Developing K POP localization strategy using Big Data

: Focusing on customer response via Twitter

Young in Lee a5159764952@gmail.com, Bachelor of Japanese Linguistics, Korea University, Thejoeun IT Academy

HyeJung.Moon  hyejung.moon@gmail.com Corresponding author, Ph D. of Public Policy, Adjunct Professor in Seoul University of Science & Technology, President of ILP, Director of Will-Be Solution

The purpose of this study is to analyze the leading group 's marketing strategies to make it possible for later runners to enter the overseas market more securely in relation to K POP, which is one of the most active exporting cultural contents in Korea. Many domestic entertainment companies are stepping up to pioneer overseas markets, but most of late runners' overseas activities often end in one time  tours. This is due to lack of preliminary research on the success factors of the target consumer group and only relying on the K POP trends.

 In this paper, we selected a group of first-runners who have succeeded in entering overseas for a long time based on the frequency of searches on Google  Trend. Each group was classified into Dog, Star, Cash Cow, and question mark using BCG Matrix based on the change in search frequency. Finally, we analyzed the strengths and weaknesses of the BTS with the strongest growth rate and the EXO with high stability. For next step we defined customer characteristics based on comparing issues experienced by each group in their overseas activities and customer's responses to them. The issue was based on the correlation between the news articles published on the time of the rapid change in search frequency on Google Trends. Customer Responses by Issue were collected based on Tweets. The impact of each issue was categorized by examining the ratios. As a result of the survey, the customers reacted very negatively to issues that threatened the identity of 'group' such as the withdrawal of members and  scandals, and they also showed a tendency to respond more positively to visual factors such as music video and SNS than musicality. In addition, we can confirm that the sensitivities of the above factors are different for each market.

Key words: big data, K POP, Twitter, network analysis, text mining