Data-Driven Strategy for Merchant Incentive Optimization in Digital Payment Ecosystems

In the rapidly evolving digital payment ecosystem, optimizing merchant incentives has become a strategic necessity for increasing transaction volume, improving platform loyalty, and fostering sustainable customer engagement. Traditional incentive schemes often rely on heuristic or fixed-rate models that fail to account for the dynamic and heterogeneous nature of merchant behavior. To address these limitations, this study introduces a novel data-driven framework that employs graph-based representation learning to model and analyze complex transactional interdependencies among merchants and consumers. By capturing structural and behavioral similarities through graph neural networks (GNNs), the proposed approach enables precise prediction of each merchant’s sensitivity to diverse incentive strategies. The model integrates transaction frequency, geographical proximity, and customer overlap to generate interpretable embeddings that guide optimized budget distribution under financial constraints. Through an integrated optimization layer, incentives are allocated based on predicted responsiveness, ensuring that marketing expenditures are directed toward high-impact segments. Real-world experiments conducted on large-scale digital payment datasets validate the framework’s effectiveness, demonstrating substantial improvements in merchant participation rates, transaction growth, and cost efficiency compared to baseline regression and deep learning models. The findings highlight the potential of combining machine learning, network science, and marketing analytics to design more adaptive and data-intelligent promotional systems, thereby paving the way for scalable and targeted incentive management in next-generation digital financial platforms.