Behavioral Satisfaction Inference: A Semi-Supervised Framework for Large-Scale Telecom Customer Intelligence

Abstract

Customer satisfaction is a key factor influencing customer retention in the telecom industry. However, conventional survey-based measurement approaches are often constrained by low response rates, sampling bias, and limited population coverage, making it difficult to obtain a comprehensive view of subscriber sentiment. This paper presents Behavioral Satisfaction Inference, a semi-supervised learning framework designed to estimate customer satisfaction across an entire subscriber base without relying on explicit feedback from every customer. The proposed approach combines a relatively small set of labeled survey responses with a large volume of unlabeled behavioral data. Using a pseudo-labeling strategy, satisfaction signals are propagated across more than 2 million subscribers while addressing a highly imbalanced distribution in which fewer than 15% of customers are dissatisfied. The framework incorporates multiple data sources, including service usage patterns, billing behavior, and network quality indicators, to generate continuous satisfaction predictions at scale. Experimental results demonstrate that the semi-supervised framework outperforms supervised-only baselines, improving recall for dissatisfied customers by more than 18%. The final model achieves an 86.2% recall rate in identifying subscribers at risk of dissatisfaction. Deployed in a production telecom environment, the system supports proactive retention efforts by enabling broader and more timely identification of potentially dissatisfied customers. The findings highlight the effectiveness of semi-supervised learning for large-scale satisfaction estimation and offer an alternative to traditional survey-centric approaches.