Songs on the Move: How Does This Work?

Not All Growth is the Same

As a label marketer, you want your artists’ songs to grow as much as possible. You make those songs available on multiple retailers. When songs are added to or removed from playlists, stream counts will tend to rise or fall suddenly and quickly. However, when listeners start to actively seek out and stream your songs, this type of growth and engagement tends to be more gradual, as more people find and like your songs over time, adding them to their collections or streaming them directly from the artist’s page. Beyond playlist growth, this form of active growth is a strong indicator that your song is making a real impact with listeners.

In the Main View, songs tagged with a yellow flag to the right of the song indicate a high potential marketing opportunity. The yellow flag is present on songs showing positive, upward growth due to active listening. 

SOTMMainViewActiveFlag.gif

Active listening stems from those who are purposefully seeking out your song by streaming from their collections or from the artist’s page, indicative of strong listener engagement.

Armed with this insight, you now see the opportunity to make more informed and timely marketing decisions based on what is happening before it is too late.

Songs on the Move provides you just such insight.

How Does This Work?

Songs on the Move uses advanced machine learning algorithms to analyze each song’s streaming patterns across multiple retailers, detect and identify if there were any significant changes compared to the song’s prior streaming patterns, and provide what is responsible for, or driving, the change as it is happening.

Every song in our catalog is analyzed daily based off of that specific song’s entire historic streaming data, generating a baseline model of expected performance. Based on that song’s history, the model predicts what the song’s expected performance will be tomorrow. The following day, if the song’s actual performance is much greater than expected, this represents a significant change and that song will be listed under the Songs on the Move section. Any song that is performing above expectations will be listed under this section starting with songs experiencing the largest change. The song’s streaming behavior will continue to be analyzed on a daily basis and the model will adjust the next day’s prediction based on the actual streaming data captured for that current day.

For new releases, these songs will only appear under this section if significant changes are detected after 14 days of analyzing the song’s streaming behavior, established as this song’s baseline.

In addition to listing which songs are trending toward growth, the driver responsible for that change will be listed in the Song View. Different types of drivers exist and include:

  • Specific retailer or multiple retailers
  • Passive (Playlists)
  • Algorithmic (100% Personalized Playlists)
  • Active (Listener-driven)

The additional messaging displayed in the Song View will indicate what is responsible for driving the change, how many days the change has been happening along with the “as of date” to provide how current the information is. Over time, if a new driver is detected, the messaging and the duration of the change will update accordingly. The default view will display the latest driver information. If there were previous drivers, you would see each represented as a segment break along the bottom line, displayed left to right from oldest to newest. Hover over any segment to see the driver responsible for that change in song streams and the date when that driver occurred.

 

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