Articles
| Open Access | Integrating Predictive Analytics and Big Data Intelligence for Customer Churn Management in Salesforce Service Cloud Environments
Dr. Amelia Laurent , Department of Information Systems and Digital Strateg University of Montreal, CanadaAbstract
Customer churn remains one of the most critical challenges for organizations operating in subscription-driven and service-oriented markets. With the increasing adoption of cloud-based customer relationship management (CRM) platforms such as Salesforce Service Cloud, the integration of predictive analytics into operational workflows has become both feasible and strategically essential. This study develops a comprehensive theoretical and analytical framework for predictive customer churn modeling within Salesforce Service Cloud ecosystems, grounded exclusively in established scholarship on churn prediction, machine learning methodologies, big data adoption, supply chain forecasting, and intelligent analytics systems. By synthesizing research on dynamic churn strategies, text analytics, deep learning models, data augmentation, logistic regression, convolutional neural networks, and business intelligence optimization, the study constructs an integrated architecture for churn detection and proactive retention management. The research elaborates how structured transactional data, unstructured textual interactions, and behavioral indicators can be leveraged within Salesforce Service Cloud to generate high-fidelity predictive insights. It further examines the implications of big data technologies for model scalability, risk management, and organizational forecasting accuracy. Through extensive theoretical elaboration and interpretive analysis, the findings demonstrate that predictive analytics in CRM environments enhances retention strategy precision, reduces revenue volatility, and strengthens long-term customer lifecycle value. The study contributes to both academic literature and managerial practice by bridging predictive modeling theory with platform-specific CRM deployment contexts, offering a holistic perspective on churn intelligence in cloud-based service ecosystems.
Keywords
customer churn prediction, predictive analytics, Salesforce Service Cloud, machine learning
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Copyright (c) 2026 Dr. Amelia Laurent
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