[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119837-en":3,"doc-seo-119837-105":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},119837,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Customer Shopping Behavior Analysis Using RFID and Machine Learning Models","Analyzing customer shopping habits in physical stores strengthens retailer–customer relationships and supports revenue growth, yet browsing behavior data is harder to capture than in online settings. This study proposes deploying RFID technology on store shelves combined with machine learning to analyze customer browsing activity. RFID tags track product movement while receive signal strength (RSS) data enables extraction of time-domain features. Models including iForest outlier detection, ADASYN balancing, and multilayer perceptron classify activities, improving accuracy up to 97.778%. A web-based application integration supports practical product placement, promotions, and recommendations.","Customer Shopping Behavior Analysis Using RFID and Machine Learning Models  \nAlfian, G, Octava, MQH, Hilmy, FM, Nurhaliza, RA, Saputra, YM, Putri, DGP, Syahrian, F, Fitriyani, NL, Atmaji, FTD, Farooq, U, Nguyen, DT & Syafrudin, M  \nPublished PDF deposited in Coventry University’s Repository  \nOriginal citation:  \nAlfian, G, Octava, MQH, Hilmy, FM, Nurhaliza, RA, Saputra, YM, Putri, DGP, Syahrian, F, Fitriyani, NL, Atmaji, FTD, Farooq, U, Nguyen, DT & Syafrudin, M 2023, 'Customer Shopping Behavior Analysis Using RFID and Machine Learning Models', Information, vol. 14, no. 10, 551.  \n[https://dx.doi.org/10.3390/info14100551](https://dx.doi.org/10.3390/info14100551)  \n[DOI 10.3390/info14100551](DOI 10.3390/info14100551)[ ](DOI 10.3390/info14100551)[ISSN 2078-2489](ISSN 2078-2489)  \nPublisher: MDPI  \nThis is an Open Access article distributed under the terms of the Creative Commons Attribution License ( [http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.  \n 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 oth","cbCaiavld9LvbQiV","https://ap.wps.com/l/cbCaiavld9LvbQiV","pdf",2891267,1,21,"English","en",105,"# Introduction\n## RFID technology and IoT applications\n# Methodology\n## RFID-based data collection using RSS\n## Feature extraction from time-domain signals\n## Machine learning model pipeline (iForest, ADASYN, MLP)\n# Results\n## Classification performance and metrics\n# Web-based application integration\n## Manager support for placement and recommendations","[{\"question\":\"Why is analyzing customer shopping behavior in physical stores more challenging than in online stores?\",\"answer\":\"Online platforms make customer activity easier to observe, while physical retail requires complex monitoring before checkout to capture browsing behavior effectively.\"},{\"question\":\"How does the study collect and represent data using RFID?\",\"answer\":\"RFID tags are placed on store shelves/items to track movement, and receive signal strength (RSS) from the tags is recorded to capture customer-related browsing activity.\"},{\"question\":\"Which approach is proposed for classifying customer shopping activities, and how well does it perform?\",\"answer\":\"The pipeline integrates iForest outlier detection, ADASYN for data balancing, and a multilayer perceptron to classify activities, achieving improvements up to 97.778% in accuracy and around 98% for several other metrics.\"}]","Customer Shopping Behavior Analysis Using RFID and Machine Learning Models | 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