E-commerce Management and AI-Based Dynamic Pricing Revenue Optimization Strategies

Abstract

This research focuses on how e-commerce managers can use artificial intelligence in dynamic pricing to help them improve on their revenues as well as integrate the change into their management systems. The above algorithms are set on a dataset that contains prior sales data, customer details, competitor price details, and trends in the market. These algorithms show that they can correctly assign best prices and also adapt them according to changes in the market condition. Comparative analysis reveals that Reinforcement Learning achieves the lowest prediction errors (MAE: 1. 5: 0. Lastly, feature importance analysis revealed that the proposed Power Laws have the highest adaptability (0. The study therefore suggests how AI trend in e-commerce has the possibilities of revolutionizing revenue enhancement measures as well as boosting the overall consumership. As for the future research prospects, there is a need to work on improving the AI models used, improving the approach to integration data sources, and shifting towards utilizing combined approaches in an effort to build on current improvements in the e-commerce pricing strategies.