Customer Shopping Behavior Analysis Using RFID and Machine Learning Models

IF 2.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Information (Switzerland) Pub Date : 2023-10-08 DOI:10.3390/info14100551
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
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引用次数: 1

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.
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基于RFID和机器学习模型的顾客购物行为分析
分析顾客在实体店的购物习惯对于加强零售商与顾客的关系和增加商业收入至关重要。然而,与在线商店相比,在实体店收集客户浏览活动的数据可能具有挑战性。这项研究建议在商店货架上使用RFID技术和机器学习模型来分析零售商店的顾客浏览活动。该研究使用RFID标签来跟踪产品运动,并使用标签的接收信号强度(RSS)收集客户行为数据。然后从RSS数据中提取时域特征,并利用机器学习模型对不同的顾客购物活动进行分类。我们提出了森林异常点检测、ADASYN数据平衡和多层感知器(MLP)的集成。结果表明,该模型的准确率提高了97.778%,准确率提高了98.008%,特异性提高了98.333%,召回率提高了98.333%,f1-score提高了97.750%。最后,我们展示了将这个训练过的模型集成到基于web的应用程序中的过程。这一结果可以帮助管理者了解顾客的偏好,并有助于产品植入、促销和顾客推荐。
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来源期刊
Information (Switzerland)
Information (Switzerland) Computer Science-Information Systems
CiteScore
6.90
自引率
0.00%
发文量
515
审稿时长
11 weeks
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