Korean pop music (K-pop) has exploded in popularity around the world, first spreading to neighboring countries before successfully expanding into countries with greater cultural distances, challenging conventional wisdom. This thesis explains the factors that drive K-pop’s unique success in the United States. By applying qualitative models like CAGE and Mode of Entry models from multinational management, this study finds that K-pop firms have relied on strategic partnerships to distribute music and mitigate cultural and administrative risks, but future goals of owning digital platforms and exporting the K-pop business model will require a stronger presence. This study also extracts sonic features from Spotify and uses machine learning techniques to perform a logistic regression and determine if the novelty or typicality of K-pop songs compared to popular American songs is predictive of international success. This research then performs a Latent Dirichlet Allocation (LDA) topic model on K-pop song lyrics to determine if there are differences in topic distributions between internationally successful K-pop songs and just domestically successful K-pop songs. The results are compared against those of another topic model performed on Billboard song lyrics. The findings indicate that typicality is a positive, statistically significant predictor of international success, and internationally successful K-pop songs over-represent specifically on topics that also appear in American songs compared to domestically successful K-pop songs. These findings shed light on aspects of K-pop firm actions and music that may be more critical to international success and carry managerial implications for firms looking to appeal to a global audience.