[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117267-en":3,"doc-seo-117267-105":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":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},117267,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Strategic Customer Segmentation - Harnessing Machine Learning For Retaining Satisfied Customers","This research paper investigates machine learning applications for strategic customer segmentation in the aviation industry, aiming to improve retention of satisfied customers. Using the Airline Passenger dataset, the study compares five classifiers—Random Forest, K-Nearest Neighbors (KNN), Decision Tree, Naive Bayes, and Artificial Neural Network (ANN). Each model is evaluated through accuracy, precision, recall, and F1-score to measure segmentation quality. Results show Random Forest achieves the strongest overall performance, followed closely by Decision Tree.","| \u003Cbr>\u003Cbr>Khalid et al. LGURJCSIT 2024\u003Cbr>LGU Research Journal of Computer Science & IT\u003Cbr>ISSN: 2521-0122 (Online)\u003Cbr>ISSN: 2519-7991 (Print)\u003Cbr>doi: 10.54692/lgurjcsit.2024.082573\u003Cbr>Vol (8): Issue (2), April  June 2024 |  |\n| --- | --- |\n|  |  |\n\nStrategic Customer Segmentation: Harnessing Machine Learning For Retaining Satisfied Customers  \nHira Khalid1*, Shazia Saqib1, 2, Muhammad Junaid Asif 2, Deshinta Arrova Dewi2  \n1Faculty of IT and Computer Sciences (FoIT & CS), University of Central Punjab, Lahore, Pakistan. 2Faculty of Data Science and Information Technology, INTI International University, Malaysia.  \n[Email: hkhalid99@gmail.com](Email: hkhalid99@gmail.com)  \nABSTRACT:  \nThis research paper explores the burgeoning field of machine learning and its application in strategic customer segmentation within the aviation industry. Leveraging the Airline Passenger dataset, this study assesses the potential of various machine learning classifiers to enhance customer retention by effectively segmenting satisfied customers. Our methodology involves a comparative analysis offive machine learning classifiers: Random Forest, K-Nearest Neighbors (KNN), Decision Tree, Naive Bayes, andArtificial Neural Network (ANN). Each classifier is rigorously tested and evaluated based on key performance metrics, including accuracy, precision, recall, and F1-score.  \nThe results indicate a diverse range ofclassifier effectiveness. Notably, the Random Forest classifier outperforms others with outstanding metrics: accuracy, precision, recall, and F1-score of 0.96. Decision Tree follows closely, achieving high performance with a score of 0.95 across all metrics. Naive Bayes and ANN demonstrate respectable performance, with accuracy scores of 0.86 and 0.90, respectively. In contrast, KNN presents lower but consistent performance, with all metrics at 0.75. These quantitative findings highlight the nuanced performance differences among classifiers, emphasizing the critical role of algorithm selection in achieving precise customer segmentation.  \nThis studyprovides significant insights into the application of machine learning for strategic customer retention in the aviation sector, presenting practical implications for airlines aiming to optimize their segmentation strategies and retain satisfied customers. By showcasing the varying performances of different classifiers, this research contributes to the broader discourse on integrating machine learning into customer-centric strategies, ultimately aiding airlines in engaging and retaining their customer base more effectively.  \nKEYWORDS: Customer Segmentation, Machine Learning, Aviation Industry, Retention Strategies .  \n1. INTRODUCTION  \nIn today's competitive business landscape, understanding and retaining satisfied customers are paramount for sustainable growth and success. With the advent of machine learning (ML) techniques, businesses have unprecedented opportunities to delve deeper into customer behaviors, preferences, and patterns. Lewaaelhamd et al. [1] Instead of analyzing the entire customer database, it's more effective to categorize cust[omers. by](omers. by)  \ncharacteristics like age or location and tailor marketing campaigns to each segment for personalized, relevant offers. One such powerful application is strategic customer segmentation, which enables businesses to tailor their strategies, products, and services to different customer segments, enhancing customer satisfaction and loyalty. Customer segmentation's transformative potential in marketing and decision-making is advocated by Thakkar et al. [2] . Based on behav-  \nLGU Research Journal of Computer Science & Information Technology, Vol (8): Issue (2), LGURJCSIT 31  \nior and demographics, segmentation allows for customized methods that improve product creation and marketing effectiveness. Division improves customer comprehension, sharpensofferings, and boosts profitability.  \nThis research paper focuses on integrating machine learning algorithms into strategic customer segmentation processes to optimize","cbCairrpt9SnaKmW","https://ap.wps.com/l/cbCairrpt9SnaKmW","pdf",19405245,1,11,"English","en",105,"# Introduction\n# Methodology and Classifiers\n## Evaluation Metrics\n# Results and Discussion\n# Conclusion and Practical Implications","[{\"question\":\"Which machine learning classifiers are compared for customer segmentation?\",\"answer\":\"The study compares Random Forest, K-Nearest Neighbors (KNN), Decision Tree, Naive Bayes, and Artificial Neural Network (ANN).\"},{\"question\":\"How is classifier performance evaluated in the study?\",\"answer\":\"Performance is assessed using accuracy, precision, recall, and F1-score across the segmentation task.\"},{\"question\":\"Which classifier performs best for retaining satisfied customers?\",\"answer\":\"Random Forest outperforms the others, reaching accuracy, precision, recall, and F1-score of 0.96 in the reported results.\"}]",1785674877,28,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"strategic-customer-segmentation-harnessing-machine-learning-for-retaining-satisfied-customers","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/strategic-customer-segmentation-harnessing-machine-learning-for-retaining-satisfied-customers/117267/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Which machine learning classifiers are compared for customer segmentation?","Question",{"text":74,"@type":75},"The study compares Random Forest, K-Nearest Neighbors (KNN), Decision Tree, Naive Bayes, and Artificial Neural Network (ANN).","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How is classifier performance evaluated in the study?",{"text":79,"@type":75},"Performance is assessed using accuracy, precision, recall, and F1-score across the segmentation task.",{"name":81,"@type":72,"acceptedAnswer":82},"Which classifier performs best for retaining satisfied customers?",{"text":83,"@type":75},"Random Forest outperforms the others, reaching accuracy, precision, recall, and F1-score of 0.96 in the reported results.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":45,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":45,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":45,"category_name":136,"show_sort_weight":105,"slug":137},19,"General","general"]