Customer Churn Prediction

Abstract

In today’s highly competitive business landscape, customer retention revenue preservation, customer experience improvement, and marketing optimisation are critical factors for sustained growth and profitability. Customer churn predictionis discontinuing their services or purchases, which presents asignificant challenge for businesses across various industries. This project focuses on developing a predictive model to expect customer churn in the banking sector using machine learning techniques. The project involves the collection and analysis of historical customer data, confined account activity, transaction history, demographic information, and customer service interactions. By extracting the right features from this data, a machine learning model is trained to forecast which bank customers are at the highest risk of churning. A critical step in this study was the selection of relevant features that influence customerchurn. Feature selection was guided by domain knowledge and feature importance analysis. The different classifiers were used and then trained on the training dataset further ensuringthe model’s optimal performance. The model’s performance is assessed through various evaluation metrics, including accuracy, precision, and recall. Additionally, the project explores a model illustration to uncover the influential factors contributing to customer churn within the banking context. This project’s outcomes can empower banks to take proactive measures in retaining customers, enhancing their overall experience, and thereby preserving revenue streams. By addressing customer churn, banks can foster long-term relationships, reduce customer acquisition costs, and boost their competitiveness in the financial industry. The results of this project are expected to assist businesses in proactively retaining customers by targeting those at the highest risk of churning. Ultimately, reducing customer churn can lead to increased customer satisfaction, revenue, and long-term business sustainability.