[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117521-en":3,"doc-seo-117521-105":29,"detail-sidebar-cat-0-en-105":90},{"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":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},117521,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine learning methods applicable in customer lifecycle management - Research paper","Globalization and digitalization shape today’s business environment, driving organizations to extract actionable knowledge about customers throughout their lifecycle. This research examines machine learning as an AI approach for learning from customer data across stages, targeting classification-type problems in the telecommunications services market. It evaluates supervised machine learning methods and justifies suitable approaches for modeling changing customer behavior. Findings highlight the classification accuracy and model performance advantages of boosting and bagging ensemble classifiers.","Machine learning methods applicable in customer lifecycle management  \nDesislava Koleva, PhD student  \nUniversity of Economics-Varna, Varna, Bulgaria  \n[desi_koleva@ue-varna.bg](desi_koleva@ue-varna.bg)  \nAbstract  \nIn the current business environment, globalization and digitalization are the main distinguishing features of the world economy. Machine learning, as a field of artificial intelligence, has gained wide popularity as a technology used in the process of extracting knowledge about customers at different stages of their life cycle.  \nThe purpose of this research paper is to explore the types of machine learning and their applications in the study of dependencies related to changing the behavior of customers in the telecommunication services market, as well as the justification of an appropriate type of machine learning method applicable to these studies. The focus of the study is aimed at the application of supervised machine learning methods, at different stages of the customer lifecycle, to solve problems that can be categorized as classification. The study shows the advantage of boosting and bagging ensemble classifiers in terms of correct classification of specimens and model accuracy. Recommendations for future research are also defined.  \nKeywords: Machine learning, customer lifecycle, machine learning methods, binary classification problem, machine learning algorithms  \nJEL Code: O33  \nDOI: 10.56065/IJUSV-ESS/2024.13.1.324  \nIntroduction  \nIn modern conditions, globalization and digitalization are the main distinguishing features of the world economy. One of the most valuable economic assets of organizations is the knowledge gained about the company's customers, allowing them to gain a competitive advantage and ensuring the sustainable development of the organization in an extremely dynamic market environment. More and more companies are investing in AI applications to increase the efficiency of their customer relationship management systems. The formed knowledge about them is not limited only within the organization, but becomes global, international and requires the application of state-of-the-art methods and methodologies in order to it’s find, extract, store, process and use. Machine learning isone of the areas of artificial intelligence and encompasses a wide range of methods and technologies that can be applied in the process of extracting knowledge related to managing an organization's relationships with its customers.  \nThe purpose of the report is to consider the nature and types of machine learning, their applications in the study of dependencies related to changing the behavior of customers in the telecommunication services market, as well as the justification of an appropriate type of machine learning method applicable to these studies.  \n1. Machine Learning  \n1.1. Тhe concept of machine learning  \nThere are different definitions of machine learning (ML) in the scientific community. Some authors define it as a field of artificial intelligence, representing adaptive learning from data to identify patterns and patterns that help make better predictions and decision-making (Corsi, Nham, Kassis, and El-Othmani, 2024) . Other authors define it as powerful techniques that can be learned from experience and whose algorithm accumulates more experience in the form of data from observations or interactions with the environment. This leads to an improvement in their performance. (Zhang, Lipton, Li and Smola, 2023) In the literature, there are also definitions, including the field of artificial intelligence, including the development of algorithms to detect trends and patterns in existing data,  \nand subsequently this information can be used to create predictions for new data (Vieira, Pinaya and Mechelli, 2020). Machine learning is also defined as an automated process that extracts models from data (Kelleher, Mac Namee and D’Arcy, 2020) and also a system that is in a changing environment and must have the abilit","cbCainFFdW8zU9uD","https://ap.wps.com/l/cbCainFFdW8zU9uD","pdf",362677,1,"English","en",105,"# Abstract\n# Introduction\n# Machine Learning\n## The concept of machine learning\n## Machine learning methods","[{\"question\":\"What is the main research purpose of this paper?\",\"answer\":\"To explore machine learning types and their applications in studying dependencies behind changing customer behavior in the telecommunications services market, and to justify suitable machine learning methods for these studies.\"},{\"question\":\"Which machine learning approach does the paper focus on?\",\"answer\":\"The paper focuses on supervised machine learning methods across different stages of the customer lifecycle to solve classification problems.\"},{\"question\":\"What ensemble methods are reported as advantageous in the study?\",\"answer\":\"The study shows advantages of boosting and bagging ensemble classifiers regarding correct classification and overall model accuracy.\"}]","Machine learning methods applicable in customer lifecycle management - 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