Abstract
Outbound telemarketing is an efficient direct marketing method wherein telemarketers solicit potential customers by phone to purchase or subscribe to products or services. However, those who are not interested in the information or offers provided by outbound telemarketing generally experience such interactions negatively because they perceive telemarketing as spam. In this study, therefore, we investigate the use of deep learning models to predict the success of outbound telemarketing for insurance policy loans. We propose an explainable multiple-filter convolutional neural network model called XmCNN that can alleviate overfitting and extract various high-level features using hundreds of input variables. To enable the practical application of the proposed method, we also examine ensemble models to further improve its performance. We experimentally demonstrate that the proposed XmCNN significantly outperformed conventional deep neural network models and machine learning models. Furthermore, a deep learning ensemble model constructed using the XmCNN architecture achieved the lowest false positive rate (4.92%) and the highest F1-score (87.47%). We identified important variables influencing insurance policy loan prediction through the proposed model, suggesting that these factors should be considered in practice. The proposed method may increase the efficiency of outbound telemarketing and reduce the spam problems caused by calling non-potential customers.
Highlights
The recent advancements in digital technology and the accelerating development of global markets are completely changing consumers’ patterns of living and spending.Consumers’ preference for contactless, remote interaction channels has increased, and they have become accustomed to using mobile technology to obtain their desired services and information almost anytime and anywhere
We compared the performance of the proposed model with those of machine learning models, deep neural networks (DNNs) models, convolutional neural network (CNN) models, and ensemble models
The results of the Ensemble (CNNS(3), CNNS(4), CNNS(5), XmCNN) model, which performed the best among the deep learning (DL) ensemble models, showed increased performance in all aspects compared to the machine learning ensemble models
Summary
Consumers’ preference for contactless, remote interaction channels has increased, and they have become accustomed to using mobile technology to obtain their desired services and information almost anytime and anywhere. To respond to this situation and gain a competitive economic advantage while avoiding potential negative business outcomes, companies are attempting to provide services tailored to the digital age while increasing the convenience of contactless channels and the proportion of direct marketing. In the outbound method, a telemarketer calls customers and invites them to subscribe to a product or service. Outbound telemarketing methods are based on offering products or services based on a customer database. The advantage of outbound telemarketing is that it can maximize the effectiveness of sales efforts by providing only the necessary information to customers and recommending sales within a short period
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