Performance Enhancement of Hybrid Algorithm for Bank Telemarketing - et. al., Rohan Desai, (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
Telemarketing is an interactive direct marketing system in which telemarketers encourage customers to leverage the resources by notifying, imparting knowledge of online products, latest business offers via direct interaction or through a telephone call. In the contemporary global pandemic spell telemarketing has become dominant backbone to increase the online banking business to withstand for the reducing retail business. It has gained prominance in the banking and financial sector with the enormous adoption and availability of cellular connections amongst customers. The contemporary work has scrutinized conventional classification as well as data mining methods have a problem of ill-fitting with multiple features and are prone to data leakage during re-training of the machine learning model. A local Indian bank were designated, contemplating the current economic slowdown and crisis. A discussion on three machine learning (ML) models is performed along with the Hybrid ML model, Logistic Regression ML model (LR), Naive Bayes ML model (NB), Decision Trees ML model (DTs). The three ML models were tested and analysed with proposed Hybrid ML model on an evaluation set, the data is partitioned as training, validation and test set. The hybrid model first identifies important features of subscribed customers and predicts response for a potential customer, both existing and new who will eventually subscribe again through the direct marketing campaign. The hybrid model is trained to predict the response of new customer who will subscribe to the product or service offered via a direct marketing campaign through transfer learning. The hybrid model API shows new customer response on the front-end screen. To overcome the problem of ill-fitting and data leakage, the model is trained on a large dataset and tuned on a validation set. The proposed hybrid machine learning technique presented the best results (Accuracy 98.69%). Python language is used to develop the model. Financial institutions and organizations can use the hybrid model for predictions of product direct marketing response with customer transaction information.