AI-Driven Business Intelligence: Machine Learning-Powered Dynamic Pricing Strategies for E-Commerce Optimization

Abstract-The rapidly evolving e-commerce landscape demands dynamic pricing strategies to maximize revenue and maintain a competitive edge. This study examines the integration of machine learning (ML) and business intelligence (BI) to enhance pricing strategies, addressing the shortcomings of outdated models in adapting to digital market shifts. While ML has proven valuable in various business applications, its potential for dynamic pricing in e-commerce remains underexplored, particularly when combined with BI. Existing research lacks a comprehensive analysis of how these technologies can work together for pricing optimization. To bridge this gap, the study employs the Support Vector Machine (SVM) algorithm, known for handling complex and nonlinear relationships in large datasets. By leveraging BI tools to collect, process, and analyze crucial data, the approach establishes a real-time pricing framework. The findings reveal that ML-powered BI systems significantly enhance a company’s ability to set accurate prices and swiftly respond to market fluctuations. The adaptability of the SVM model ensures pricing decisions are both precise and responsive to dynamic market conditions, leading to a more effective and competitive pricing strategy.