The Evolution of Pricing Models in E-Commerce: From Dynamic Pricing to AI-Driven Price Optimization

Abstract

Abstract: This article explores the evolution of pricing models in e-commerce, focusing on the shift from traditional dynamic pricing to AI-driven price optimization. The study uses a narrative review methodology, examining relevant literature on e-commerce pricing strategies to provide a comprehensive understanding of how pricing models have adapted in response to technological advancements. The review includes an analysis of static pricing, dynamic pricing, and the latest AI-driven models, comparing their mechanisms, effectiveness, and implications for businesses. It delves into key influencing factors such as market demand, competitor pricing, and consumer behavior, and discusses how AI-driven pricing enhances decision-making through real-time optimization, customer segmentation, and predictive analytics. The findings highlight the advantages of AI over traditional dynamic pricing models, including its ability to provide personalized pricing, anticipate market trends, and operate efficiently across various business sizes. Additionally, the study addresses challenges associated with AI-driven pricing models, such as data privacy concerns, algorithmic bias, and legal and regulatory issues. Case studies of companies like Amazon and Uber illustrate successful implementation of both dynamic and AI-driven pricing models. The review also discusses future trends, such as the increasing role of big data and IoT in enhancing AI-driven models, the rise of personalized pricing, and the integration of blockchain to improve transparency and security in pricing. The article concludes that AI-driven pricing models offer significant competitive advantages for e-commerce businesses, but companies must carefully navigate the ethical and regulatory challenges to fully realize their potential in the evolving digital marketplace.