Customer Shopping Behavior Analysis Using RFID and Machine Learning Models
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/14/10/551 |
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| author | Ganjar Alfian Muhammad Qois Huzyan Octava Farhan Mufti Hilmy Rachma Aurya Nurhaliza Yuris Mulya Saputra Divi Galih Prasetyo Putri Firma Syahrian Norma Latif Fitriyani Fransiskus Tatas Dwi Atmaji Umar Farooq Dat Tien Nguyen Muhammad Syafrudin |
| author_facet | Ganjar Alfian Muhammad Qois Huzyan Octava Farhan Mufti Hilmy Rachma Aurya Nurhaliza Yuris Mulya Saputra Divi Galih Prasetyo Putri Firma Syahrian Norma Latif Fitriyani Fransiskus Tatas Dwi Atmaji Umar Farooq Dat Tien Nguyen Muhammad Syafrudin |
| author_sort | Ganjar Alfian |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T21:11:08Z |
| format | Article |
| id | doaj.art-c03b94cdf0ca4027b929bca0d02f02d5 |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-10T21:11:08Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-c03b94cdf0ca4027b929bca0d02f02d52023-11-19T16:48:04ZengMDPI AGInformation2078-24892023-10-01141055110.3390/info14100551Customer Shopping Behavior Analysis Using RFID and Machine Learning ModelsGanjar Alfian0Muhammad Qois Huzyan Octava1Farhan Mufti Hilmy2Rachma Aurya Nurhaliza3Yuris Mulya Saputra4Divi Galih Prasetyo Putri5Firma Syahrian6Norma Latif Fitriyani7Fransiskus Tatas Dwi Atmaji8Umar Farooq9Dat Tien Nguyen10Muhammad Syafrudin11Department of Electrical Engineering and Informatics, Vocational College, Universitas Gadjah Mada, Yogyakarta 55281, IndonesiaDepartment of Electrical Engineering and Informatics, Vocational College, Universitas Gadjah Mada, Yogyakarta 55281, IndonesiaDepartment of Electrical Engineering and Informatics, Vocational College, Universitas Gadjah Mada, Yogyakarta 55281, IndonesiaDepartment of Electrical Engineering and Informatics, Vocational College, Universitas Gadjah Mada, Yogyakarta 55281, IndonesiaDepartment of Electrical Engineering and Informatics, Vocational College, Universitas Gadjah Mada, Yogyakarta 55281, IndonesiaDepartment of Electrical Engineering and Informatics, Vocational College, Universitas Gadjah Mada, Yogyakarta 55281, IndonesiaDepartment of Electrical Engineering and Informatics, Vocational College, Universitas Gadjah Mada, Yogyakarta 55281, IndonesiaDepartment of Data Science, Sejong University, Seoul 05006, Republic of KoreaIndustrial and System Engineering School, Telkom University, Bandung 40257, IndonesiaFaculty of Business and Law, Coventry University, Coventry CV1 5FB, UKFaculty of Electrical and Electronic Engineering, Phenikaa University, Yen Nghia, Ha Dong, Hanoi 12116, VietnamDepartment of Artificial Intelligence, Sejong University, Seoul 05006, Republic of KoreaAnalyzing 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.https://www.mdpi.com/2078-2489/14/10/551shopping behaviorRFIDRSSmachine learningoutlier detectiondata balancing |
| spellingShingle | Ganjar Alfian Muhammad Qois Huzyan Octava Farhan Mufti Hilmy Rachma Aurya Nurhaliza Yuris Mulya Saputra Divi Galih Prasetyo Putri Firma Syahrian Norma Latif Fitriyani Fransiskus Tatas Dwi Atmaji Umar Farooq Dat Tien Nguyen Muhammad Syafrudin Customer Shopping Behavior Analysis Using RFID and Machine Learning Models Information shopping behavior RFID RSS machine learning outlier detection data balancing |
| title | Customer Shopping Behavior Analysis Using RFID and Machine Learning Models |
| title_full | Customer Shopping Behavior Analysis Using RFID and Machine Learning Models |
| title_fullStr | Customer Shopping Behavior Analysis Using RFID and Machine Learning Models |
| title_full_unstemmed | Customer Shopping Behavior Analysis Using RFID and Machine Learning Models |
| title_short | Customer Shopping Behavior Analysis Using RFID and Machine Learning Models |
| title_sort | customer shopping behavior analysis using rfid and machine learning models |
| topic | shopping behavior RFID RSS machine learning outlier detection data balancing |
| url | https://www.mdpi.com/2078-2489/14/10/551 |
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