From Network Experience to Subscriber Retention: An Explainable AI Framework for Mobile Operators
For mobile operators, this framework offers actionable insights to improve subscriber retention by leveraging QoE-centric analytics.
The paper proposes an explainable AI framework for predicting subscriber churn in mobile operators, demonstrating on real data from a major telco that QoE indicators provide stronger churn signals than traditional network counters.
This article presents a framework for the prediction of subscriber churn in mobile operators also known as telecommunication operators (or telcos). This framework covers relevant aspects of data-driven approaches using explainable artificial intelligence and machine learning. To demonstrate the robustness of the framework, we implement it on real data from one of the globally leading telcos with tens of millions of subscribers and show results and actionable insights confirming the usefulness and longevity of the framework. Our results suggest that subscriber quality of experience (QoE) indicators provide stronger churn signals than traditional network counters alone, reinforcing the need for QoE-centric analytics in modern operations in telcos. We conclude with future research directions for improving churn predictability and operational deployment.