Developing Customer Retention Strategies Through Churn Prediction with Sequential Learning Models

Abstract

Customer churn prediction has become a pivotal challenge for industries operating in subscription-based or competitive market environments. Advances in artificial intelligence, particularly sequential learning models such as Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks, offer unprecedented potential for detecting patterns in customer behavior over time. This research explores the development of customer retention strategies informed by churn prediction models utilizing sequential learning approaches. We leverage comparative studies of models across banking and telecom sectors and present a synthesized view of current methodologies, their effectiveness, and their implications for proactive retention strategy formulation.