Customer Segmentation and Retention Strategies for Bank Using Machine Learning

Customer churn is a pressing problem faced by banks, affecting their revenues and customer satisfaction. To solve this problem, this study explores the use of machine learning, specifically Naive Bayes, Decision Trees, and AdaBoost classifiers, to predict and reduce customer churn. By leveraging these complex algorithms, banks can gain insight into customer behaviour and take proactive steps to retain valuable products as customers. The process starts with pre- processed data, model design and coding. This optimization can solve many problems such as excessive priority, missing data, categorical features, etc. Additionally, Data Analytics (EDA) and visualizations to gain a deeper understanding of customer segmentation; This allows to uncover important trends and good standards. When evaluating performance in the model, Focused on simple metrics such as recall and ROC- AUC scores. These metrics provide insight into the model’s ability to accurately identify past customers while minimizing false positives. To improve the performance of model, hyperparameter tuning was performed to ensure that we achieved the highest accuracy. The Model adopt ROC-AUC curve to see the balance between true positive and negative value. This analysis provides a better understanding of the performance model, allowing to make informed decisions and strategies to manage the customer’s quality. Actual and estimated loss status is displayed as an estimate of Customer’s final model. This valuable information provides banks with the strategies necessary to identify potential credit losers and take the necessary steps to maintain their trust.