Predicting the Success of Bank Telemarketing for Selling Long-term Deposits: An Application of Machine Learning Algorithms
Keywords:
Long-term deposit, Machine learning algorithms, TelemarketingAbstract
This study attempts to investigate the demand for the adoption of telemarketing practices for promoting long-term bank deposits to potential bank customers. The study explored the demand for long-term bank deposits by employing various machine learning algorithms like Random Forest (RF), Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), Decision Tree (DT), and Logistic Regression (LR). The dataset related to direct marketing campaigns (phone calls) of a Portuguese banking institution is considered for analysis. The results confirm that the LR model provides 92.48% accuracy, which is the best model for predicting the potential customers who have an interest in long-term deposits through telemarketing. The results of the study also provide insightful information to banks for making telemarketing policy decisions in the success of bank deposits to their existing and prospective bank customers.
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