policy and Practice, Elsevier, vol. 113(C), pages 243-258.
    13. Henckaerts, Roel & Antonio, Katrien, 2022. "The added value of dynamically updating motor insurance prices with telematics collected driving behavior data," Insurance: Mathematics and Economics, Elsevier, vol. 105(C), pages 79-95.
    14. Meng, Shengwang & Gao, Yaqian & Huang, Yifan, 2022. "Actuarial intelligence in auto insurance: Claim frequency modeling with driving behavior features and improved boosted trees," Insurance: Mathematics and Economics, Elsevier, vol. 106(C), pages 115-127.
    15. Liu, Zhenkun & Jiang, Ping & De Bock, Koen W. & Wang, Jianzhou & Zhang, Lifang & Niu, Xinsong, 2024. "Extreme gradient boosting trees with efficient Bayesian optimization for profit-driven customer churn prediction," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Omid Ghaffarpasand & Mark Burke & Louisa K. Osei & Helen Ursell & Sam Chapman & Francis D. Pope, 2022. "Vehicle Telematics for Safer, Cleaner and More Sustainable Urban Transport: A Review," Sustainability, MDPI, vol. 14(24), pages 1-20, December.
    2. Meng, Shengwang & Gao, Yaqian & Huang, Yifan, 2022. "Actuarial intelligence in auto insurance: Claim frequency modeling with driving behavior features and improved boosted trees," Insurance: Mathematics and Economics, Elsevier, vol. 106(C), pages 115-127.
    3. Shengkun Xie, 2024. "Analyzing the Influence of Telematics-Based Pricing Strategies on Traditional Rating Factors in Auto Insurance Rate Regulation," Mathematics, MDPI, vol. 12(19), pages 1-23, October.
    4. McDonnell, Kevin & Sheehan, Barry & Murphy, Finbarr, 2026. "Bridging transparency in insurance claims prediction: A comparative study of explainable AI and traditional linear models using vehicle telematics data," Technological Forecasting and Social Change, Elsevier, vol. 223(C).
    5. Francis Duval & Jean‐Philippe Boucher & Mathieu Pigeon, 2023. "Enhancing claim classification with feature extraction from anomaly‐detection‐derived routine and peculiarity profiles," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 90(2), pages 421-458, June.
    6. Donatella Porrini & Giulio Fusco & Cosimo Magazzino, 2020. "Black boxes and market efficiency: the effect on premiums in the Italian motor-vehicle insurance market," European Journal of Law and Economics, Springer, vol. 49(3), pages 455-472, June.
    7. Catalina Bolancé & Montserrat Guillen & Ana M. Pérez-Marín & Anna-Patrícia Orteu, 2024. "Difference-in-Difference models to estimate causal effects on auto insurers behavior," IREA Working Papers 202411, University of Barcelona, Research Institute of Applied Economics, revised Feb 2024.
    8. Ramon Alemany & Catalina Bolancé & Roberto Rodrigo & Raluca Vernic, 2020. "Bivariate Mixed Poisson and Normal Generalised Linear Models with Sarmanov Dependence—An Application to Model Claim Frequency and Optimal Transformed Average Severity," Mathematics, MDPI, vol. 9(1), pages 1-18, December.
    9. Alfiero, Simona & Battisti, Enrico & Ηadjielias, Elias, 2022. "Black box technology, usage-based insurance, and prediction of purchase behavior: Evidence from the auto insurance sector," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    10. Wang, Lei & Wang, Xinyu & Zhao, Zhongchao, 2024. "Mid-term electricity demand forecasting using improved multi-mode reconstruction and particle swarm-enhanced support vector regression," Energy, Elsevier, vol. 304(C).
    11. Liu, Zhenkun & Zhang, Ying & Abedin, Mohammad Zoynul & Wang, Jianzhou & Yang, Hufang & Gao, Yuyang & Chen, Yinghao, 2024. "Profit-driven fusion framework based on bagging and boosting classifiers for potential purchaser prediction," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).
    12. Montserrat Guillen & Jens Perch Nielsen & Ana M. Pérez‐Marín, 2021. "Near‐miss telematics in motor insurance," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 88(3), pages 569-589, September.
    13. Rahman, Shimanto & Janssens, Bram & Bogaert, Matthias, 2025. "Profit-driven pre-processing in B2B customer churn modeling using fairness techniques," Journal of Business Research, Elsevier, vol. 189(C).
    14. Loeys, Stijn & Boute, Robert N. & Antonio, Katrien, 2025. "The use of IoT sensor data to dynamically assess maintenance risk in service contracts," European Journal of Operational Research, Elsevier, vol. 324(2), pages 454-465.
    15. Usama Arshad & Gohar Khan & Fawaz Khaled Alarfaj & Zahid Halim & Sajid Anwar, 2025. "Q-ensemble learning for customer churn prediction with blockchain-enabled data transparency," Annals of Operations Research, Springer, vol. 353(2), pages 607-633, October.
    16. Feng, Yi & Yin, Yunqiang & Wang, Dujuan & Ignatius, Joshua & Cheng, T.C.E. & Marra, Marianna & Guo, Yihan, 2024. "Enhancing e-commerce customer churn management with a profit- and AUC-focused prescriptive analytics approach," Journal of Business Research, Elsevier, vol. 184(C).
    17. Jaiswal, Rachana & Gupta, Shashank & Tiwari, Aviral Kumar, 2024. "Big data and machine learning-based decision support system to reshape the vaticination of insurance claims," Technological Forecasting and Social Change, Elsevier, vol. 209(C).
    18. Montserrat Guillen & Ana M. Pérez-Marín & Manuela Alcañiz, 2020. "Risk reference charts for speeding based on telematics information," IREA Working Papers 202003, University of Barcelona, Research Institute of Applied Economics, revised Apr 2020.
    19. Marian Reiff & Erik Šoltés & Silvia Komara & Tatiana Šoltésová & Silvia Zelinová, 2022. "Segmentation and estimation of claim severity in motor third-party liability insurance through contrast analysis," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 17(3), pages 803-842, September.
    20. Yiwen Zhou & Fengxiang Guo & Simin Wu & Wenyao He & Xuefei Xiong & Zheng Chen & Dingan Ni, 2022. "Safety and Economic Evaluations of Electric Public Buses Based on Driving Behavior," Sustainability, MDPI, vol. 14(17), pages 1-17, August.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:insuma:v:124:y:2025:i:c:s0167668725000794. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/505554 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.