The rapid expansion of digital financial services has transformed payment ecosystems, creating opportunities for financial inclusion, operational efficiency, and sustainable economic growth. Nevertheless, digital payment platforms continue to encounter challenges involving customer acquisition, user retention, transaction abandonment, trust, and inconsistent adoption patterns. This study examines how data-driven personalization, consumer behavior analysis, and strategic product growth models can optimize digital payment adoption and strengthen platform performance. It explores the integration of behavioral analytics, predictive modeling, customer segmentation, and personalized engagement strategies to identify adoption barriers, anticipate user preferences, and improve transaction experiences. Particular attention is given to behavioral indicators, including transaction frequency, payment preferences, engagement patterns, perceived security, and customer lifetime value. The study further considers how experimentation frameworks, conversion optimization, and lifecycle-based growth strategies can support sustained adoption and monetization. By integrating technological, behavioral, and strategic perspectives, the proposed framework highlights the importance of responsible data utilization, privacy protection, and consumer trust. The study contributes to understanding how evidence-based product decisions can enhance payment accessibility, customer engagement, competitive advantage, and long-term digital financial ecosystem sustainability across diverse consumer markets and evolving technological environments.