Predicting Success of Outbound Telemarketing in Insurance Policy Loans Using an Explainable Multiple-Filter Convolutional Neural Network
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/15/7147 |
| _version_ | 1797525788591915008 |
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| author | Jinmo Gu Jinhyuk Na Jeongeun Park Hayoung Kim |
| author_facet | Jinmo Gu Jinhyuk Na Jeongeun Park Hayoung Kim |
| author_sort | Jinmo Gu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T09:17:57Z |
| format | Article |
| id | doaj.art-89382031f22248888a8be4775f0683ea |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T09:17:57Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-89382031f22248888a8be4775f0683ea2023-11-22T05:25:08ZengMDPI AGApplied Sciences2076-34172021-08-011115714710.3390/app11157147Predicting Success of Outbound Telemarketing in Insurance Policy Loans Using an Explainable Multiple-Filter Convolutional Neural NetworkJinmo Gu0Jinhyuk Na1Jeongeun Park2Hayoung Kim3Graduate School of Information, Yonsei University, Yonsei-ro 50, Seodaemun-gu, Seoul 03722, KoreaPersonal Loan Digital Marketing Support Center, KYOBO Life Insurance, Jong-ro 1, Jongno-gu, Seoul 03154, KoreaGraduate School of Information, Yonsei University, Yonsei-ro 50, Seodaemun-gu, Seoul 03722, KoreaGraduate School of Information, Yonsei University, Yonsei-ro 50, Seodaemun-gu, Seoul 03722, KoreaOutbound 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.https://www.mdpi.com/2076-3417/11/15/7147outbound telemarketingdeep learningmachine learningconvolutional neural networkinsurance policy loanexplainability |
| spellingShingle | Jinmo Gu Jinhyuk Na Jeongeun Park Hayoung Kim Predicting Success of Outbound Telemarketing in Insurance Policy Loans Using an Explainable Multiple-Filter Convolutional Neural Network Applied Sciences outbound telemarketing deep learning machine learning convolutional neural network insurance policy loan explainability |
| title | Predicting Success of Outbound Telemarketing in Insurance Policy Loans Using an Explainable Multiple-Filter Convolutional Neural Network |
| title_full | Predicting Success of Outbound Telemarketing in Insurance Policy Loans Using an Explainable Multiple-Filter Convolutional Neural Network |
| title_fullStr | Predicting Success of Outbound Telemarketing in Insurance Policy Loans Using an Explainable Multiple-Filter Convolutional Neural Network |
| title_full_unstemmed | Predicting Success of Outbound Telemarketing in Insurance Policy Loans Using an Explainable Multiple-Filter Convolutional Neural Network |
| title_short | Predicting Success of Outbound Telemarketing in Insurance Policy Loans Using an Explainable Multiple-Filter Convolutional Neural Network |
| title_sort | predicting success of outbound telemarketing in insurance policy loans using an explainable multiple filter convolutional neural network |
| topic | outbound telemarketing deep learning machine learning convolutional neural network insurance policy loan explainability |
| url | https://www.mdpi.com/2076-3417/11/15/7147 |
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