Smart Predictive Churn Management in Telecommunications Using Neural Networks

Abstract

The telecommunications market is highly competitive and customer retention is one of
the top strategic challenges for service providers to address with innovative technical solutions.
The study presents Smart Neural Networks (ANN & RNN) Based Telecom Customer Retention
Intelligence which is a Neural Network-based approach for predictive modelling to avoid
retention. With a holistic view of predictive analytics across customer demographics, usage
dynamics, billing cycles, and customer services interaction metrics, the proposed model builds a
comprehensive comprehension of the drivers of subscriber churn. Unlike traditional predictive
models, the smart neural network architecture utilizes advanced machine learning algorithms to
identify intricate connections among various customer characteristics. In contrast to traditional
methods, the model offers more than just predictive capability, delivering valuable insights
about the key drivers of customer loss. Data was collected and used to train a machine learning
model to predict customer retention from general characteristics reflecting customer behavior in
the telecom industry. The proactive nature of predictions obtained via the application of the
proposed system enables organizations to develop and execute data-driven interventions to
mitigate churn on a timely basis, thus enhancing customer retention.