{"title":"基于K-Means算法的改进RFM客户细分模型研究","authors":"Yong Huang, Mingzhen Zhang, Yue He","doi":"10.1109/ICCIA49625.2020.00012","DOIUrl":null,"url":null,"abstract":"The RFM model used for customer segmentation in the traditional retail industry is not suitable for the industry with distinct attributes of social groups, so the RFMC model is created by introducing the parameter C of social relations. Educational e-commerce enterprise M is selected for empirical study, and k-means algorithm is used for cluster analysis of valid customers of enterprise M, which resulted in 5 distinct customer groups and verified the effectiveness of the model.","PeriodicalId":237536,"journal":{"name":"2020 5th International Conference on Computational Intelligence and Applications (ICCIA)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Research on improved RFM customer segmentation model based on K-Means algorithm\",\"authors\":\"Yong Huang, Mingzhen Zhang, Yue He\",\"doi\":\"10.1109/ICCIA49625.2020.00012\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The RFM model used for customer segmentation in the traditional retail industry is not suitable for the industry with distinct attributes of social groups, so the RFMC model is created by introducing the parameter C of social relations. Educational e-commerce enterprise M is selected for empirical study, and k-means algorithm is used for cluster analysis of valid customers of enterprise M, which resulted in 5 distinct customer groups and verified the effectiveness of the model.\",\"PeriodicalId\":237536,\"journal\":{\"name\":\"2020 5th International Conference on Computational Intelligence and Applications (ICCIA)\",\"volume\":\"47 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 5th International Conference on Computational Intelligence and Applications (ICCIA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCIA49625.2020.00012\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 5th International Conference on Computational Intelligence and Applications (ICCIA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCIA49625.2020.00012","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Research on improved RFM customer segmentation model based on K-Means algorithm
The RFM model used for customer segmentation in the traditional retail industry is not suitable for the industry with distinct attributes of social groups, so the RFMC model is created by introducing the parameter C of social relations. Educational e-commerce enterprise M is selected for empirical study, and k-means algorithm is used for cluster analysis of valid customers of enterprise M, which resulted in 5 distinct customer groups and verified the effectiveness of the model.
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