They say you are what you eat, but these days, we wonder if that still rings true. Are we, instead, what we search? Are we the daily digital actions we make every day: the questions we google, the music we play, the articles we read? 

Like the rings of a tree, our data footprint grows as we grow. In CommonAlly’s Data Print Series, we examine everyday activities that inform our digital DNA. 

When I press play, the familiar beats and lyrics of musicians I love fill the air, from Nirvana to the Bill Evans Trio. As I observed my eclectic playlist, it struck me: is this how I’m summed up? Does this compilation of disparate songs tell the story of “me”?

Flashback to my childhood in the 1980s. Creating my mixtape was an arduous task (and, likely, copyright infringement) of blank cassettes and mastering the precision of hitting the “record” button at the right time. In the old days, I defined what I listened to and, therefore, what it said about me. 

The largest streaming services of today are designed to create a better music experience. And hands-down - it has. It’s when they present me with music they think I’ll like - I wonder: do they know me better than I know myself? And, do they get it right? How do they know that a particular song coming over the airwaves will generate a “that’s my jam” response? Our everyday interactions with every online platform generate a data profile that I neither see, own, or can change.  

Today we'll dive deeper into the data print of music playlists.

Dominating streaming market share

Currently, there are two music streaming services with the lion's share of the global market:

• Spotify 
• Apple Music 

Depending on who you talk to (and the amount of subreddits on the topic), one is better than the other for various reasons. But both platforms offer data-driven playlists and stations tailored to the user, or listener in this case. 

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What streaming platforms track and monitor

Leveraging various data sources and algorithms facilitates music streaming platforms' ability to cater to their users' diverse musical preferences. 

Yet, most platforms are also tracking your activity and data. 

• User Listening Habits & Interaction: collected data on what songs users listen to, how often they listen to them, and what playlists they create or follow. Analyzing this data, the platform tracks users' music preferences and creates personalized playlists.
  

• Machine Learning Algorithms: sophisticated algorithms analyze the characteristics of each song, such as tempo, instrumentation, genre, and mood, to create playlists with similar attributes.
  

• Collaborative Filtering: a technique to recommend songs based on the preferences of users with similar tastes. If you like certain songs or artists, the platform will recommend other songs or artists that users with similar tastes have enjoyed.
  

• Contextual Data: data such as the time of day, location, or current user activity when creating custom playlists. For example, it may suggest upbeat songs for a morning workout playlist or relaxing tunes for a late-night wind-down playlist.
  

• Feedback Loop: continuous learning from users' interactions, including whether they skip songs, add them to their library, or listen to them repeatedly. This feedback loop improves the accuracy of playlist recommendations. 

In exchange for listener data, the platform provides personalized recommendations and custom playlists designed to introduce new music, including tracks based on listening history. Sometimes, this process works well. Other times, we find our favorite Radiohead and Deftones are replaced with BTS and Taylor Swift, due to a crafty 9-year-old daughter (who got her hands on the “play” button). Let’s just say that can make for a rather…wide ranging listening sesh. 

For freemium and premium listeners, though, that history of beautiful and/or eclectically curated data belongs to the streaming service - even if they don’t hang on to it after account deletion.

Questioning if you signed up for all that?

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Understanding privacy policies of music streaming services ‍

Consent and disclosure of data use are important, complicated, and easier to overlook. If you’re skeptical or curious about the collection of your data and its subsequent use, read privacy policies and terms before checking the “sign up” or “subscribe” box. 

Spotify does note that they retain some data for the life of your account “for example, to provide retrospective playlists that users enjoy and personalized recommendations that take listening habits into account [...]”. 

You can read more about Spotify’s use of your data here.

Apple is known for its user-privacy-centric philosophy. In its privacy policy, it cites the collection of information “about how you use Apple Music [...] in order to tailor features to your musical tastes. These features include personalized recommendations such as albums and playlists picked for you and Radio, which plays selections from your favorite artists and genres.”‍

You can read more about Apple Music’s use of your data here. 

Like your tastes in music genres and artists, your data print is constantly changing and evolving. It’s as unique as your fingerprint and as complex as T-Swift lyrics. 

Customized music playlists offered by streaming services uncover stories behind the data prints you leave behind. This everyday example of your data print creates a highly personalized listening experience that you can enjoy for hours on end. 

I’ll take that over manually recording cassettes any day!