Analysis of AI Customer Service Application Strategy of E-commerce Platform based on Hotelling Model

Abstract

This study employs the Hotelling model and backward induction to analyze the decision-making processes of providers and e-commerce platforms. The findings reveal that AI customer service exerts distinct influences on product pricing, market demand, and profits for both high-quality and low-quality e-commerce platforms. Pricing is subject to various constraints. In terms of market demand, the demand for low-quality e-commerce platforms consistently lags behind that of high-quality platforms. Regarding charging standards, both platforms are inclined to accept the service when the charge is low; only high-quality e-commerce platforms accept it at a medium charge; neither accepts it when the charge is high. The provider's service is shaped by cost differences and application levels: when the cost difference is significant and the application level of high-quality e-commerce is elevated, the provider demonstrates greater willingness to enhance service quality. When the gap in application levels between the two types of platforms widens, the provider tends to invest more resources; however, an increase in the application level of low-quality e-commerce platforms may inhibit the provider's investment.