[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124131-en":3,"doc-seo-124131-105":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},124131,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Leveraging Machine Learning and Deep Learning Models for Proactive Churn Customer Retention - Proactive churn prediction","Customer attrition is a critical challenge in retail, banking, and telecommunications, where retaining existing customers is far more cost-effective than acquiring new ones. The study develops churn prediction using machine learning and deep learning to enable early intervention for retention. Six machine learning methods and four deep learning architectures are evaluated using accuracy, precision, recall, and F1-score on behavioral data after feature extraction. Results indicate stronger churn versus non-churn classification with deep learning, particularly LSTM and ANN, and emphasize preprocessing and bootstrapping to improve real-world performance.","VFAST Transactions on Software Engineering Volume 12, Issue 4, 2024  \nVFAST Transactions on Software Engineering [https://vfast.org/journals/index.php/VTSE@ 2024](https://vfast.org/journals/index.php/VTSE@ 2024), ISSN(e): 2309-3978, ISSN(p): 2411-6246  \nVolume 12, Number 4, October-December 2024 pp:70-86  \nKeywords: Churn, Prediction, Binary Classification, Machine Learning, Models, Pre-Processing, Data Driven, Deep Learning, Retention, Machine learning, Deep learning, LSTM, CNN, ANN, Retention, Customer Analysis.  \nJournal Info:  \nSubmitted: October 06, 2024 Accepted:  \nNovember 17, 2024 Published:  \nNovember 23, 2024  \nLeveraging Machine Learning And Deep Learning Models for Proactive Churn Customer Retention  \nHira Farman 1* , Samar Raza Talpur2 , Usman Amjad2 , Govari shankar3 , Umm e Laila4 , Lubaba Naseem5  \n1 Department of Computer Science, Iqra University, Karachi, Pakistan; 2 Department of Computer Science, Sukkur IBA University, Sindh, Pakistan; 3 Department of Computer Science & Information Technology TIEST Constituent Institute of NED University Karachi, Pakistan; 4 Department of Computer Science, Institute of Business Management Karachi, Pakistan ; 5 Department of Computer Science, Iqra University, Karachi Pakistan  \nAbstract  \nCustomer attrition is especially an issue in industries such as retail, banking, and telecommunications where customer acquisition costs are significantly higher than the costs of retaining repeat customers. The customer lack of interest is now predictable through machine learning models, and deep learning has become instrumental in early intervention for retention. In order to assess the quality of churn prediction, the study tests six basic machine learning techniques: random forest, logistic regression, and the k-nearest neighbors method, as well as four deep learning techniques: long short term memory (LSTM), bidirectional LSTM, convolutional neural networks (CNN), and artificial neural networks (ANN). The performance of the model is then assessed via the evaluation matrices, including the accuracy, precision, recall, and F1-score from the customer’s behavioral data after feature extraction from large datasets. The study reveals that DL models offer improved handling of the churn and non-churn customer classification and Random Forest as well as other ML models comparable accuracy. This research can conclude that LSTM and ANN models outshine in actual-world churn prediction circumstances, especially when long-term consumer behavior evaluation is required. To enhance the current outcomes of a given prediction model, this research focuses on data preprocessing and the utilization of bootstrapping, feature extraction, and the combination of multiple models. The implications of the study provide specific practical recommendations for firms to effectively manage customer churn and increase customer retention by employing datadealing techniques.  \n*Correspondence author email address: [hira.farman@iqra.edu.pk](hira.farman@iqra.edu.pk)[ ](hira.farman@iqra.edu.pk)DOI: 10.21015/vtse.v12i4 .1928  \nThis work is licensed under a Creative Commons Attribution 3.0 License.  \nVFAST Transactions on Software Engineering Volume 12, Issue 4, 2024  \n1 Introduction  \nSustaining a customer base is crucial for maintaining steady growth and profitability in the fiercely competitive world of modern business. In industries such as banking, retail, and telecommunications, where keeping existing consumers is more economical than attracting new ones, customer churn and the loss of consumers to competitors is a major problem. Machine learning has developed into an effective method for churn prediction with the rise of data-driven methodologies, allowing companies to promptly deploy retention initiatives.  \nParticularly in the telecom industry, methods like Random Forest, Logistic Regression, and ensemble models like XG-Boost have demonstrated great accuracy in churn prediction [1] [2] [3] . Bi-LSTM and CNN are two examples o","cbCaikdS1htVEoN1","https://ap.wps.com/l/cbCaikdS1htVEoN1","pdf",822971,1,17,"English","en",105,"# 1 Introduction\n## 1.1 Problem Statement","[{\"question\":\"Why is customer churn especially important in industries like banking, retail, and telecommunications?\",\"answer\":\"Customer churn directly threatens revenue and competitiveness. In these industries, acquisition costs can exceed retention costs, making early churn detection valuable for sustaining growth and profitability.\"},{\"question\":\"Which machine learning and deep learning models are evaluated for proactive churn prediction?\",\"answer\":\"The study tests machine learning techniques including random forest, logistic regression, and k-nearest neighbors, and deep learning models including LSTM, bidirectional LSTM, CNN, and ANN.\"},{\"question\":\"How is model performance assessed in the study?\",\"answer\":\"Performance is measured using evaluation metrics such as accuracy, precision, recall, and F1-score based on customer behavioral data after feature extraction from large datasets.\"}]","Leveraging Machine Learning and Deep Learning Models for Proactive Churn Customer Retention - Proactive churn prediction | PDF",1785820626,43,{"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},"leveraging-machine-learning-and-deep-learning-models-for-proactive-churn-customer-retention-proactive-churn-prediction","",{"@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/leveraging-machine-learning-and-deep-learning-models-for-proactive-churn-customer-retention-proactive-churn-prediction/124131/",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 is customer churn especially important in industries like banking, retail, and telecommunications?","Question",{"text":75,"@type":76},"Customer churn directly threatens revenue and competitiveness. In these industries, acquisition costs can exceed retention costs, making early churn detection valuable for sustaining growth and profitability.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning and deep learning models are evaluated for proactive churn prediction?",{"text":80,"@type":76},"The study tests machine learning techniques including random forest, logistic regression, and k-nearest neighbors, and deep learning models including LSTM, bidirectional LSTM, CNN, and ANN.",{"name":82,"@type":73,"acceptedAnswer":83},"How is model performance assessed in the study?",{"text":84,"@type":76},"Performance is measured using evaluation metrics such as accuracy, precision, recall, and F1-score based on customer behavioral data after feature extraction from large datasets.","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"]