{"title":"使用支持向量机模型实现RFM分析","authors":"Ananthi Sheshasaayee, L. Logeshwari","doi":"10.1109/I-SMAC.2018.8653758","DOIUrl":null,"url":null,"abstract":"In the modern business customer response is one of the vital characteristics of services. The customer relationship management accurately predict the invaluable customer. Because attention is needed to rate low response rating customers. Most of the direct marketing sectors randomly select and reduce degree of the influencing problem. But online marketing sectors face more difficulties to identify customer responses. This paper proposes SVM model based on the RFM values and also according to the monetary value to predict recency and frequency weights.","PeriodicalId":53631,"journal":{"name":"Koomesh","volume":"11 1","pages":"760-763"},"PeriodicalIF":0.0000,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"IMPLEMENTATION OF RFM ANALYSIS USING SUPPORT VECTOR MACHINE MODEL\",\"authors\":\"Ananthi Sheshasaayee, L. Logeshwari\",\"doi\":\"10.1109/I-SMAC.2018.8653758\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the modern business customer response is one of the vital characteristics of services. The customer relationship management accurately predict the invaluable customer. Because attention is needed to rate low response rating customers. Most of the direct marketing sectors randomly select and reduce degree of the influencing problem. But online marketing sectors face more difficulties to identify customer responses. This paper proposes SVM model based on the RFM values and also according to the monetary value to predict recency and frequency weights.\",\"PeriodicalId\":53631,\"journal\":{\"name\":\"Koomesh\",\"volume\":\"11 1\",\"pages\":\"760-763\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Koomesh\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/I-SMAC.2018.8653758\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Koomesh","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/I-SMAC.2018.8653758","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Medicine","Score":null,"Total":0}
IMPLEMENTATION OF RFM ANALYSIS USING SUPPORT VECTOR MACHINE MODEL
In the modern business customer response is one of the vital characteristics of services. The customer relationship management accurately predict the invaluable customer. Because attention is needed to rate low response rating customers. Most of the direct marketing sectors randomly select and reduce degree of the influencing problem. But online marketing sectors face more difficulties to identify customer responses. This paper proposes SVM model based on the RFM values and also according to the monetary value to predict recency and frequency weights.