Information Cascades within Music Streaming Charts
Billboard listed their first Hot 100 on August 4, 1958. This list was originally composed of radio airplay, jukebox play, and singles sales. Fast forward to the present day, in many ways this list serves as the preeminent benchmark for a hit song in the United States. However, the composition of the list has changed significantly, as jukeboxes became simply trivia questions, streaming has taken a step to the forefront. Streaming industry leaders: Spotify and Apple Music, also incorporate charts into their own services. However, recent efforts have been made to remove them from their home pages. Information cascades could be a driving factor for this decision.
Distinct from Billboard and other curated charts, streaming charts are solely representations of the most played songs on their respective services. Meaning the existence of these charts, particularly a chart accompanied by play count in Spotify’s case, serve as key representations of information cascades occurring. A clearer way to track this progression is by looking at what is called a ‘sleeper hit’. This is a song that initially does not have much success, but over an extended period of time grows to be massively successful. A hit like this relies on a massive information cascade. Initially, this would not have the ability to rely on past responses as there is not much of a following, however the initial listeners would serve as the traditional ‘first player’ in the cascade as they make an autonomous decision to listen to this music based on their own signals. The consequential ‘second players’ are a bit harder to define, and certainly constitute a significant jump as they play the part of pushing these songs onto an initial, larger chart. The second players, like an arbitrary player B, take into consideration their own signals, but have also been influenced by their public knowledge of player A’s decision. If these ‘second players’ do choose to listen to the music, the decisions of these large groups would have built a cascade, one publicly accessible through the usage of these charts. At this point, these charts will be self-serving as consumers will seek out the most popular music, imitating the prior decisions of other consumers, further reinforcing the popularity of these once unknown songs.
This has hindered these streaming services’ goals of promoting discoverability within their service, as a bubble can quickly be created of solely the most popular music. Rather, the services have made a concerted effort to pack their home screens with curated options and playlists, akin to charts of yesteryear. Hiding the top charts behind multiple screens, while mixing their front page playlists with the popular, the unknown, and a lot paid placement.