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Sensor Data and Behavioral Tracking: Does Usage-Based Auto Insurance Benefit Drivers?

Author

Listed:
  • Miremad Soleymanian

    (Sauder School of Business, University of British Columbia, Vancouver, British Columbia V6T 1Z2, Canada)

  • Charles B. Weinberg

    (Sauder School of Business, University of British Columbia, Vancouver, British Columbia V6T 1Z2, Canada)

  • Ting Zhu

    (Krannert School of Management, Purdue University, West Lafayette, Indiana 47907)

Abstract

Usage-based insurance (UBI) is a recent auto insurance innovation that enables insurers to collect individual-level driving data, provide feedback on driving performance, and offer individually targeted price discounts based on each consumer’s driving behavior. Using individual driving behavior (from sensor data) and other information for UBI adopters, we estimate the relationship from being enrolled and monitored (for up to 26 weeks) in the UBI program and changes in the driving behavior of UBI customers. The key results of our analysis show that after UBI adoption, the UBI users improve the safety of their driving, providing a meaningful benefit for the individual driver, the insurer, and society as a whole. While UBI customers decrease their daily average hard-brake frequency by an average of 21% after six months, their mileage driven does not decrease comparing week 26 to week 1. We also find heterogeneous effects across different demographic groups. For example, younger drivers improve their UBI scores more than older drivers after UBI adoption, and females show more improvement than males. Furthermore, we find evidence that negative feedback and economic incentives correlate with greater improvement in driving behavior. Our results suggest that by sharing private consumer information with the insurer, UBI can benefit consumers who become better drivers, as well as the entire society from improved road safety.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:ormksc:v:38:y:2019:i:1:p:21-43
    DOI: 10.1287/mksc.2018.1126
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    References listed on IDEAS

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    Cited by:

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    5. Vivek Choudhary & Masha Shunko & Serguei Netessine & Seongjoon Koo, 2022. "Nudging Drivers to Safety: Evidence from a Field Experiment," Management Science, INFORMS, vol. 68(6), pages 4196-4214, June.
    6. Giulio Cornelli & Jon Frost & Saurabh Mishra, 2023. "Artificial intelligence, services globalisation and income inequality," BIS Working Papers 1135, Bank for International Settlements.
    7. Siqi Liu & Bhoomija Ranjan & Benjamin Reed Shiller, 2020. "Are Coarse Ratings Fine? Applications to Crashworthiness Ratings," Working Papers 132, Brandeis University, Department of Economics and International Business School.
    8. Vikas Chauhan & Rohit Joshi & Vipin Choudhary, 2024. "Understanding intention to adopt telematics-based automobile insurance in an emerging economy: a mixed-method approach," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 29(3), pages 1017-1036, September.
    9. Coyne, Rory & Hanlon, Michelle & Smeaton, Alan & Corcoran, Peter & Walsh, Jane C, 2024. "Understanding drivers’ perspectives on the use of driver monitoring systems during automated driving: Findings from a qualitative focus group study," OSF Preprints cdgkx, Center for Open Science.
    10. Anindya Ghose & Beibei Li & Meghanath Macha & Chenshuo Sun & Natasha Ying Zhang Foutz, 2020. "Trading Privacy for the Greater Social Good: How Did America React During COVID-19?," Papers 2006.05859, arXiv.org.
    11. Ming-Hui Huang & Roland T. Rust, 2021. "A strategic framework for artificial intelligence in marketing," Journal of the Academy of Marketing Science, Springer, vol. 49(1), pages 30-50, January.
    12. Tesary Lin, 2022. "Valuing Intrinsic and Instrumental Preferences for Privacy," Marketing Science, INFORMS, vol. 41(4), pages 663-681, July.

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