Authors: Mrs. Ch. Veera Gayatri, Palivela Geethasri, Kothapalli Venkannababu, Madhavarapu Chandhra Sekhar Sri Sai, A Lakshmi Chinmayi, Anupoju Sainadh
Abstract: The rapid growth of e-commerce platforms has increased the need for intelligent pricing strategies that can adapt to continuously changing market conditions. Traditional pricing methods used in online retail are often static and rely heavily on historical data analysis, making them inefficient in responding to dynamic market factors such as customer demand, competitor pricing, and seasonal trends. In modern digital marketplaces, businesses generate large volumes of transactional and behavioural data, which creates opportunities for applying machine learning techniques to improve pricing decisions. This study proposes a machine learning-enabled business intelligence framework for dynamic pricing optimization in e-commerce environments. The proposed system integrates data preprocessing, predictive modelling, and business intelligence analytics to support real-time pricing decisions. During the preprocessing stage, historical pricing data, market trends, competitor price information, and customer behavior patterns are collected and processed to improve data quality and consistency. Support Vector Machine (SVM) is employed as the primary machine learning algorithm due to its ability to handle complex and non-linear relationships within large datasets. The business intelligence component of the framework enables efficient data visualization, monitoring, and analysis of market conditions through interactive dashboards and analytical tools. This integration allows businesses to combine predictive insights from machine learning with data-driven business intelligence reports to determine optimal pricing strategies. The proposed system dynamically adjusts product prices by analyzing multiple influencing factors such as demand fluctuations, competitor behavior, and customer purchasing patterns. Experimental evaluation demonstrates that the integration of machine learning and business intelligence significantly improves pricing accuracy, market responsiveness, and decision-making efficiency. By enabling automated and adaptive pricing strategies, the proposed framework helps businesses maximize revenue, enhance competitiveness, and respond effectively to rapidly changing e-commerce environments.
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