EXAMINING THE ROLE OF BIG DATA AND PREDICTIVE ANALYTICS IN ENHANCING CUSTOMER EXPERIENCE AND PERSONALIZED FINANCIAL PRODUCTS

Abstract

The rapid growth of big data and predictive analytics has fundamentally transformed the way financial institutions design and deliver customer-centric services. This study empirically examines the role of big data and predictive analytics in enhancing customer experience and enabling personalized financial products through an experimental mixed-methods research approach. Large-scale transactional, behavioral, and demographic datasets were analyzed using advanced predictive modeling techniques to evaluate improvements in customer engagement, satisfaction, retention, and revenue performance relative to traditional rule-based systems. Quantitative results demonstrate that predictive analytics significantly improves model accuracy, product adoption rates, cross-selling effectiveness, and customer retention, while also reducing prediction error across iterative model development. Complementary qualitative insights further confirm that data-driven personalization enhances perceived service quality, trust, and customer satisfaction. The integrated findings indicate that financial institutions leveraging big data analytics can achieve superior decision-making and more precise personalization, leading to sustainable competitive advantage. The study contributes to the growing literature on data-driven financial innovation by providing empirical evidence of the strategic value of predictive analytics in improving customer experience and optimizing personalized financial offerings within modern digital financial ecosystems.