Music Streaming Analytics — Can Artists Be Data-Driven?

Palace performing at the Hoxton Hall, London

Artists could see how they were performing and who was listening to their music.

Spotify (arguably) suggests that this data could be used to guide Artists.

What’s available to Artists?

See https://artists.spotify.com/

1. Streams

First things first, we can see how many times a track is played.

2. Saves

This is a count of how many listeners add your track to their library or their playlist. In Spotify, this is done by clicking on a star icon on each track/album.

3. Listeners

The number of individuals that have streamed a track.

  • Save Rate (Saves / Listeners) — is there a track making its way into a users library more frequently than others?

By looking at save and repeat rates we could start to quantify listeners apart from fans.

4. Source

Or, ‘where your streams come from’ breaks down your streams by where the user played it from. We have these ‘sources’ to review on Spotify:

  • The listeners own playlist or library
  • Other listeners playlists
  • Spotify algorithmic playlists
  • Spotify curated playlists
  • ‘Other’

5. Demographics

We can split listeners by age, gender, country, and city. In basic terms, having an understanding of your audience can be useful for planning promotions or social media activity.

How could we expand on these insights?

We have a lot of potential with the metrics available on our Spotify dashboard. In theory, these are additional metrics that could be added to the mix.

1. Average drop-out time (track)

Wouldn’t it be good to understand at what point in a track a listener hits skip?

2. Number of completed playbacks

As we now know, streams are counted beyond 30 seconds of playback. This means that if a track is skipped at 31 seconds it is still counted as a stream.

3. Repeat rates per demographic

Is there a group of listeners repeating a track more than others? Currently, we can only breakdown demographics by listeners and not streams or saves.

What does this all mean?

Experiences & tech | UX Researcher / Designer

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