What are the key determining factors that guarantee an artist\u2019s success?<\/p>\n
With the exploding popularity of BTS in the recent years, especially their growth in the Western music scene, the blog post (linked below) explored what sets BTS apart from the hundreds of K-pop groups, and from other Western artists.<\/p>\n
In his blog post, the author concluded from his analysis on the music, rather than on the artist, that what makes BTS\u2019s songs unique is in fact their \u2018energy\u2019, higher ratio of vocal parts vs lower ratio of instrumental parts, and the diverse tempo offerings from song to song, which resonate better with the mainstream audiences.<\/p>\n
Though the author focuses on using data analysis to identify the \u2018secret sauce\u2019 that makes BTS\u2019s songs unique, I believe that there are further applications of data and people analytics that would impact upstream portion of the K-pop value chain.<\/p>\n
<\/p>\n
Mapping Backward to Music Production<\/strong><\/p>\n In comparison to other traits and characteristics of the artists, their music and its success are the most quantifiable outputs. In the K-pop scene, it is less common for artists to self-produce\/compose their releases, but rather their companies will source the music from different producers\/studios.<\/p>\n Using the analysis in the referenced post, entertainment companies could not only identify the musical traits of the chart toppers, but also see the trend of listeners\u2019 music consumption behavior over time. With this data, the companies could backtrack to identify the producers who have previously produced similar style of music, and source more songs from them, or even recruit them. The company could also guide their in-house producers to produce music that reflects the current trend.<\/p>\n The downfall of this application is that, if the new releases converge on the same traits that were once unique, will any of the songs stand out and make the same impact as BTS\u2019s songs?<\/p>\n The referenced post ended by hinting further explorations on the lyrics and how they contribute to BTS\u2019 uniqueness. Though the author didn\u2019t mention his approach to this further exploration, I think he would use a form of NLP algorithm to identify the factors, such as: word counts, number of english words, overall sentiment of the song; that differentiate hit songs from other, less successful ones.<\/p>\n <\/p>\n Beyond Music<\/strong><\/p>\n Coming back to the main question of the article: what makes BTS successful in the Western market, in comparison to other K-pop groups? Undoubtably, artist\u2019s success depends on more than just their music. Their live performance, physical appearance, music videos, social media presence, and many other components all contribute to their success in the music scene.<\/p>\n In the K-pop system, where artists started off as trainees and went through years of training before making their debut, entertainment companies incur huge upfront investments to groom the trainees. Only a handful however made their debut, and even less became profitable for the company. How could an entertainment company better identify which individuals to bring onboard to the training program to minimize costs and maximize future returns?<\/p>\n I believe this is where people analytics has a great potential. By assessing the incoming trainees\u2019 tendency to persevere through the rigorous training program, their personality, as well as their talent, and mapping it to the ideal score based on successful artists in the industry, entertainment company could have a better screening method to recruit and invest in selected trainees that would potentially yield better returns for them down the road.<\/p>\n However, a problem for this approach is the fact that people are dynamic: perseverance could change, talent and skills could be improved with training. Thus, entertainment companies need to answer these preliminary questions before moving forward: Which independent variables to use? How to quantify or which proxy to use for each variable? Also, the company need to address the potential bias baked into the algorithm based on the algorithm makers\u2019 idea of what an \u201cideal\u201d artist is.<\/p>\n <\/p>\n Referenced Post: https:\/\/towardsdatascience.com\/the-data-science-of-k-pop-understanding-bts-through-data-and-a-i-part-1-50783b198ac2<\/p>\n","protected":false},"excerpt":{"rendered":" How data analytics and people analytics could potentially change the landscape of K-pop music industry. <\/p>\n","protected":false},"author":18859,"featured_media":476,"comment_status":"open","ping_status":"closed","template":"","categories":[427,507,431],"class_list":["post-468","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-data-analytics","category-kpop","category-people-analytics"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-peopleanalytics\/assignment\/lpa-2021-blog-assignment\/","yoast_head":"\n