Refined Push Algorithm of Marketing Data Based on Data Mining Algorithm

Zhen-hong Xie
{"title":"Refined Push Algorithm of Marketing Data Based on Data Mining Algorithm","authors":"Zhen-hong Xie","doi":"10.1145/3510858.3510883","DOIUrl":null,"url":null,"abstract":"Data mining algorithm is a very popular algorithm now. Today, with the popularization of e-commerce models, the relationship between traditional enterprises and customers has undergone certain changes, and enterprises need to be customized according to customer needs. Based on the data mining algorithm, this paper conducts an in-depth study of the marketing data refined push algorithm, and finds the balance point in line with the relationship between the enterprise and the customer. This research is in the specific marketing process, applying data mining algorithms and related technologies to analyze the collected customer information, and formulating appropriate refined and private marketing strategies. Using this algorithm can achieve long-term and effective results with customers. cooperate. Experimental results show that customer relationship management is now one of the most important issues for enterprises to consider. The accuracy and recall rates of customer data information are 0.102875 and 0.095874, respectively. Now the relationship with customers is effectively maintained and corresponding. The push has gradually attracted attention from many aspects.","PeriodicalId":6757,"journal":{"name":"2021 IEEE 3rd International Conference on Civil Aviation Safety and Information Technology (ICCASIT)","volume":"23 3 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2021-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE 3rd International Conference on Civil Aviation Safety and Information Technology (ICCASIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3510858.3510883","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Data mining algorithm is a very popular algorithm now. Today, with the popularization of e-commerce models, the relationship between traditional enterprises and customers has undergone certain changes, and enterprises need to be customized according to customer needs. Based on the data mining algorithm, this paper conducts an in-depth study of the marketing data refined push algorithm, and finds the balance point in line with the relationship between the enterprise and the customer. This research is in the specific marketing process, applying data mining algorithms and related technologies to analyze the collected customer information, and formulating appropriate refined and private marketing strategies. Using this algorithm can achieve long-term and effective results with customers. cooperate. Experimental results show that customer relationship management is now one of the most important issues for enterprises to consider. The accuracy and recall rates of customer data information are 0.102875 and 0.095874, respectively. Now the relationship with customers is effectively maintained and corresponding. The push has gradually attracted attention from many aspects.
基于数据挖掘算法的营销数据精细化推送算法
数据挖掘算法是目前非常流行的一种算法。在电子商务模式普及的今天,传统企业与客户的关系发生了一定的变化,企业需要根据客户的需求进行定制。本文基于数据挖掘算法,对营销数据精细化推送算法进行了深入研究,找到了符合企业与客户关系的平衡点。本研究是在具体的营销过程中,运用数据挖掘算法及相关技术对收集到的客户信息进行分析,并制定相应的精细化、私人化营销策略。使用该算法可以获得长期有效的客户服务效果。合作。实验结果表明,客户关系管理是目前企业需要考虑的重要问题之一。客户数据信息的准确率和召回率分别为0.102875和0.095874。现在与客户的关系得到了有效的维护和对应。这一推动逐渐引起了多方面的关注。
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 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信