enhancing proactive customer care with machine learning models for kpi forecasting and experience prediction

As telecommunications networks become increasingly complex, maintaining seamless service quality and customer satisfaction is a growing challenge. this paper presents a machine learning-based framework for proactive customer care in the telecommunications sector. leveraging various data sources such as base transceiver station (bts) kpis, user account information, and call experience metrics, we implement predictive models to forecast call statuses. by employing advanced feature engineering techniques and utilizing long short-term memory (lstm) for kpi prediction, we develop a unified classification model to predict and mitigate instances of poor user experience. our results demonstrate significant improvements in predicting call drops and enhancing overall customer satisfaction through real-time monitoring and proactive interventions.