Machine Learning-Driven Insights into Digital Payment Systems for Vendor Network Expansion and Customer Retention

This study will investigate the effect of digital payment systems on vendor network expansion and customer retention in a mixed-method approach, covering both organized and unorganized vendors. The research uses statistical techniques and machine learning models to analyze the relationships between digital payment adoption, vendor growth, and customer loyalty. The study found that organized vendors, in terms of network expansion, benefit more from digital payment systems, while the availability and reliability of digital payment options significantly improve customer retention. This research also reveals vendor clusters based on revenue growth, frequency of digital payment usage, and transaction values; it also identifies rewards and security as key factors that influence customer preferences. This is the first study that applies sentiment analysis of customer feedback by applying an SVM classifier for the purpose of understanding user perceptions. The current study has certain limitations, including a low number of datasets and a low R-squared value; still, it points out valuable insights important to policymakers and financial service providers for the optimization of digital payment strategies toward sustainable and inclusive growth.