[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118574-en":3,"doc-seo-118574-105":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},118574,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Unveiling Gold Membership Classification Using Machine Learning","Loyalty programs face a core constraint: choosing which customers to target when marketing budgets are limited. This study evaluates multiple machine-learning classification models to improve gold membership promotion for customers with prior transaction histories. It compares decision-tree, random-forest, and logistic-regression approaches while testing gradient boosting methods, where the Gradient Boost algorithm achieves the strongest performance at about 88% accuracy. Key drivers are recency of last visit, wine and meat transaction counts, marital status, and offline store transaction numbers, enabling automated, efficient customer selection and better resource allocation under budget constraints.","INTERNATIONAL JOURNAL ON INFORMATICS VISUALIZATION  \n[journal homepage : www.joiv.org/index.php/joiv](journal homepage : www.joiv.org/index.php/joiv)  \nUnveiling Gold Membership Classification Using Machine Learning  \nVincencius Christiano Tjokro a, Raymond Sunardi Oetama a,*, Iwan Prasetiawan a  \nInformation System Department, Universitas Multimedia Nusantara, Tangerang, Indonesia  \n*Corresponding author: [raymond@umn.ac.id](raymond@umn.ac.id)  \nAbstract—The main challenge in loyalty programs is selecting customers with limited funding. To address it, we explore various machine learning-based classification models. This study aims to enhance the effectiveness of a marketing strategy that promotes gold membership to customers with prior transaction history. Previously, much research applied decision trees, random forests, and logistic regression for classification, but gradient boosting is still unpopular. However, in this study, the Gradient Boost algorithm exhibits the best performance among these models, achieving an impressive accuracy of around 88%. This result underscores the model's capability to classify customers, thereby suggesting its potential to significantly enhance the marketing strategy's effectiveness. The analysis identifies crucial features that influence the model's predictive capabilities. Notably, the recency of the last visit, the number of transactions involving wine and meat, marital status, and the number of offline store transactions are identified as influential factors. Leveraging machine learning techniques enables the automation of the customer selection process, facilitating the attraction of a more extensive customer base. By targeting those customers most likely to respond positively to the gold membership offer, efficient resource allocation can be achieved. This research provides valuable insights and practical recommendations for implementing an effective marketing strategy under resource constraints. Combining machine learning algorithms and feature identification enables efficient targeting of potential customers, maximizing the impact of the gold membership offering. Implementing the findings ofthis study could lead to increased customer acquisition and improved overall business performance.  \nKeywords—Customer selection; gold membership classification; gradient boosting; machine learning; marketing strategy.  \nManuscript received 31 Jul. 2023; revised 16 Oct. 2023; accepted 2 Apr. 2024. Date of publication 31 Dec. 2024.  \nInternational Journal on Informatics Visualization is licensed under a Creative Commons Attribution-Share Alike 4.0 International License.  \nI. INTRODUCTION  \nLoyalty programs in industries with low switching costs have the potential to drive differentiation and sustain a competitive advantage. However, the incentives provided through these programs also carry the risk of escalating into costly price wars [1]. Businesses must carefully manage the incentives provided to prevent potential price wars and ensure the program's and industry's long-term viability. There are challenges in customer targeting within loyalty programs using promotional tools for loyalty programs [2].  \nGold-class membership applies to Loyalty programs [3] . However, the Company faces challenges in optimizing its marketing budget [4] and maximizing its effectiveness to attract as many members as possible [5]. The Company requires a method to understand customer behavior. This method would involve analyzing and interpreting customer data to gain insights into their preferences.  \nMachine learning algorithms possess substantial potential as a fundamental technique for data classification. Machine learning has gained significant popularity as a technique for  \nclassification. Analyzing customer behavior can be conducted through classification methods [6]. Machine learning can be used to classify customer loyalty [7], identify and categorize various types of scam activities[8], or classify images based","cbCaicau2n7UgeBK","https://ap.wps.com/l/cbCaicau2n7UgeBK","pdf",3684829,1,"English","en",105,"# Introduction\n## Loyalty program challenges and customer targeting\n## Gold membership marketing constraints and customer behavior understanding\n## Machine learning methods for customer classification\n## Prior research and related classification approaches","[{\"question\":\"What problem does this study address in gold membership promotions?\",\"answer\":\"The study targets the difficulty of selecting customers for loyalty offers when marketing budgets are limited, aiming to improve the effectiveness of gold membership marketing.\"},{\"question\":\"Which machine learning model performs best and what accuracy is reported?\",\"answer\":\"The Gradient Boost (gradient boosting) algorithm shows the best performance among the tested models, reaching around 88% accuracy.\"},{\"question\":\"What features are identified as influential for predicting customer responsiveness?\",\"answer\":\"The recency of the last visit, counts of transactions involving wine and meat, marital status, and the number of offline store transactions are highlighted as influential predictors.\"}]","Unveiling Gold Membership Classification Using Machine Learning | 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