What Makes Popular Culture Popular?: Product Features and Optimal Differentiation in Music

In this paper, we propose a new explanation for why certain cultural products outperform their peers to achieve widespread success. We argue that products’ positioning within feature space significantly predicts their popular success. Using tools from computer science, we construct a novel data set that allows us to test how the musical features of nearly 27,000 songs from Billboard’s Hot 100 charts structure the consumption of popular music. We find that, in addition to artist familiarity, genre affiliation, and institutional support, a song’s perceived proximity to its peers influences its position on the charts. Contrary to the claim that all popular music sounds the same, we find that songs sounding too much alike—those that are highly typical—are less likely to succeed, while those exhibiting some degree of optimal differentiation are more likely to rise to the top of the charts. These findings offer a new contingent perspective on popular culture by specifying how content organizes competition and consumption behavior in cultural markets.