Stakeholder-Centric Product Management Using Machine Learning, Data Visualization, And Growth Analytics

Abstract

In contemporary digital product ecosystems, product success increasingly depends on the ability to align product decisions with the diverse and evolving expectations of stakeholders. This study proposes and empirically examines a stakeholder-centric product management framework that integrates machine learning, data visualization, and growth analytics to support data-driven decision making across the product lifecycle. Using multi-source stakeholder and product performance data, machine learning techniques were employed to segment stakeholders, predict retention and feature success, and quantify growth-related outcomes. Growth analytics was used to contextualize predictive insights across lifecycle stages, while data visualization translated complex analytical results into interpretable decision-support artifacts. The results demonstrate clear differentiation among stakeholder segments, high predictive accuracy of stakeholder-centric models, and measurable improvements in product performance and growth indicators following analytics integration. The study highlights the synergistic value of combining intelligent modeling, visual analytics, and growth-oriented metrics within a unified framework. Overall, the findings provide both theoretical and practical contributions by advancing stakeholder-centric product management as a scalable, analytics-driven strategy for sustainable product growth and operational efficiency.