Journal of Economics Intelligence And Technology

Abstract: In contemporary digital markets, product managers face increasing pressure to achieve rapid and sustainable growth amid expanding data complexity and competitive uncertainty. This study presents an integrated framework that combines visual analytics and machine learning to support scalable, growth-oriented product management. Multi-source product data capturing user acquisition, engagement, retention, and monetization were analyzed using interactive visual exploration and advanced predictive modeling. Visual analytics facilitated the identification of temporal trends, behavioral heterogeneity, and multidimensional interactions among key growth variables, while machine learning models, particularly ensemble-based approaches, enabled accurate prediction of retention, churn, and revenue outcomes. The results reveal that user experience–centric factors, including interaction depth, onboarding completion, and feature adoption, are the dominant drivers of sustainable growth, whereas pricing strategies yield diminishing returns in the absence of strong engagement. By integrating interpretability with predictive rigor, the proposed framework enhances strategic decision making, prioritization, and scalability in product management. The study contributes a practical and analytically robust approach for leveraging data-driven insights to guide long-term product growth in complex digital ecosystems.