Technology Perception

Abstract: Product decision-making in contemporary organizations increasingly requires systematic, evidence-based approaches to manage complexity, uncertainty, and rapid market change. This study proposes and evaluates an integrated framework that leverages machine learning within business intelligence systems to support data-driven product decisions across the product lifecycle. Using a quantitative analytical design, multiple machine learning models were applied to product, customer, marketing, and operational data to predict performance outcomes, identify key decision drivers, and segment product–customer groups. Ensemble models demonstrated superior predictive accuracy compared to traditional analytics, while engagement-related variables emerged as the strongest determinants of product growth and retention. The integration of predictive insights with business intelligence dashboards enabled faster decision cycles, improved interpretability, and measurable improvements in growth, retention, and revenue performance. The findings confirm that combining machine learning and business intelligence strengthens the empirical foundation of product strategies and facilitates the transition from intuition-driven to evidence-based product management.