Analysis of the behavior of customers in the social networks using data mining techniques
Leidys del Carmen Contreras Chinchilla, Kevin Andrey Rosales Ferreira · Advances in Social Networks Analysis and Mining · 2016
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.