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Analysis of customer segmentation based on broad learning system
Wang, Zhenyu1; Zuo, Yi1; Li, Tieshan1; Philip Chen, C. L.2; Yada, Katsutoshi3
2019-12-01
Source Publication2019 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2019
Pages75-80
AbstractIn the field of retail industry and marketing, identifying customer segments is one of the most important tasks. A meaningful segmentation is able to help the managers to enhance the quality of products and services for the targeting segments. Most of traditional methods used POS data to classify the customer loyalty as 'heavy' segment while others are belonging to 'light' segment. Based on the previous studies, this paper presents three improvements. Firstly, in addition to customer purchasing behavior, we also include RFID (Radio Frequency IDentification) data, which can accurately represent the consumers' in-store behavior. Secondly, this paper uses broad learning system (BLS) to analyze the consumer segmentation. BLS is one of the most state-of-the-art machine learning techniques, and quite efficient and effective for classification tasks. Thirdly, the customer behavior data used in this paper are collected from a real-world supermarket in Japan. We also consider the customer segmentation as a multi-label classification problem based on both of POS data and RFID data. In the experiment, the results were compared with other popular classification models, such as neural network and support vector machine, and it was found that BLS greatly reduced training time while guaranteeing accuracy.
KeywordConsumer behavior Customer segmentation In-store behavior Machine learning Multi-label classification
DOI10.1109/SPAC49953.2019.237870
URLView the original
Language英語English
Scopus ID2-s2.0-85086253882
Fulltext Access
FWCI0.5602022
Citation statistics
Document TypeConference paper
CollectionUniversity of Macau
Corresponding AuthorPhilip Chen, C. L.
Affiliation1.Dalian Maritime University, Navigation College, Dalian, China
2.University of MacauSouth China University of Technology, Faculty of Science and Technology, Macao
3.Kansai University, Faculty of Business and Commerce, Osaka, Japan
Corresponding Author AffilicationFaculty of Science and Technology
Recommended Citation
GB/T 7714
Wang, Zhenyu,Zuo, Yi,Li, Tieshan,et al. Analysis of customer segmentation based on broad learning system[C],2019:75-80.
APA Wang, Zhenyu,Zuo, Yi,Li, Tieshan,Philip Chen, C. L.,&Yada, Katsutoshi.(2019).Analysis of customer segmentation based on broad learning system.2019 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2019,75-80.
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