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

Abstract

In the rapidly evolving digital payment landscape, optimizing merchant incentives is crucial for boosting transaction volume and customer engagement. This research introduces a novel data-driven approach that leverages graph-based learning to analyze merchant behavior and predict their sensitivity to various incentive strategies. By modeling transaction patterns and customer interactions, the proposed framework effectively allocates marketing budgets to maximize commercial objectives. Real-world experiments demonstrate that this method not only enhances merchant participation but also reduces marketing costs, paving the way for more efficient and targeted promotional campaigns.