Business Analytics in Telemarketing: Cost-Sensitive Analysis of Bank Campaigns Using Artificial Neural Networks

Abstract

The banking industry has been seeking novel ways to leverage database marketing efficiency. However, the nature of bank marketing data hindered the researchers in the process of finding a reliable analytical scheme. Various studies have attempted to improve the performance of Artificial Neural Networks in predicting clients’ intentions but did not resolve the issue of imbalanced data. This research aims at improving the performance of predicting the willingness of bank clients to apply for a term deposit in highly imbalanced datasets. It proposes enhanced Artificial Neural Network models (i.e., cost-sensitive) to mitigate the dramatic effects of highly imbalanced data, without distorting the original data samples. The generated models are evaluated, validated, and consequently compared to different machine-learning models. A real-world telemarketing dataset from a Portuguese bank is used in all the experiments. The best prediction model achieved 79% of geometric mean, and misclassification errors were minimized to 0.192, 0.229 of Type I & Type II Errors, respectively. In summary, an interesting Meta-Cost method improved the performance of the prediction model without imposing significant processing overhead or altering original data samples.

Predicting Success of Outbound Telemarketing in Insurance Policy Loans Using an Explainable Multiple-Filter Convolutional Neural Network

“…Ghatasheh et al [5] Business analytics in telemarketing: cost-sensitive analysis of bank campaigns using artificial neural networks -Artificial neural networks -Support vector machine -Random forest…”

“…Experimental results showed that the long short-term memory model using SMOTE outperformed other models. Ghatasheh et al [5] proposed an ANN model for bank telemarketing prediction using 16 variables and compared the performance of traditional machine-learning classifiers. They found that, the Type II and Type I errors of their proposed model were higher and lower, respectively, than those of other models.…”

“…We used the false positive rate (FPR), false negative rate (FNR), recall, precision, accuracy, and F1-score as indicators to evaluate the model's performance, and the calculation formula is shown in Table 5. To offer exhaustive evaluations, these classification performance metrics have been comprehensively used in related research [1][2][3][4][5][6][10][11][12][13][14]. The confusion matrix which is the basis for calculating the performance evaluation index is presented in Table 6.…”

توقع دقة عملية التسويق عبر الهاتف في البنوك باستخدام استخراج البيانات

“…The main issue with the bank is how to get correct prediction ratio during the direct marketing campaign. The idea was presented in the study [2] where the researcher proposed the artificial neural net model to forecast the chances of customers who can apply for short and long term deposits in the bank. Furthermore, the research incorporated by defining the rules, which can be helpful for the decision makers to understand the potential benefits that can be achieved through the marketing campaign.…”

“…To provide some ease in this operation, researchers have proposed several strategies integrated with use of latest technologies. For example business analytics in telemarketing using artificial neural networks [2] , chat-bot design on real marketing data [3] , and telemarketing forecasting using SVM [4] . Therefore, it can be evident that use of current technology can reveal many facts for the organizations by extracting hidden patterns from the collected dataset.…”