Multimedia data-driven customer churn prediction using an enhanced extreme learning machine.

This study proposes a novel modification to the extreme learning machine (ELM) by introducing an optimized least-squares formulation with penalty regularization, enabling stable and efficient handling of sparse, high-dimensional multimedia data. In contrast to traditional autoencoders, our approach incorporates ELM-driven hidden layer refinement, achieving more effective feature compression and dimensionality reduction tailored for multimedia churn prediction. Additionally, we design a Gaussian kernel adaptation specifically for multimedia datasets, which replaces random feature mappings and enhances both predictive robustness and generalization performance. Empirical validation on a public multimedia customer behavior dataset reveals the model's superiority over traditional churn prediction methods, showcasing notable improvements in prediction accuracy and precision. This research equips businesses with a robust model for informed CRM decision-making, enhancing customer retention and profitability. The study contributes to the in-depth understanding of multimedia data's role in fostering sustainable and profitable customer relationships in today's digital era.