Developing Digital Marketing Optimization Tools for Micro-Loans in Financial Markets

This study explores the optimization of digital marketing tools for micro-loan services, focusing on Yu'ebao, China’s leading digital wealth management platform, as a case study. By using a mixed-methods approach—incorporating Data Envelopment Analysis (DEA), logistic regression, and qualitative interviews—the research investigates the influence of digital marketing strategies on user adoption, trust, and engagement. The study applies the Technology Acceptance Model (TAM) and the 5A Accessibility Framework to analyze user behavior and platform interaction. The findings reveal that digital marketing strategies decreased customer acquisition costs by 54% and increased loan approval efficiency by 42%. However, digital adoption remains lower in rural areas, largely due to digital literacy gaps and risk perception. The study highlights that factors such as platform trust, perceived affordability, and user financial literacy are significant drivers of adoption. A DEA-based segmentation model classifies users by efficiency, offering valuable insights into personalized micro-loan services through algorithmic marketing, user data analytics, and ecosystem-based service delivery. The study addresses challenges in data governance, privacy concerns, and the adaptation of strategies across markets, offering both theoretical and practical contributions to the digital financial inclusion space.