Customer Segment Classification Prediction in the Australian Retail Based on Machine Learning Algorithms

doi:10.1145/3650215.3650302

Details of Research Outputs

Title	Customer Segment Classification Prediction in the Australian Retail Based on Machine Learning Algorithms
Creator	Luo，Yiming 1; Zhang，Rui 2; Wang，Fei 3; Wei，Tao 4
Date Issued	2023-10-27
Source Publication	ACM International Conference Proceeding Series
Pages	498-503
Abstract	Customer segmentation is an essential area of business analytics today. Accurate customer segmentation is access to improves the efficiency of marketing campaigns and customer satisfaction. This study employs multiple machine learning methods to classify Australian Retail company BIGW's customer segments and first to apply multiple model explanation methods to find insights related to customer segmentation identification. After rigorous comparison and hyperparameter fine-tuning, XGBoost is the most adept for this dataset. We derive three key insights through the model results and model interpretive methods. First, BIGW's primary clientele comprises young families in urban areas who prefer cost-effective products, establishing the foundation of their consumer base. Second, the model result indicates a notable gap in BIGW's understanding of its high-end customers, suggesting an area for immediate attention. Third, a specific customer segment emerges from the data: individuals favoring online shopping, demonstrating high total expenditure but low interest in promotions, representing a high-value segment.
Keyword	Australian Retail Customer segment classification machine learning model interpretation
DOI	10.1145/3650215.3650302
URL	View source
Language	英语English
Scopus ID	2-s2.0-85191301409
Citation statistics
Document Type	Conference paper
Identifier	http://repository.uic.edu.cn/handle/39GCC9TT/11559
Collection	Beijing Normal-Hong Kong Baptist University
Corresponding Author	Luo，Yiming
Affiliation	1.Business Schoo,The University of Sydney,Sydney,Australia 2.Computer and Information Sciences Program,Harrisburg University of Science and Technology,Harrisburg,United States 3.Faculty of Science and Technology,Beijing Normal University-Hong Kong Baptist University United International College,Zhuhai,China 4.School of Chemical and Environmental Engineering,Sichuan University of Science and Engineering,Zigong,China
Recommended Citation GB/T 7714	Luo，Yiming,Zhang，Rui,Wang，Feiet al. Customer Segment Classification Prediction in the Australian Retail Based on Machine Learning Algorithms[C], 2023: 498-503.

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