Explainable AI-Driven Content Optimization for 2D Character Merchandise Marketing: A Causal Feature Attribution and Attention-Guided Framework

Abstract

This research propose an explainable AI-driven framework for optimizing 2D character merchandise marketing content, addressing the critical gap between conventional heuristic-driven strategies and data-driven decision-making. The proposed system integrates causal feature attribution and attention-guided generation to systematically model the relationship between content attributes and user engagement dynamics. At its core, a feature attribution engine quantifies the impact of visual and textual elements using Shapley values, while a vision-language transformer prioritizes high-attention regions during content creation. Furthermore, a Bayesian optimization loop iteratively refines marketing strategies based on real-time feedback, dynamically adjusting design parameters and posting schedules. The framework uniquely bridges interpretable AI with creative workflows, enabling marketers to make quantifiable adjustments rather than relying on intuition. Our implementation leverages state-of-the-art multimodal transformers and accelerated Shapley value approximations, ensuring scalability without sacrificing interpretability. Experimental results demonstrate that the system outperforms traditional methods in engagement metrics, particularly in click-through rates and user retention. The novelty lies in its closed-loop feedback mechanism, where explainable insights directly parametrize content generation tools, fostering a symbiotic relationship between machine intelligence and human creativity. This work contributes to both the AI and marketing communities by providing a transparent, adaptive solution for content optimization in the rapidly growing 2D character merchandise industry.