Machine Learning-Enabled Customer Churn Prediction and Personalized Marketing Optimization in Digital Platforms
Keywords:
customer churn prediction, personalized marketing, machine learning, digital platforms, large-scale systems, data infrastructure, algorithmic fairness, concept drift, marketing optimization, socio-technical governanceAbstract
Customer churn, the loss of existing subscribers, represents a critical challenge for digital platform businesses where acquisition costs far exceed retention expenditures. This paper presents a comprehensive system-level analysis of machine learning-enabled churn prediction frameworks integrated with personalized marketing optimization. We examine the architectural trade-offs inherent in designing scalable, real-time churn detection pipelines that operate on high-velocity user interaction data from digital platforms such as e-commerce marketplaces, streaming services, and social networks. The study explores how supervised and unsupervised learning paradigms, including gradient boosting, deep neural networks, and ensemble methods, are deployed within large-scale production environments to generate granular propensity scores. We further analyze the coupling between these prediction engines and downstream marketing optimization modules that allocate promotional resources, tailor content recommendations, and adjust pricing strategies at the individual level. Key structural considerations discussed include data governance, feature engineering pipelines, model refresh cadences, computational efficiency, and the tension between predictive accuracy and operational latency. We also address robustness against concept drift, fairness constraints across demographic segments, and the ethical implications of hyper-personalized interventions that may amplify digital divides. By drawing on illustrative cases from telecommunications, retail, and media sectors, the paper highlights cross-domain variations in churn drivers and optimization objectives. Finally, we propose a governance framework that balances profitability with customer welfare and long-term platform sustainability. The findings underscore the necessity of aligning technical infrastructure with organizational policy to realize the full potential of machine learning-driven churn management without compromising trust or equity.
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