Research on the Construction of AI Driven Cross border E-commerce Precision Marketing System and Overseas User Conversion Efficiency
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Abstract
Enhancing overseas user conversion efficiency remains a major challenge for cross-border e-commerce due to cultural diversity, fragmented customer journeys, and rapidly changing market conditions. This study proposes an AI-driven precision marketing framework integrating knowledge graphs, transformer-based behavioral modeling, deep reinforcement learning, and federated learning. The framework enables localized multimodal content generation, dynamic user-intent recognition, real-time advertising optimization, and privacy-preserving conversion attribution. Experimental results demonstrate improvements of approximately 60% in click-through rate, 50% in conversion rate, 40 % in user lifetime value, and 50% in return on investment. The proposed system provides an effective solution for intelligent marketing optimization and offers methodological references for information recommendation, behavioral prediction, and intelligent decision-making networks.
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