AI-Driven Business Intelligence for Adaptive Pricing Strategies in E-Commerce

Authors- M. V. Rajesh, Nelluri Sai Jyothi, Pynda Satya Harshitha, Palla Rakesh, V V S R K Kamalesh Janapa, Kola Uday Surya Prakash

Abstract-In the rapidly evolving landscape of e-commerce, businesses must adopt dynamic pricing strategies to maximize revenue and maintain a competitive edge. This study explores the integration of machine learning (ML) and business intelligence (BI) in optimizing dynamic pricing, addressing the limitations of traditional pricing models that struggle to adapt to shifting digital market conditions. Despite the proven benefits of ML in various business applications, its full potential in online pricing remains underexplored, particularly when combined with BI. Existing research lacks a comprehensive understanding of how ML and BI can work synergistically to enhance pricing decisions. To bridge this gap, this study employs the Support Vector Machine (SVM) algorithm, chosen for its effectiveness in handling complex, nonlinear interactions within large datasets. By leveraging BI technologies to gather, process, and analyze critical data, this study establishes a robust framework for real-time pricing decisions. The findings indicate that integrating ML-driven BI systems enhances pricing accuracy and enables businesses to respond swiftly to market fluctuations. The adaptability of the SVM model allows for precise, context-aware pricing decisions, ultimately strengthening a company’s ability to navigate the dynamic e-commerce environment.

DOI: /10.61463/ijset.vol.13.issue2.228