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题名Customer Segment Classification Prediction in the Australian Retail Based on Machine Learning Algorithms
作者
发表日期2023-10-27
会议录名称ACM International Conference Proceeding Series
页码498-503
摘要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.
关键词Australian Retail Customer segment classification machine learning model interpretation
DOI10.1145/3650215.3650302
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语种英语English
Scopus入藏号2-s2.0-85191301409
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文献类型会议论文
条目标识符https://repository.uic.edu.cn/handle/39GCC9TT/11559
专题北师香港浸会大学
通讯作者Luo,Yiming
作者单位
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
推荐引用方式
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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