Analysis of weakness of data validation from social CRM

Ali Ibrahim, Ermatita, Saparudin, Zefta Adetya
{"title":"Analysis of weakness of data validation from social CRM","authors":"Ali Ibrahim, Ermatita, Saparudin, Zefta Adetya","doi":"10.1109/ICODSE.2017.8285849","DOIUrl":null,"url":null,"abstract":"Developments of Technology are currently expanding rapidly, CRM technology has evolved into social CRM or CRM 2.0 utilizing from web 2.0. Social. Big social data is data derived from the activities of social network users. In the benefits of implementation of CRM, the process of validating data in analyzing the selected social media becomes a matter of concern. Because one of important in the implementation of social CRM is data. Therefore the goal of the research is to show result research in social CRM currently with comparing to see problem currently and giving the solution, furthermore describing opportunities social CRM for a company, government and all people which use technology to promote something to other people (customer). The characteristics of each social network need to be reviewed to the user. Social CRM is closely related to the behavior performed by the customer (social network users). Behavior and conditions in social media greatly affect the results to be achieved. Validating data from the result of analysis data is important to make result from implementation of social CRM can be better.","PeriodicalId":366005,"journal":{"name":"2017 International Conference on Data and Software Engineering (ICoDSE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 International Conference on Data and Software Engineering (ICoDSE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICODSE.2017.8285849","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 12

Abstract

Developments of Technology are currently expanding rapidly, CRM technology has evolved into social CRM or CRM 2.0 utilizing from web 2.0. Social. Big social data is data derived from the activities of social network users. In the benefits of implementation of CRM, the process of validating data in analyzing the selected social media becomes a matter of concern. Because one of important in the implementation of social CRM is data. Therefore the goal of the research is to show result research in social CRM currently with comparing to see problem currently and giving the solution, furthermore describing opportunities social CRM for a company, government and all people which use technology to promote something to other people (customer). The characteristics of each social network need to be reviewed to the user. Social CRM is closely related to the behavior performed by the customer (social network users). Behavior and conditions in social media greatly affect the results to be achieved. Validating data from the result of analysis data is important to make result from implementation of social CRM can be better.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
分析社交CRM数据验证的不足
当前科技发展迅速,客户关系管理技术已经从web 2.0发展到社会化客户关系管理或客户关系管理2.0。社会。社交大数据是来源于社交网络用户活动的数据。在实施CRM的好处中,在分析选定的社交媒体时验证数据的过程成为一个值得关注的问题。因为在实施社会化CRM的过程中,数据是非常重要的。因此,本研究的目标是展示目前社交CRM的结果研究,比较当前的问题并给出解决方案,进一步描述社交CRM为公司,政府和所有使用技术向其他人(客户)推广某些东西的人提供的机会。每个社交网络的特点都需要向用户进行回顾。社会化CRM与客户(社交网络用户)的行为密切相关。社交媒体中的行为和条件对要达到的结果有很大的影响。从分析数据的结果中验证数据对于使社交CRM的实施结果更好是非常重要的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Hybrid recommender system using random walk with restart for social tagging system Comparison of optimal path finding techniques for minimal diagnosis in mapping repair Cells identification of acute myeloid leukemia AML M0 and AML M1 using K-nearest neighbour based on morphological images Utility function based-mixed integer nonlinear programming (MINLP) problem model of information service pricing schemes Graph clustering using dirichlet process mixture model
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1