In this piece, Kristen Westcott Grant explores the evolving role of data algorithms in the streaming industry, and how Spotify’s continued rollouts of new personalized playlists can help not just fans and the streaming service, but also artists and labels.
______________________________
By Kristen Westcott Grant of Wescot Multimedia from Forbes
A few years ago I sat down with Spotify’s then-VP of Content and Distribution, who spoke about the idea of Spotify becoming the soundtrack to a listener’s day, offering a “Songs To Sing In The Shower” playlist and news podcasts in the morning, “Deep Focus” and “Afternoon Acoustic“ playlists midday, and “Read & Unwind” playlists in the evening. The extent to which Spotify is working to tailor its user experience to each listener is constantly evolving.
On September 24, Spotify continued in this vein by announcing the release of two new personalized playlists called “On Repeat” and “Repeat Rewind.” The “On Repeat” playlist curates the songs that a listener has listened to multiple times within the last 30 days. The “Repeat Rewind” playlist includes tracks that a listener has played over a month ago. These algorithms are beneficial for listeners, but they can also be used to benefit artists. It comes down to motivation, perspective, and choice of use.
Have you ever gone panning for gold? Do you understand the process? You pick up a sieve, you dip it into the water and you pick up a whole lot of things that you do not need, including dirt and rocks. But somewhere in there is your gold. The motivations of the person holding the filter dictate how the filter is shaped and how the filter is shaped dictates what gold you get.
Digital service providers are motivated to utilize data to find music on behalf of listeners. Record labels are motivated to utilize data to find fans on behalf of musicians. These two perspectives use the same filter or algorithm and data to achieve different outcomes. Record labels use the platform Spotify for Artists to provide insights from streaming data to musicians and the businesses that support them. Platforms like this are similar to Apple Music Connect and Pandora’s Amp. Identifying repeat listeners can be useful for the purpose of creating tailored marketing and touring strategies, as well as creating tailored listening experiences. Providing demographics and city-level geographic insights of repeat listeners could amplify the effectiveness of insights available on Spotify For Artists.
The same holds true for Spotify’s marketing initiative, “Found Them First” Tool released a few years back. This marketing campaign was available on a microsite and let users see which breakout artists they had listened to on Spotify before they had their breakout success. From a label perspective, Spotify quantified what it means to be an early adopting fan. An example of what this could look like is identifying fans who have listened to an artist multiple times within an allotted time period, within two months of the artist’s first release. Algorithms that have the potential of defining standardized ways of categorizing listener types should also be transparent.
Today’s music industry involves many stakeholders which share the same need for data but for different reasons. When you create an algorithm that involves streaming data, it is worthwhile to look at its potential use from multiple perspectives and ask the question “How can I make this algorithm useful to multiple stakeholders and how can I facilitate dialogue around its impact?”