Radio programmers and record industry exes are raising concerns over a development with song charts - especially in on-demand streaming: distinction in popularity of songs as rank compression accelerates.
As compression increases, it becomes more difficult to differentiate song popularity simply by virtue of stream counts
The compression of song stream numbers, where there’s less separation between popular song titles in rankings, can be attributed to several factors:
Increased Access and Availability: With the rise of music streaming platforms like Spotify, Apple Music, and others, listeners have unprecedented access to a vast library of music. This democratizes music consumption, allowing more songs to gain traction simultaneously.
Algorithmic Playlists and Recommendations: Streaming services use sophisticated algorithms to create personalized playlists and recommendations. These algorithms often promote a wide variety of songs to users, leading to a more even distribution of streams across many tracks.
Social Media and Virality: Platforms like TikTok, Instagram, and YouTube can rapidly boost a song’s popularity. Viral trends often lead to multiple songs gaining significant streams in a short period, contributing to the compression in stream numbers.
Playlist Culture: Many users now listen to curated playlists rather than individual albums or artists. These playlists often feature a mix of popular and emerging tracks, which helps distribute streams more evenly across different songs.
Global Audience: The global reach of streaming platforms means that songs can become popular in multiple regions simultaneously. This broadens the base of listeners and can lead to a more even distribution of streams.
Shorter Attention Spans: With the sheer volume of available music, listeners tend to switch between songs more frequently. This behavior can result in a more balanced distribution of streams across many tracks.
These factors collectively contribute to the phenomenon where popular songs have more compressed stream numbers, making it harder for any single track to dominate the charts for extended periods.
Bridge Ratings has addressed this development this year by introducing two new metrics to our client service STREAMSTATS.
1) Passion Scores
Pure stream counts used to tell a different story for researchers and music programmers. The stream count differences between #1 and say #20 on a chart would be significant enough that true relative popularity variance of any two songs was clearly determined.
Compression has blurred these results.
But passion for a song - how many times it is played and by how many different users - adds a dimension of a song’s value not previously unknown. This Song Value Metric allows for clear distinctions between songs and dissolves the chart compression syndrome.
2) Predictive Artificial Intelligence in which song popularity is further analyzed with a calculus based on previous song-type consumption success. This produces a song appeal metric which is used to further appreciate song value currently disguised by streaming chart compression.
As discerning true popularity becomes a more complicated process due to chart compression, we’ve developed these interpretation tools in order to clarify blurred chart rank results.
Dave Van Dyke, President