Advanced Predictive Architectures for Mitigating Customer Attrition: A Comparative Analysis of Machine Learning and Deep Learning Methodologies across Global Service Sectors

Abstract

Customer churn, defined as the loss of clients or subscribers to a service provider, represents a critical challenge for modern enterprises operating within hyper-competitive environments. This research provides a comprehensive examination of predictive modeling techniques designed to identify and mitigate churn across various industries, including telecommunications, banking, e-commerce, and traditional broadcast media. By synthesizing contemporary advancements in machine learning-ranging from classic logistic regression and random forests to sophisticated deep learning architectures and hybrid evolutionary algorithms-this study evaluates the efficacy of different algorithmic approaches in disparate data environments. A central focus is placed on the integration of predictive analytics within enterprise service platforms like Salesforce, as well as the utilization of unstructured data sources, such as call logs, to enhance model precision. The methodology details the transition from traditional feature engineering to class-specific metaheuristic techniques for explainable feature selection, addressing the "black box" nature of complex models. Results indicate that while deep learning offers superior performance in large-scale e-commerce datasets, hybrid models combining evolutionary programming with standard classifiers often yield more robust results in telecommunications. Furthermore, the study explores the role of explainable AI in ensuring that churn predictions translate into actionable strategic insights within a Strategic Knowledge Management framework. The discussion highlights the limitations of current models, particularly regarding data imbalance and the temporal dynamics of customer behavior, and proposes a roadmap for future research in cross-industry predictive resilience.