REAL TIME DYNAMIC PRICING IN ONLINE RETAIL USING PREDICTIVE MACHINE LEARNING MODELS

Abstract

Predictive machine learning algorithms are implemented by online businesses to promptly adjust prices in response to market trends, inventories, competitor prices, user behavior, and demand. Sophisticated algorithms, including regression models, decision trees, ensemble approaches, and deep learning networks, utilize real-time data, including browsing patterns, seasonal impacts, and promotion reactions, in conjunction with historical sales data to rapidly determine pricing and evaluate demand elasticity. This data-driven approach enables retailers to enhance their comprehension of their consumers, boost sales, and preserve their competitiveness in digital markets that are perpetually evolving. Automated pricing systems enable price customization, reduce human labor, and increase efficiency, all while scaling large product libraries. Strategic innovations, such as dynamic pricing that is predicated on predictive machine learning, improve the responsiveness, profitability, and consumer engagement of the e-commerce ecosystem.