Integrating AI and ML for Dynamic Pricing Strategies: Innovations in Marketing Analytics and Revenue Management

The focus of the research is the difficulty of using AI and machine learning to optimize dynamic pricing techniques. The paper provides a thorough method for dynamic pricing using K-Means clustering for client segmentation, reinforcement learning for real-time price changes, and LSTM networks for demand forecasting. Compared to previous models, the suggested model produced a 30% boost in revenue, an 85% customer satisfaction rating, a 28% market share, and a 50% decrease in average response time after being trained and tested on a large retail dataset. These outcomes show how much better the model can perform than more conventional pricing techniques. Better pricing accuracy and response to market circumstances are other enhancements that result in improved client experiences and increased profitability. According to the study's conclusion, AI-driven dynamic pricing models can completely transform pricing strategies across a range of sectors and provide a scalable and practical answer for companies looking to maximize profits while maintaining a high level of customer happiness.