A decision support system based on machined learned Bayesian network for predicting successful direct sales marketing

This paper proposes a decision support system based on a machine-learned Bayesian network (BN) to predict the success rate of telemarketing calls for long-term bank deposits. Telemarketing is one of the most common interactive techniques of direct marketing, widely used by financial institutions such as banks to sell long-term deposits. In this study, we develop a BN model that predicts the likelihood that a potential client subscribes to a long-term deposit, which is considered an output variable. The causal relationship among client attributes and outcomes has been identified using the augmented Naïve Bayes approach, a well-known supervised learning algorithm. The impact of each client's attribute on the likelihood of subscribing is predicted. Further, we carry out multiple simulation scenarios using BN’s unique features (forward and backward propagation) to provide more in-depth discussions and analysis on predicting the likelihood of subscription for clients with particular characteristics.