Research on Application of AI and Machine Learning in Precision Marketing of Cross-border E-commerce

In the context of increasingly fierce global cross-border e-commerce competition, the industry is facing core challenges such as homogenization competition, logistics bottleneck and diversification of consumer demand. In order to break through the dilemma, this study takes JOYSTAR, a children’s bicycle brand in China, as an example, and proposes a data-driven intelligent analysis framework. Firstly, the framework integrates and processes the global operational data from multiple platforms, and then constructs a customer life cycle value prediction model to quantitatively evaluate the user value and accurately identify the middle-value user group as the core basic disk. At the same time, this study uses natural language processing technology to conduct emotional analysis and theme mining on large-scale user comments, and automatically extracts key features about product quality and service experience. The analysis results show that JOYSTAR has advantages in data insight and core products, but its calculation model also reveals obvious shortcomings in product experience, high-end market competitiveness and supply chain resilience. Finally, this study demonstrates the joint analysis method based on CLV modeling and NLP, which provides a reproducible technical path for cross-border e-commerce enterprises to realize accurate operation and strategic decision.