A study on the application of artificial intelligence in personalized Go-to-market strategy in retail industry

In order to improve the accuracy and efficiency of personalized offer strategies in the retail industry, a dynamic pricing and personalized recommendation system is constructed based on multi-source data fusion and intelligent decision-making models. Analyzing the homogenization problem of traditional offer strategies, deep learning and reinforcement learning algorithms are used to optimize the construction of user profiles, the prediction of purchasing behavior and the generation of offer strategies. The results show that the intelligence-driven personalized Go-to-market strategy effectively improves the user conversion rate and customer unit price, optimizes the inventory turnover efficiency, and enhances the ability to accurately deploy marketing resources. Further research can focus on data privacy protection, cross-platform adaptability and computational cost optimization to enhance the stability and value of the strategy.