DYNAMIC PRICING IN E-COMMERCE USING PREDICTIVE MACHINE LEARNING TECHNIQUES

Abstract

Dynamic pricing has emerged as a critical strategy in modern e-commerce platforms, enabling businesses to adjust product prices in real time based on market demand, customer behavior, competition, and inventory levels. The growing complexity of online marketplaces necessitates intelligent pricing mechanisms that not only maximize profitability but also maintain customer satisfaction and trust. This paper explores the application of machine learning techniques in developing dynamic pricing models that effectively balance these dual objectives. Various algorithms, including regression models, decision trees, reinforcement learning, and deep learning approaches, are analyzed for their ability to capture nonlinear demand patterns and customer sensitivity to price changes. The study proposes a hybrid machine learning framework that integrates predictive analytics with adaptive pricing strategies to optimize revenue while minimizing customer churn. Key factors such as historical sales data, competitor pricing, seasonal trends, and user behavior are incorporated to enhance model accuracy. Experimental results demonstrate that machine learning-driven pricing strategies outperform traditional rule-based methods by improving profit margins and customer engagement simultaneously. Furthermore, the framework emphasizes fairness and transparency in pricing to avoid negative customer perceptions. The findings highlight the importance of intelligent pricing systems in achieving sustainable growth in e-commerce ecosystems. This research contributes to the development of advanced pricing models that align business objectives with customercentric approaches, ensuring long-term competitiveness in dynamic digital markets.