[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120257-en":3,"doc-seo-120257-105":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},120257,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Customer Shopping Behavior Analysis Using RFID and Machine Learning Models","Analyzing customer shopping habits in physical stores is crucial for strengthening retailer–customer relationships and increasing revenue, yet it is difficult to collect in-store browsing data compared with online tracking. This study proposes using RFID technology on store shelves together with machine learning models to analyze customer browsing activity. RFID tags track product movement, while receive signal strength (RSS) is used to capture customer behavior. Time-domain features are extracted from RSS signals, then classification is performed using an integrated approach combining iForest outlier detection, ADASYN data balancing, and a multilayer perceptron (MLP) classifier. Results show substantial performance gains and support product placement, promotions, and recommendations, with deployment into a web-based application.","information   \nArticle  \nCustomer Shopping Behavior Analysis Using RFID and Machine Learning Models  \nGanjar Alfian 1, Muhammad Qois Huzyan Octava 1, Farhan Mufti Hilmy 1, Rachma Aurya Nurhaliza 1, Yuris Mulya Saputra 1, Divi Galih Prasetyo Putri 1, Firma Syahrian 1, Norma Latif Fitriyani 2, Fransiskus Tatas Dwi Atmaji 3, Umar Farooq 4, Dat Tien Nguyen 5 and Muhammad Syafrudin 6, *  \nCitation: Alfian, G.; Octava, M.Q.H.; Hilmy, F.M.; Nurhaliza, R.A.; Saputra, Y.M.; Putri, D.G.P.; Syahrian, F.; Fitriyani, N.L.; Atmaji, F.T.D.; Farooq, U.; et al. Customer Shopping Behavior Analysis Using RFID and Machine Learning Models.  \nInformation 2023, 14, 551. [https://](https://)[ ](https://)[doi.org/10.3390/info14100551](doi.org/10.3390/info14100551)  \nAcademic Editors: Christos Michalakelis, Mara Nikolaidou and Evangelia Filiopoulou  \nReceived: 7 September 2023  \nRevised: 3 October 2023  \nAccepted: 6 October 2023  \nPublished: 8 October 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Electrical Engineering and Informatics, Vocational College, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia  \n2 Department of Data Science, Sejong University, Seoul 05006, Republic of Korea  \n3 Industrial and System Engineering School, Telkom University, Bandung 40257, Indonesia  \n4 Faculty of Business and Law, Coventry University, Coventry CV1 5FB, UK  \n5 Faculty of Electrical and Electronic Engineering, Phenikaa University, Yen Nghia, Ha Dong, Hanoi 12116, Vietnam  \n6 Department of Artificial Intelligence, Sejong University, Seoul 05006, Republic of Korea  \n* [Correspondence: udin@sejong.ac.kr](Correspondence: udin@sejong.ac.kr)  \nAbstract: Analyzing customer shopping habits in physical stores is crucial for enhancing the retailer– customer relationship and increasing business revenue. However, it can be challenging to gather data on customer browsing activities in physical stores as compared to online stores. This study suggests using RFID technology on store shelves and machine learning models to analyze customer browsing activity in retail stores. The study uses RFID tags to track product movement and collects data on customer behavior using receive signal strength (RSS) of the tags. The time-domain features were then extracted from RSS data and machine learning models were utilized to classify different customer shopping activities. We proposed integration of iForest Outlier Detection, ADASYN data balancing and Multilayer Perceptron (MLP) . The results indicate that the proposed model performed better than other supervised learning models, with improvements of up to 97.778% in accuracy, 98.008% in precision, 98.333% in specificity, 98.333% in recall, and 97.750% in the f1-score. Finally, we showcased the integration of this trained model into a web-based application. This result can assist managers in understanding customer preferences and aid in product placement, promotions, and customer recommendations.  \nKeywords: shopping behavior; RFID; RSS; machine learning; outlier detection; data balancing  \n1. Introduction  \nComprehending customer behavior is essential for enterprises as it offers valuable insights into customer preferences and decision making, allowing companies to customize their offerings and marketing strategies to enhance customer satisfaction and loyalty [1] . While understanding customer shopping patterns in online stores is relatively straightforward, it becomes challenging in physical retail settings, where monitoring shopper behavior prior to checkout is complex. Utilizing Radio-Frequency Identification (RFID) technology is one approach to gaining insights into customer behavior.  \nRFID is a technology t","cbCaim0FOkvZ8XOv","https://ap.wps.com/l/cbCaim0FOkvZ8XOv","pdf",3950099,1,20,"English","en",105,"# Introduction\n## RFID for in-store behavior insights\n## RSS-based activity tracking and machine learning\n## Handling outliers and imbalanced datasets\n## Proposed integration and expected classification benefits","[{\"question\":\"Why is customer shopping behavior analysis harder in physical stores than online stores?\",\"answer\":\"Monitoring shopper behavior before checkout in physical retail is complex, making browsing activity data collection more challenging than in online settings.\"},{\"question\":\"How does the study use RFID to analyze in-store customer activity?\",\"answer\":\"RFID tags are used to track product movement, and customer behavior data are collected using the receive signal strength (RSS) from the tags.\"},{\"question\":\"What techniques are integrated in the proposed machine learning pipeline?\",\"answer\":\"The approach integrates iForest outlier detection, ADASYN for data balancing, and a multilayer perceptron (MLP) classifier to improve activity classification performance.\"}]","Customer Shopping Behavior Analysis Using RFID and Machine Learning Models | 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