Machine Learning-Enabled Business Intelligence For Dynamic Pricing Strategies In E-Commerce

The ever-evolving landscape of e-commerce makes dynamic pricing strategies an absolute necessity for businesses that want to maximise their earnings and keep a leading edge in their respective industries. This study was conducted with the intention of determining whether or not the application of machine learning (ML) and business intelligence (BI) could result in the development of dynamic pricing strategies that are more successful. Many companies have been unsuccessful because they relied on pricing techniques that had become obsolete as a result of the constantly shifting nature of the digital market. This study addresses the need for a more dynamic and data-driven pricing approach in online environments, thereby bridging a gap in the existing body of research on the topic. Although machine learning has been shown to be helpful in a variety of business contexts, its potential for dynamic pricing in online stores has not yet been completely explored through its integration with business intelligence. This is despite the fact that machine learning has been valuable in a number of business situations. The currently available research does not provide a comprehensive understanding of the synergies that can be achieved by combining ML and BI in pricing optimisation. The Support Vector Machine (SVM), a machine learning technique, was selected as the primary tool for this study because of its ability to deal with intricate and nonlinear interactions included within enormous datasets. Combining technologies that are used for business intelligence in order to collect, process, and present important data results in the creation of a sophisticated framework that can be used to make decisions regarding pricing in real time. According to the findings, having a business intelligence system that is empowered with machine learning significantly improves a company's capacity to accurately price its products and services and to respond rapidly to changes in the market. Price decisions can be made that are both more precise and more sensitive to nuances thanks to the adaptability of the SVM model to changing market conditions.