An autonomous mixed data oversampling method for AIOT-based churn recognition and personalized recommendations using behavioral segmentation.

ABSTRACT: The telecom sector is currently undergoing a digital transformation by integrating artificial intelligence (AI) and Internet of Things (IoT) technologies. Customer retention in this context relies on the application of autonomous AI methods for analyzing IoT device data patterns in relation to the offered service packages. One significant challenge in existing studies is treating churn recognition and customer segmentation as separate tasks, which diminishes overall system accuracy. This study introduces an innovative approach by leveraging a unified customer analytics platform that treats churn recognition and segmentation as a bi-level optimization problem. The proposed framework includes an Auto Machine Learning (AutoML) oversampling method, effectively handling three mixed datasets of

SUBMITTER: Fatima G 

PROVIDER: S-EPMC10773761 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature