Predictive Analytics Using Neural Networks: A Case Study of Customer Retention in Banking

Abstract

Customer retention plays a crucial role in sustaining business profitability and ensuring long - term success. Retaining existing customers is often more cost - effective than acquiring new ones. This research explores how Neural Networks (NNs) can be effectively used to predict customer churn, enabling businesses to take proactive measures to retain high - risk customers. The study implements structured data preprocessing, exploratory analysis, model optimization, and evaluation techniques to enhance prediction accuracy. Various machine learning frameworks such as Scikit - learn, TensorFlow, Keras, and Pandas are utilized to preprocess data, train models, and evaluate performance. The experimental outcomes demonstrate a significant improvement in predictive accuracy, allowing businesses to make data - driven decisions. Furthermore, the study presents actionable recommendations to optimize customer engagement strategies and resource allocation.