[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-detail-606471-en":59,"doc-seo-606471-105":80},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":5,"data":60},{"doc_id":61,"user_id":62,"nickname":63,"user_avatar":64,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":66,"doc_content":67,"file_id":68,"file_url":69,"file_type":70,"file_size":71,"view_count":14,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":72,"language":73,"language_code":74,"site_id":75,"html_lang":74,"table_of_contents":76,"faqs":77,"seo_title":78,"seo_description":66,"update_tm":79,"read_time":41},606471,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","How to improve the success of bank telemarketing - Prediction and interpretability analysis based on machine learning","Telemarketing is widely used by banks to obtain deposits at low cost, yet selling financial services over the phone is difficult without effective consumer targeting. This paper builds bank telemarketing prediction models using Random Subspace (RS), Multi-Boosting (MB), and Random Subspace-Multi-Boosting (RS-MB), then performs interpretability analysis to support marketing decisions. Variable importance ranking selects influential predictors, and reconstructed models improve performance. Partial Dependence Plots quantify marginal effects on subscription success.","Computers & Industrial Engineering 175 (2023) 108874  \nContents lists available at ScienceDirect  \nComputers & Industrial Engineering  \njournal [homepage:](homepage: www.elsevier.com/locate/caie)[ www.elsevier.com/locate/caie](homepage: www.elsevier.com/locate/caie)  \n| How to improve the success of bank telemarketing? Prediction and interpretability analysis based on machine learning |  |  | |\n| --- | --- | --- | --- |\n| Chi Xiea, b, Jia-Le Zhang a, You Zhu a, b, *, Beibei Xionga, Gang-Jin Wang a, ba Business School, Hunan University, Changsha 410082, China\u003Cbr>b Center for Finance and Investment Management, Hunan University, Changsha 410082, China |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Bank telemarketing Machine learning Prediction performance Interpretability analysis |  | Because of the low cost and user-friendliness, telemarketing has become a common way for banks to obtain deposits for a long time. Meanwhile, researchers have been attempting to analyze consumer information in-depth to improve the effectiveness of bank telemarketing and attract deposits through telephone communication. In this paper, we construct bank telemarketing prediction models by three machine learning (ML) methods, i.e., Random Subspace (RS), Multi-Boosting (MB) and Random Subspace-Multi-Boosting (RS-MB), and find the best performing model. Also, we make the interpretability analysis to provide banks with decision information to develop and implement an effective marketing plan. We rank the importance of the original independent variables by the ML method and select those variables whose influence on the prediction results was significant. And we reconstruct the bank telemarketing prediction models based on the selected independent variables. Furthermore, we illustrate the marginal effects of the selected independent variables on the consumers’ subscription of deposits by the Partial Dependence Plots (PDP) to analyze how these selected independent variables affect the success of bank telemarketing campaigns. The empirical results indicate that the RS-MB using selected independent variables achieves the best performance for prediction. It is worth noting that banks would rather contact uninterested customers than miss potential deposit customers. Therefore, when predicting the success of telemarketing campaigns, it is more critical to reduce false negative rate than false positive rate. Moreover, banks using telemarketing should pay more attention to type of job that the customer does, the month that the customer was connected, and contact day of week. |  |\n\n1. Introduction  \nBanks with sufficient deposits can reduce depositors’ apprehensions about bank solvency and liquidity and are therefore less vulnerable to financial crises (Ivashina & Scharfstein, 2010). With the increasing capital requirements, banks attempt to capture deposits as their stable source of funding through various marketing techniques. Among them, direct marketing provides consumers with specific products and services, reduces transaction costs, and obtains an immediate response (Liao, Chen, & Hsieh, 2011). Direct marketing includes mail marketing, telemarketing, and Internet marketing. Since telemarketing is userfriendly and cost-effective, it has become a widely favored direct marketing approach in the past few years (Lock & Kaner, 2000; Yan, Li, & Liu, 2020).  \nHowever, it is often difficult for bank marketers to effectively sell  \nfinancial services or products in a short period of time through telephone communication. To increase the success rate and improve effectiveness, scholars recommend that banks elaborate analysis of consumer information before telemarketing. In recent years, increasing studies have applied machine learning (ML) to identify potential consumers who are willing to subscribe to deposits (Yan, Li, & Liu, 2020; Moro, Cortez, & Rita, 2014; Ghatasheh et al., 2020; Feng, Yin, Wang, & Dhamotharan, 2022). In this way, banks w","cbCaitX9ExsCrjRJ","https://ap.wps.com/l/cbCaitX9ExsCrjRJ","pdf",2033007,12,"English","en",105,"# Introduction\n## Telemarketing in banking and the need for prediction\n## Machine learning for identifying likely deposit subscribers\n# Methods and interpretability analysis\n## Feature importance and model reconstruction\n## Partial Dependence Plots and marginal effects\n# Results and implications","[{\"question\":\"Which machine learning methods are used to predict bank telemarketing success?\",\"answer\":\"The study constructs prediction models with three machine learning methods: Random Subspace (RS), Multi-Boosting (MB), and Random Subspace-Multi-Boosting (RS-MB).\"},{\"question\":\"How does the paper improve model interpretability?\",\"answer\":\"It ranks the importance of original independent variables using machine learning, selects variables with significant influence, and reconstructs the prediction models using those selected variables. It also uses interpretability via Partial Dependence Plots (PDP) to analyze marginal effects.\"},{\"question\":\"What do the results indicate about prediction performance and error priorities?\",\"answer\":\"The RS-MB model using selected independent variables achieves the best prediction performance. The findings emphasize reducing the false negative rate more than the false positive rate, because banks would rather contact uninterested customers than miss potential deposit customers.\"}]","How to improve the success of bank telemarketing - Prediction and interpretability analysis based on machine learning | PDF",1792184465,{"code":4,"msg":81,"data":82},"ok",{"site_id":75,"language":74,"slug":83,"title":65,"keywords":84,"description":66,"schema_data":85,"social_meta":139,"head_meta":141,"extra_data":143,"updated_unix":144},"how-to-improve-the-success-of-bank-telemarketing-prediction-and-interpretability-analysis-based-on-machine-learning","",{"@graph":86,"@context":138},[87,101,121],{"@type":88,"itemListElement":89},"BreadcrumbList",[90,94,96,99],{"item":91,"name":92,"@type":93,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":95,"name":9,"@type":93,"position":14},"https://docshare.wps.com/document/",{"item":97,"name":40,"@type":93,"position":98},"https://docshare.wps.com/document/research-report/",3,{"item":100,"name":65,"@type":93,"position":19},"https://docshare.wps.com/document/how-to-improve-the-success-of-bank-telemarketing-prediction-and-interpretability-analysis-based-on-machine-learning/606471/",{"url":100,"name":65,"@type":102,"image":103,"author":108,"headline":65,"publisher":110,"fileFormat":113,"inLanguage":74,"description":66,"dateModified":114,"datePublished":115,"encodingFormat":113,"isAccessibleForFree":116,"interactionStatistic":117},"DigitalDocument",{"url":104,"@type":105,"width":106,"height":107},"https://docshare.wps.com/thumbnails/how-to-improve-the-success-of-bank-telemarketing-prediction-and-interpretability-analysis-based-on-machine-learning/606471.png","ImageObject",300,407,{"name":63,"@type":109},"Person",{"url":91,"name":111,"@type":112},"DocShare","Organization","application/pdf","2026-10-18","2026-10-16",true,{"@type":118,"interactionType":119,"userInteractionCount":14},"InteractionCounter",{"@type":120},"ViewAction",{"@type":122,"mainEntity":123},"FAQPage",[124,130,134],{"name":125,"@type":126,"acceptedAnswer":127},"Which machine learning methods are used to predict bank telemarketing success?","Question",{"text":128,"@type":129},"The study constructs prediction models with three machine learning methods: Random Subspace (RS), Multi-Boosting (MB), and Random Subspace-Multi-Boosting (RS-MB).","Answer",{"name":131,"@type":126,"acceptedAnswer":132},"How does the paper improve model interpretability?",{"text":133,"@type":129},"It ranks the importance of original independent variables using machine learning, selects variables with significant influence, and reconstructs the prediction models using those selected variables. It also uses interpretability via Partial Dependence Plots (PDP) to analyze marginal effects.",{"name":135,"@type":126,"acceptedAnswer":136},"What do the results indicate about prediction performance and error priorities?",{"text":137,"@type":129},"The RS-MB model using selected independent variables achieves the best prediction performance. The findings emphasize reducing the false negative rate more than the false positive rate, because banks would rather contact uninterested customers than miss potential deposit customers.","https://schema.org",{"og:url":100,"og:type":140,"og:title":65,"og:site_name":111,"og:description":66},"article",{"robots":142,"canonical":100},"index,follow",{"doc_id":61,"site_id":75},1792329069]