Dynamic Pricing Strategies of Crowdsourced Logistics Service Providers from a Game Theory Perspective

With the continuous development of the sharing economy, crowdsourced logistics has emerged as an innovative business operation model, attracting increasing attention. This paper explores the dynamic pricing strategies of crowdsourced logistics service providers from a game theory perspective. By constructing a pricing game model that considers factors such as service quality, market demand, price sensitivity and natural environment, it analyzes how different platforms can maximize their profits and increase their market share in a competitive environment. The study found that the implementation of dynamic pricing and differentiated pricing strategies based on market changes can effectively enhance the market adaptability and profit space of the platform. In addition, this article proposes to introduce artificial intelligence and big data technology to predict and optimize real-time orders, user behavior and environmental factors, which will help to achieve more intelligent price adjustments. In order to cope with demand fluctuations, reasonable subsidies and incentive mechanisms also play a key role in stabilizing the rider team and improving service efficiency. The study found that through real-time price adjustments and differentiated pricing strategies, crowdsourcing logistics service providers can better adapt to market fluctuations and thus improve economic benefits.