CUSTOMER CHURN PREDICTION IN MOBILE NETWORKS: A GA-OPTIMIZED K-MEANS AND NEURAL NETWORK APPROACH - Jibril, Bello; Obunadike, G. N; Nuradeen, S. A (FUDMA Journal of Sciences, 2026)
Customer churn prediction constitutes a critical computational challenge within Nigeria’s telecommunications ecosystem, characterized by substantial economic ramifications exceeding ₦150 billion in annual revenue attrition. This investigation presents a novel algorithmic synthesis integrating Genetic Algorithm optimization protocols, K-means clustering methodologies, and Artificial Neural Network architectures, specifically calibrated to address the distinctive behavioral and infrastructural characteristics inherent within Nigeria’s telecommunications marketplace. Empirical validation utilizing comprehensive multi-operator telecommunications datasets (n=17,743) demonstrates that the proposed GA-K-means-ANN computational framework achieves superior predictive performance with 87.3% accuracy and 83.4% F1-score metrics, representing statistically significant improvements of 11.1% relative to baseline methodological approaches (p<0.01, Cohen’s d=0.87). The advanced segmentation analysis successfully identified six heterogeneous customer clusters exhibiting churn probability distributions ranging from 8.1% to 62.7%, thereby empirically validating the substantial market heterogeneity characterizing Nigeria’s telecommunications landscape (χ²=427.6, p<0.001). The proposed hybrid methodology demonstrates superior probabilistic calibration capabilities (Brier score=0.124) while maintaining enhanced computational efficiency through 10.0% reduction in training temporal requirements (p<0.05). Comprehensive business value analysis indicates potential annual cost savings approximating ₦5.3 million per 10,000 customer cohorts, establishing significant economic relevance for telecommunications infrastructure providers operating within North-Central Nigeria’s market constraints. This research contributes methodological advancement in segment-specific churn prediction while providing computationally feasible implementation frameworks designed for deployment within Nigeria’s regional telecommunications infrastructure limitations.