Analysis of the Behavior of Customers in the Social Networks Using Data Mining Techniques

被引：0

作者：

Contreras Chinchilla, Leidys del Carmen ^{[1
]}

Rosales Ferreira, Kevin Andrey ^{[1
]}

机构：

[1] Popular Univ Cesar, Dept Syst Engn, Valledupar, Colombia

来源：

PROCEEDINGS OF THE 2016 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING ASONAM 2016 | 2016年

关键词：

Data mining; Social networks; marketing strategies; customer behavior; clustering; k-means;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Companies today are developing business strategies taking into consideration behavior of their customers through social networks, which have allowed to extract large amounts of relevant data about users. This is why it has been necessary to apply data mining techniques to find patterns that describe the preferences of users in different contexts. This paper describes the results of using data mining techniques to analyze the behavior of customers of a fashion company in Instagram social network. The methodology used was CRISP-DM through which the descriptive models using the techniques of clustering and association rules were evaluated. The results shows that the proposed models can provide useful information to designing marketing strategies appropriate according to user preferences.

引用

页码：623 / 625

页数：3