Intelligent optimization and data-driven modeling of marketing strategies for cross-border e-commerce platforms

As global cross-border e-commerce continues to expand rapidly, competition within the platform market has intensified, making traditional marketing strategies inadequate for keeping up with the fast-evolving consumer demands and market conditions. Cross-border e-commerce, a key sector in international trade, plays a crucial role in driving economic development, with key functions such as operations, customer service, marketing, and logistics requiring the frequent generation of content like copy, images, and videos. The challenge now is how to leverage advanced intelligent optimization and data-driven models to improve the marketing effectiveness of cross-border e-commerce platforms. This study explores marketing strategies for cross-border e-commerce platforms powered by intelligent optimization and data-driven models, aiming to propose corresponding optimization paths. By utilizing technologies such as big data analytics, machine learning, and artificial intelligence, platforms can more accurately understand consumer needs, create personalized marketing strategies, and implement precise advertising and product recommendations. Additionally, the research delves into the optimization of multi-channel integration and marketing strategies to enhance the platform's market coverage and improve user satisfaction. Through specific case studies, this research demonstrates the implementation and effect evaluation of intelligent marketing strategies, providing practical solutions for marketing optimization. Ultimately, this study offers both a theoretical foundation for intelligent marketing in the cross-border e-commerce sector and a new direction for future research and development in this field.