User Perceptions of Usage-Based Insurance: Benefits, Challenges, and Impacts on Driving Behavior

Usage-based insurance (UBI) programs have emerged as potential tools for promoting safe driving behaviors and enhancing road safety. These programs utilize advanced technology and data-driven methods to track the driving behaviors of their users. Understanding the benefits and challenges users face when interacting with these programs requires a systematic analysis of user feedback. This study aims to evaluate user feedback on UBI programs implemented by US insurance companies to identify benefits and challenges facing users of the program. The study analyzes reviews collected from Google Play Store and Apple App Store between 2019 and 2024. The study utilizes logistic regression and text mining techniques to accomplish the study objectives. Logistic regression results show speeding behavior to be statistically significant compared with other driving behaviors. Furthermore, text analysis and thematic-analysis-associated statistical quantifiers evaluated reviews associated with speeding behavior. The findings of the analysis reveal positive feedback, indicating that programs are user-friendly applications that help to improve driving behavior. Text mining analysis revealed that each of the programs had specific recommended improvements that were identified from the user reviews. The improvements were associated with improving location service issues, causing inaccurate citations, a need for better awareness, and updating Google Maps integration to address outdated speed limits. These findings offer actionable insights for enhancing UBI programs by addressing usability gaps, refining application functionalities, and improving behavioral monitoring accuracy. Strengthening these areas can improve user satisfaction, promote safer driving habits, and advance UBI’s role in road safety.