Enhancing Bank Marketing Campaigns with Predictive Analytics: A Data-Driven Approach Using XGBoost

Abstract

The study improves marketing approaches and provides a data-driven way to predict bank marketing performance. We want to reliably forecast bank marketing campaign results using the robust and effective gradient boosting framework XG Boost. These performance measures demonstrate the model's robustness and ability to balance recall and precision, ensuring a reliable marketing success estimate. XG Boost, known for its speed and performance, can analyze complex and large banking datasets, making it ideal for prediction tasks. After evaluating our predictive model using various measures, we get an F1 score of 85, recall of 88, accuracy of 85, and precision of 82. We found that banks may improve marketing campaign targeting, resource allocation, and ROI by integrating a data-driven approach with cutting-edge machine learning techniques like XG Boost. This article examines how predictive analytics might boost bank marketing. Banks can better target marketing using customer data in the data-rich world. Our data-driven approach uses machine learning algorithms to assess client data and forecast marketing response. This method prioritizes high-converting contacts to optimize marketing resource allocation. Data-driven predictive analytics can boost marketing campaign performance, client engagement, and profitability for banks.