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AI-Driven Dynamic Pricing Optimization for U.S. SMB Fleet and Rental Operations

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  • Wang, Ziru

Abstract

This research investigates the application of artificial intelligence (AI) in optimizing dynamic pricing strategies for small and medium-sized business (SMB) fleet and rental operations within the United States. The study develops a practical system employing tree-based machine learning models (e.g., Gradient Boosting and Random Forest) to predict optimal prices by integrating historical demand, competitor pricing, seasonality, and vehicle characteristics. The system's effectiveness is evaluated through simulation-based experiments using real-world data from U.S. SMB fleet and rental companies. The results demonstrate significant improvements, achieving an average revenue increase of 18.5% compared to static cost-plus pricing and a 7.2% improvement in fleet utilization rate. This research contributes to the growing body of knowledge on AI applications in business by providing an empirically validated, practical framework for SMBs seeking to leverage data-driven methods for pricing optimization and operational efficiency.

Suggested Citation

  • Wang, Ziru, 2026. "AI-Driven Dynamic Pricing Optimization for U.S. SMB Fleet and Rental Operations," Financial Economics Insights, Scientific Open Access Publishing, vol. 3(1), pages 74-84.
    • Handle: RePEc:axf:feiaaa:v:3:y:2026:i:1:p:74-84
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