LEVERAGING SOCIAL MEDIA METRICS IN IMPROVING SOCIAL MEDIA PERFORMANCES THROUGH ORGANIC REACH: A DATA MINING APPROACH

Jen-Peng Huang (), Genesis Sembiring Depari (), Srivandayuli Riorini () and Pai-Chouwang ()
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Jen-Peng Huang: Southern Taiwan University of Science and Technology, Taiwan, 710, Taiwan, R.O.C
Genesis Sembiring Depari: Southern Taiwan University of Science and Technology, Taiwan, 710, Taiwan, R.O.C
Srivandayuli Riorini: Trisakti University Jakarta,Jakarta, Indonesia
Pai-Chouwang: Southern Taiwan University of Science and Technology, Taiwan, 710,Taiwan, R.O.C,

Review of Economic and Business Studies, 2018, issue 22, 33-48

Abstract: This paper identified the relevance of several publication’s characteristics of each publication in reaching more people through organic strategy using Support Vector Machines. Before finding the relevance of several inputs, 10 potential models were examined. Based on the results of 10 models examination, we found that Comments, Likes and Shares have smallest error. However, those variables represent the customer engagements, instead of reaching more people. In the other side, Lifetime total organic reach is the best model compares to other models, therefore lifetime total organic reach was selected as a model. Furthermore, page total likes were found as the most relevance input in reaching more people through organic reach. The next most relevance inputs were followed by Type, month, day and hour of publication. Eventually, we come up with a managerial implication on how to publish a post in order to reach more people through organic reach.

Keywords: social media; data mining; organic reach; Facebook pages; Rapidminer (search for similar items in EconPapers)
JEL-codes: M1 M3 (search for similar items in EconPapers)
Date: 2018
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