Ai And Dynamic Pricing in E-Commerce: Strategies for Maximizing Revenue and Customer Value

Abstract

Dynamic pricing, powered by Artificial Intelligence (AI), has emerged as a transformative strategy in e-commerce, enabling businesses to optimize revenue and enhance customer value by adjusting prices in real-time. This paper explores the integration of AI in dynamic pricing, where advanced algorithms analyze vast datasets including supply and demand trends, competitor prices, customer preferences, and purchasing behavior to tailor prices dynamically. By leveraging machine learning models, AI can continuously refine pricing strategies, maximizing profitability while maintaining customer satisfaction and loyalty. Key techniques, such as demand forecasting, price elasticity analysis, and personalized pricing, are examined to illustrate their role in achieving optimal price points. The study also considers ethical considerations and consumer perception, which are critical to balancing profit objectives with customer trust. Findings suggest that AI-driven dynamic pricing not only helps businesses respond rapidly to market fluctuations but also enables a personalized shopping experience, fostering a mutually beneficial relationship between e-commerce platforms and their customers. This research contributes to a deeper understanding of AI's capabilities in price optimization and offers insights for e-commerce practitioners aiming to implement effective, customer-centered dynamic pricing strategies.