AI-DRIVEN DYNAMIC PRICING MODELS IN COMPETITIVE MARKETS: A COMPARATIVE ANALYSIS OF ALGORITHMS

Abstract

Dynamic pricing has emerged as a vital strategy for businesses operating in competitive markets, allowing them to adjust prices in real time in response to various factors like demand fluctuations, competitor pricing, and market trends. With the advent of artificial intelligence (AI) and machine learning (ML), firms can now leverage advanced algorithms to optimize pricing strategies dynamically, maximizing revenue and market share. This research paper explores three AI-based algorithms—reinforcement learning, neural networks, and decision trees—evaluating their effectiveness in different competitive market scenarios. Using simulations, real-world case studies, and performance metrics such as revenue generation, adaptability, and computational efficiency, this study comprehensively analyzes how these algorithms perform in varying market conditions. The findings reveal the strengths and limitations of each model, offering insights for businesses aiming to optimize their dynamic pricing strategies while balancing risks and opportunities.