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Customer churn continues to be an issue of great importance for digital service providers, which harms revenues and long-term growth. The work presented here aims to utilize a machine learning point of view on user content interaction patterns over different digital platforms to predict churn. Temporally based engagement metrics, behavioral clustering and feature engineering is proposed to model user intent and detect users’ early signals of churn in an integration using the proposed framework. Supervised learning models are analyzed with Random Forest, Gradient Boosting, and Support Vector Machines among others on real world datasets that are available from streaming and SaaS platforms. It is demonstrated that the results help a lot in improving the prediction accuracy and early detection rates over traditional demographic and transactional models. Finally, this study provides a practical roadmap for digital service providers to adhere to data driven strategies and create counter measures that will increase user retention through the implementation of the proposed solution