Framework demonstrates improved pricing accuracy in e-commerce by integrating machine learning and business intelligence analytics, suggesting enhanced competitiveness.
Key Points
The central research aim is to develop a dynamic pricing system that adapts based on real-time market data and influences.
Proposed a machine learning-enabled framework for dynamic pricing optimization.
Integrated data preprocessing, predictive modeling, and business intelligence analytics.
Utilized Support Vector Machine (SVM) as the primary algorithm for analyzing complex data relationships.
Collected and processed historical pricing data, market trends, and customer behavior patterns.
Demonstrated significant improvements in pricing accuracy and market responsiveness.
Enhanced decision-making efficiency through automated pricing adjustments.
Showcased that the framework maximizes revenue by responding effectively to market changes.