Optimizing Multi-Channel AI Advertising Budget Allocation and ROI Prediction in Cross-Border E-Commerce: A Machine Learning Approach for North American and Southeast Asian Markets

Abstract

The proliferation of Artificial Intelligence Generated Content (AIGC) has significantly reconfigured the digital marketing landscape, yet cross-border e-commerce enterprises continuously encounter multi-channel budget fragmentation and highly volatile Return on Investment (ROI) trajectories. This paper constructs a dynamic allocation framework integrating advanced machine learning architectures to address the non-linear operational feedback loops inherent in cross-border advertising across distinct regional jurisdictions, specifically the North American and Southeast Asian markets. Utilizing empirical desensitized operational data, we developed an incorporates time-series deep learning algorithms to predict channel-specific ROI while concurrently employing reinforcement learning agents to simulate real-time budget optimization under stochastic market fluctuations. The empirical trajectory was not entirely linear; significant data volatility and algorithmic drift emerged when adjusting for platform-specific policy shifts, necessitating recursive hyperparameter calibrations. The empirical findings indicate that the proposed model, to some extent, enhances the precision of multi-channel resource distribution, although its explanatory power exhibits geographical heterogeneity due to local digital infrastructures and cultural variations. Competing interpretations of the empirical anomalies suggest that algorithmic performance may remain contingent upon unobserved macroeconomic noise rather than purely endogenous operational variables. Considering the inherent complexity of platform black-boxes, further research is required to unpack the boundary conditions of automated governance. This study contributes to the transitioning paradigm of data-driven global marketing architectures.