Alfian, Ganjar and Octava, Muhammad Qois Huzyan and Hilmy, Farhan Mufti and Nurhaliza, Rachma Aurya and Saputra, Yuris Mulya and Putri, Divi Galih Prasetyo and Syahrian, Firma and Fitriyani, Norma Latif and Atmaji, Fransiskus Tatas Dwi and Farooq, Umar and Nguyen, Dat Tien and Syafrudin, Muhammad (2023) Customer Shopping Behavior Analysis Using RFID and Machine Learning Models. Information (Switzerland), 14 (10). ISSN 20782489
Full text not available from this repository. (Request a copy)Abstract
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. © 2023 by the authors.
Item Type: | Article |
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Additional Information: | Cited by: 3; All Open Access, Gold Open Access |
Uncontrolled Keywords: | Anomaly detection; Balancing; Public relations; Retail stores; Sales; Statistics; Behavior analysis; Customer relationships; Data balancing; Machine learning models; Machine-learning; Outlier Detection; Physical stores; Receive signal strength; Shopping behaviour; Signal strengths; Machine learning |
Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
Divisions: | Vocational School |
Depositing User: | Sri JUNANDI |
Date Deposited: | 05 Nov 2024 01:28 |
Last Modified: | 05 Nov 2024 01:28 |
URI: | https://ir.lib.ugm.ac.id/id/eprint/10482 |