TY - JOUR
T1 - Is it getting harder to make a hit? Evidence from 65 years of US music chart history
AU - Lech, Marta Ewa
AU - Lehmann, Sune
AU - Juul, Jonas L.
PY - 2025/9/1
Y1 - 2025/9/1
N2 - Since the creation of the Billboard Hot 100 music chart in 1958, the chart has been a window into the music consumption of Americans. Since its introduction, the chart has documented music consumption through eras of globalization, economic growth, and the emergence of new technologies for music listening. In recent years, artists have voiced their worry that the music world is changing: Many claim that it is getting harder to make a hit. Until now, however, the claims have not been backed using chart data. Here we show that the dynamics of the Billboard Hot 100 chart have changed significantly since the chart’s founding in 1958, and, in particular, in the past 15 years. Whereas most songs spend less time on the chart now than songs did in the past, we show that top-1 songs have tripled their chart lifetime since the 1960s, and the highest-ranked songs maintain their positions for far longer than previously. At the same time, churn has increased drastically, and the lowest-ranked songs are replaced more frequently than ever. Together, these observations support two competing and seemingly contradictory theories of digital markets: The Winner-takes-all theory and the Long Tail theory. Who occupies the chart has also changed over the years: In recent years, fewer new artists make it into the chart and more positions are occupied by established hit makers. Finally, investigating how song chart trajectories have changed over time, we show that historical song trajectories cluster into clear trajectory archetypes characteristic of the time period they were part of. Our results are interesting in the context of collective attention: Whereas recent studies have documented that other cultural products such as books, news, and movies fade in popularity quicker in recent years, music hits seem to last longer now that in the past.
AB - Since the creation of the Billboard Hot 100 music chart in 1958, the chart has been a window into the music consumption of Americans. Since its introduction, the chart has documented music consumption through eras of globalization, economic growth, and the emergence of new technologies for music listening. In recent years, artists have voiced their worry that the music world is changing: Many claim that it is getting harder to make a hit. Until now, however, the claims have not been backed using chart data. Here we show that the dynamics of the Billboard Hot 100 chart have changed significantly since the chart’s founding in 1958, and, in particular, in the past 15 years. Whereas most songs spend less time on the chart now than songs did in the past, we show that top-1 songs have tripled their chart lifetime since the 1960s, and the highest-ranked songs maintain their positions for far longer than previously. At the same time, churn has increased drastically, and the lowest-ranked songs are replaced more frequently than ever. Together, these observations support two competing and seemingly contradictory theories of digital markets: The Winner-takes-all theory and the Long Tail theory. Who occupies the chart has also changed over the years: In recent years, fewer new artists make it into the chart and more positions are occupied by established hit makers. Finally, investigating how song chart trajectories have changed over time, we show that historical song trajectories cluster into clear trajectory archetypes characteristic of the time period they were part of. Our results are interesting in the context of collective attention: Whereas recent studies have documented that other cultural products such as books, news, and movies fade in popularity quicker in recent years, music hits seem to last longer now that in the past.
KW - Music
KW - Ranking
KW - Popularity
KW - Collective Attention
U2 - 10.1140/epjds/s13688-025-00571-9
DO - 10.1140/epjds/s13688-025-00571-9
M3 - Journal article
SN - 2193-1127
VL - 14
SP - 67
JO - EPJ Data Science
JF - EPJ Data Science
IS - 1
ER -