[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122003-en":3,"doc-seo-122003-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122003,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Enhancing Telemarketing Success Using Ensemble-Based Online Machine Learning","Telemarketing effectiveness hinges on selecting the right customer base; contacting uninterested people wastes resources and reduces conversion while missing likely buyers. Business intelligence and machine learning can improve targeting by predicting potential customers, yet challenges persist in feature selection, building accurate models, handling imbalanced training data, and updating models as customer preferences shift over time. This work introduces an ensemble model with feature selection and oversampling, plus a novel online learning method for retraining under streaming new samples. Experiments with real-world data reach strong performance, including 98.6% accuracy, outperforming recent methods by about 3% on a standard dataset.","BIG DATA MINING AND ANALYTICS  \nISSN 2096-0654 03/15 pp294−314  \nDOI: 10. 26599/BDMA.2023.9020041  \nVolume 7 , Number 2 , June 2024  \nEnhancing Telemarketing Success Using Ensemble-Based  \nOnline Machine Learning  \nShahriar Kaisar* , Md Mamunur Rashid, Abdullahi Chowdhury, Sakib Shahriar Shafin,  \nJoarder Kamruzzaman, and Abebe Diro  \nAbstract: Telemarketing is a well-established marketing approach to offering products and services to prospective customers. The effectiveness of such an approach, however, is highly dependent on the selection of the appropriate consumer base, as reaching uninterested customers will induce annoyance and consume costly enterprise resources in vain while missing interested ones. The introduction of business intelligence and machine learning models can positively influence the decision-making process by predicting the potential customer base, and the existing literature in this direction shows promising results. However, the selection of influential features and the construction of effective learning models for improved performance remain a challenge. Furthermore, from the modelling perspective, the class imbalance nature of the training data, where samples with unsuccessful outcomes highly outnumber successful ones, further compounds the problem by creating biased and inaccurate models. Additionally, customer preferences are likely to change over time due to various reasons, and/or a fresh group of customers may be targeted for a new product or service, necessitating model retraining which is not addressed at all in existing works. A major challenge in model retraining is maintaining a balance between stability (retaining older knowledge) and plasticity (being receptive to new information) . To address the above issues, this paper proposes an ensemble machine learning model with feature selection and oversampling techniques to identify potential customers more accurately. A novel online learning method is proposed for model retraining when new samples are available over time. This newly introduced method equips the proposed approach to deal with dynamic data, leading to improved readiness of the proposed model for practical adoption, and is a highly useful addition to the literature. Extensive experiments with real-world data show that the proposed approach achieves excellent results in all cases (e.g. , 98.6% accuracy in classifying customers) and outperforms recent competing models in the literature by a considerable margin of 3% on a widely used dataset.  \nKey words: telemarketing ; machine learning ; imbalanced dataset ; oversampling ; ensemble model ; online learning  \n Shahriar Kaisar is with the Department of Information Systems and Business Analytics, RMIT University, Melbourne 3000, Australia. E-mail: [shahriar.kaisar@rmit.edu.au](shahriar.kaisar@rmit.edu.au).  \n Md Mamunur Rashid is with the School of Engineering and Technology, Central Queensland University, Rockhampton 4700, Australia. [E-mail: m.rashid@cqu.edu.au](E-mail: m.rashid@cqu.edu.au).  \n Abdullahi Chowdhury is with the Faculty of Engineering, Computer and Mathematical Science, University of Adelaide, Adelaide 5005, [Australia. E-mail: abdul.chowdhury@adelaide.edu.au](Australia. E-mail: abdul.chowdhury@adelaide.edu.au).  \n Sakib Shahriar Shafin and Joarder Kamruzzaman are with the Centre for Smart Analytics, Federation University Australia, Ballarat 3350, Australia. E-mail: [ss.shafin@federation.edu.au](ss.shafin@federation.edu.au); [joarder.kamruzzaman@federation.edu.au](joarder.kamruzzaman@federation.edu.au).  \n Abebe Diro is with the School of Accounting, Information Systems and Supply Chain, RMIT University, Melbourne 3000, Australia. [E-mail: abebe.diro3@rmit.edu.au](E-mail: abebe.diro3@rmit.edu.au).  \n* To whom correspondence should be addressed.  \nManuscript received: 2023-05-08; revised: 2023-11-02; accepted: 2023-12-26  \n© The author(s) 2024. The articles published in this open access journal are distributed under the ","cbCaigiCmB0paOvh","https://ap.wps.com/l/cbCaigiCmB0paOvh","pdf",1171589,1,21,"English","en",105,"# Introduction\n## Telemarketing as targeted direct marketing\n## Challenges: feature selection and imbalanced data\n# Proposed Approach\n## Ensemble learning with feature selection\n## Oversampling to address class imbalance\n## Online retraining under dynamic data\n# Experimental Results\n## Real-world dataset evaluation\n## Accuracy and comparison with competing models","[{\"question\":\"Why does telemarketing require careful customer selection?\",\"answer\":\"Telemarketing success depends on reaching customers likely to buy; contacting uninterested customers wastes enterprise resources and increases annoyance while missing interested buyers reduces outcomes.\"},{\"question\":\"What challenges does the paper target in machine learning for telemarketing?\",\"answer\":\"It addresses feature selection and constructing effective learning models, class imbalance in training data, and the need to retrain models as customer preferences or target groups change over time.\"},{\"question\":\"How does the proposed method improve model retraining for evolving data?\",\"answer\":\"It combines an ensemble machine learning model with feature selection and oversampling, then introduces a novel online learning method to update the model when new samples arrive, balancing stability with adaptability.\"}]","Enhancing Telemarketing Success Using Ensemble-Based Online Machine Learning | PDF",1785808249,53,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"enhancing-telemarketing-success-using-ensemble-based-online-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/enhancing-telemarketing-success-using-ensemble-based-online-machine-learning/122003/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why does telemarketing require careful customer selection?","Question",{"text":75,"@type":76},"Telemarketing success depends on reaching customers likely to buy; contacting uninterested customers wastes enterprise resources and increases annoyance while missing interested buyers reduces outcomes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What challenges does the paper target in machine learning for telemarketing?",{"text":80,"@type":76},"It addresses feature selection and constructing effective learning models, class imbalance in training data, and the need to retrain models as customer preferences or target groups change over time.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed method improve model retraining for evolving data?",{"text":84,"@type":76},"It combines an ensemble machine learning model with feature selection and oversampling, then introduces a novel online learning method to update the model when new samples arrive, balancing stability with adaptability.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]