[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125698-en":3,"doc-seo-125698-105":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},125698,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Customer Churn Prediction of Telecom Company Using Machine Learning Algorithms - read online free","Telecommunications has become deeply embedded in everyday life, and the Covid-19 period further increased reliance on telecom connectivity while reshaping the industry’s growth environment. This study applies six supervised machine learning algorithms—KNN, Random Forest, AdaBoost, Logistic Regression, XGBoost, and Support Vector Machine—to predict customer churn for a telecom company in California. XGBoost achieves the strongest overall performance (accuracy 79.67%, precision 64.67%, recall 51.87%, F1-score 57.57%).","e-ISSN : 2716-621X  \nCustomer Churn Prediction of Telecom Company Using Machine Learning Algorithms  \nAngela Yi Wen Chong1, Khai Wah Khaw1*, Wai Chung Yeong2, Wen Xu Chuah1  \n1School of Management,  \nUniversiti Sains Malaysia, 11800 USM, Penang, MALAYSIA  \n2School of Mathematical Sciences,  \nSunway University, Petaling Jaya, MALAYSIA  \n*Corresponding Author  \nDOI: [https://doi.org/10.30880/jscdm.2023.04.02.001](https://doi.org/10.30880/jscdm.2023.04.02.001)  \nReceived 18 February 2023; Accepted 21 August 2023; Available online 04 October 2023  \nAbstract: We can’t escape the fact that using telecommunications has become a significant part of our everyday lives. Since the Covid-19 pandemic, the telecommunication industry has become crucial. Hence, the industry now enjoys growth opportunities. In this study, KNN, Random Forest (RF), AdaBoost, Logistic Regression (LR), XGBoost, and Support Vector Machine (SVM) are 6 supervised machine learning algorithms that will be used in this study to predict the customer churn ofa telecom company in California. The goal of this study is to identify the classifier that predicts customer churn the most effectively. As evidenced by its accuracy of 79.67%, precision of 64.67%, recall of 51.87%, and F1-score of 57.57%, XGBoost is the overall most effective classifier in this study. Next, the purpose of this study is to identify the characteristics of customers who are most likely to leave the telecom company. These characteristics were discovered based on customers’ demographics and account information. Lastly, this study also provides the company with advice on how to retain customers. The study advises company to personalize the customer experience, implement a customer loyalty program, and apply AI in customer relationship management in retaining customers.  \nKeywords: Machine learning, supervised machine learning, customer churn prediction, XGBoost  \n1. Introduction  \nIn this era of globalization, we can’t deny that the use of telecommunications has become an important part of our daily life. Telecommunication is the term used to describe the conveyance of a communication or message across a distance through different techniques, including using the telephone, cable, and other methods. Telecommunications also had a distinct meaning before the growth ofthe Internet and other data networks, which is the public switched telephone network (PSTN) that offered telephone service, making it possible for people to speak to one another across long distances [1] . It was born out of people’s need to communicate across longer distances than were possible with human voice alone. Telecommunications have undergone a significant transformation due to technical advancement during the past three decades. For instance, people communicated with drums and smoke signals in the early days whereas today people use digital wireless networks to communicate. Later in the paper, telecommunication will be shortened to telecom.  \nThe World Health Organization (WHO) identified Covid-19 as a worldwide public health issue on January 30, 2020, and a pandemic on March 11, 2020 [2] . The Covid-19 pandemic is drastically affecting people’s health, lifestyle, economy, society and so on. When the pandemic first began, the government implemented various preventative measures to stop its spread, including a nationwide lockdown to prevent people from crowding. Therefore, the telecom industry  \nhas been playing an extra significant role in connecting people and delivering information and message during the lockdown. Consequently, the increased usage of the internet, video conferencing, and telephone services have created chances for the telecom industry to grow. According to [3], the revenue growth rate of California telecom companies such as Zoom Video Communications has increased dramatically from-7.1% in 2020 to 3.5% in 2022, which has grown 149.30% . Overall, telecommunication includes a variety of service providers such as telepho","cbCaiaKDwCqK4Gvr","https://ap.wps.com/l/cbCaiaKDwCqK4Gvr","pdf",1063962,1,22,"English","en",105,"# Introduction\n## Background of telecommunications and Covid-19 impact\n## Customer churn definition and industry challenge\n## Study objectives and machine learning approach","[{\"question\":\"Which machine learning algorithms are used to predict customer churn?\",\"answer\":\"The study uses six supervised algorithms: KNN, Random Forest, AdaBoost, Logistic Regression, XGBoost, and Support Vector Machine (SVM).\"},{\"question\":\"Why is customer churn considered important for telecom companies?\",\"answer\":\"Customer churn forces customers to switch providers, reducing growth and revenue; it becomes harder to retain customers as competition increases and markets become saturated.\"},{\"question\":\"What model performs best in predicting churn in this study?\",\"answer\":\"XGBoost is reported as the most effective classifier overall, with accuracy 79.67% and F1-score 57.57%.\"}]","Customer Churn Prediction of Telecom Company Using Machine Learning Algorithms - read online free | PDF",1785900719,55,{"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},"customer-churn-prediction-of-telecom-company-using-machine-learning-algorithms-read-online-free","",{"@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/customer-churn-prediction-of-telecom-company-using-machine-learning-algorithms-read-online-free/125698/",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","2026-08-05",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},"Which machine learning algorithms are used to predict customer churn?","Question",{"text":75,"@type":76},"The study uses six supervised algorithms: KNN, Random Forest, AdaBoost, Logistic Regression, XGBoost, and Support Vector Machine (SVM).","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why is customer churn considered important for telecom companies?",{"text":80,"@type":76},"Customer churn forces customers to switch providers, reducing growth and revenue; it becomes harder to retain customers as competition increases and markets become saturated.",{"name":82,"@type":73,"acceptedAnswer":83},"What model performs best in predicting churn in this study?",{"text":84,"@type":76},"XGBoost is reported as the most effective classifier overall, with accuracy 79.67% and F1-score 57.57%.","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"]