Prediction and Optimization for Multi-Product Marketing Resource Allocation in Cross-Border E-Commerce

In cross-border e-commerce, effective marketing resource allocation is crucial due to the complexity introduced by diverse product categories, regional differences, and competition among category managers. Current methods either overlook these constraints or fail to enforce them efficiently due to computational challenges. We propose a two-stage optimization framework that integrates predictive models with constrained optimization. In the first stage, predictive models estimate user purchase probabilities and determine upper bounds on product-specific sending volumes. In the second stage, the resource allocation problem is formulated as a large-scale integer programming model, which is then transformed into a minimum-cost flow problem to ensure computational efficiency while preserving solution optimality. Experiments on real-world data show that our framework significantly outperforms baseline strategies, achieving a 14.48% increase in order volume and revenue improvements ranging from 0.19% to 43.91%. The minimum-cost flow algorithm consistently outperforms the greedy approach, especially in large-scale instances. The proposed framework enables scalable and constraint-compliant marketing resource allocation in cross-border e-commerce. It not only improves sales performance but also ensures strict adherence to operational constraints, making it well-suited for large-scale commercial deployment.