Last week I had a familiar experience: I plugged one of my favorite songs (by the excellent drone metal band Earth) into Spotify’s radio feature and their first recommendation was worlds apart from my original. Yes, the genre and vibe were similar, but the song felt completely different to me. Put simply, I hated it. So I skipped ahead to the next song and then the next. Like so many others, I gave up, and instead used Spotify as a way to stream music, not as a means of discovering great new songs.
This is a problem. Online content discovery is a huge opportunity for tech companies, and streaming music platforms are a hothouse of experimentation and dynamism. Unfortunately, the bigger players like Spotify, Pandora, and now Google’s Play Music, have yet to nail the music discovery process. Why is that?
John Paul Titlow’s recent article in Fast Company outlines some of the key challenges facing music discovery platforms. The way that many of these services currently attempt to crack the music discovery nut is to break songs down into as many “constitutive” aspects as possible–genre, decade, tempo, etc. It then makes inferences about what a user is looking for next predicated on previous choices.
Google’s new service is a bit more ambitious in that instead of using real people to input these track aspects into their song catalogue, it uses “machine listening.” This means that computers automatically identify the aspects and then feed them directly back into the algorithm. The basic logic of this kind of grouping harkens back to old-fashioned commercial radio stations, where songs were catalogued into categories such as genre (the “Classical” station) or decade (the ’80s station).
The approach makes sense from a coding perspective–the algorithm must have some way of identifying songs and artists and connecting them to others in their catalogue. However, it has very little to do with how people actually see connections among songs. It may even get in the way of people who are truly interested in music discovery.
Famed innovation expert Everett Rogers grew up on a farm in Iowa. He always wondered why it took his father so long to start using the newly available hybrid seed corn, both drought-resistant and higher producing. He went on to study the question “How do we embrace new ideas?” in academia, and developed the now-famous diffusion of innovations theory. Rogers concluded that behavior change is both a social and a temporal phenomenon.
This is essential for embracing new music: It takes time to commit to a new artist, a new album, just as it takes time to commit to anything new and valuable. According to Rogers, individuals tend to go through five different stages in the process of adopting new ideas. The very first stage is knowledge, or coming into contact with the idea. All music discovery sites get this phase right. They let users “know” about a new song or band.
The second stage is entirely social: Knowledge must be followed by persuasion. Only through persuasion do most individuals make the decision to embrace something new. This decision is then followed by an implementation and a confirmation stage.
What do these stages look like in an actual music discovery process? If an algorithm tells us to listen to a song from Bob Dylan’s Blonde on Blonde, say, we might instantly turn it off because we don’t like his voice. Algorithms have no persuasive power. As much of our most iconic and celebrated music does not have “instant traction”–it is not instantly “likeable”–users on more mainstream music sites are missing out. In fact, many artists specifically design their songs and albums to reveal themselves over time. In a recent segment on National Public Radio, Trent Reznor, former member of Nine Inch Nails, told the interviewer, “I aspire to make a record that sounds better 10 listens in than it does after two–and still, at 50 listens, you’re picking out things that add a depth and a thoughtfulness.”
If music discovery sites are to succeed, they need to account for the persuasive phase so integral to Rogers’s theory. Great music discovery often requires an impassioned advocate and a commitment of time. (So what if you didn’t like it the first time? I’m telling you, it’ll change your life. Try listening again.)
Of course, we know all of this intuitively because this is how music discovery worked in the past. Twenty years ago, when people wanted to hear a broad range of new and exciting music, they turned to their favorite DJ. A radio DJ had the advantage of playing a song several times a day, introducing the listeners to the new sounds and allowing them to consider and then embrace an adoption of the music. After the fifth or sixth “play,” listeners were able to create their own unique relationship to new music, guided by the trusted curatorial skills of the DJ.
If music platforms have any hope of mainstream success, they have to stop atomizing music into bits and parts, which divorces it of its context. Instead, they need to replicate elements of an essentially social phenomenon, allowing for listeners to embrace new music over time through the persuasions of peer and expert recommendations. Yes, it’s true that a platform like Spotify has shared playlists with a social component but it feels like an afterthought in the program’s design. It’s not an intuitive experience–the interface is clunky and it doesn’t fully capture the aspects that make a personal recommendation so powerful.
Fortunately for music lovers, there are several sites out there starting to get the discovery process right:
Turntable.fm, started in 2011, allows users to join chat rooms that stream music from a rotating list of fellow-user DJs. Discussions in the chat room can heat up regarding the musical choices. If enough people decide that a song is “lame,” the app skips to the next song on the playlist.
If Turntable simulates the “real time” social experience of a DJ, Piki, developed by the same designers, provides an alternative to casual streaming. Like Pandora, users simply turn it on and press play. The app will select songs from a playlist curated by friends and trusted influencers. Piki bills itself as the “middle man” between the fully engaged Turntable experience and the completely passive Pandora experience. It is both casual and personal–entirely intuitive–like a radio station designed by your favorite people.
The Echo Nest relies on algorithms for its recommendations but instead of taking the constitutive bits from the music itself–tempo, chord, or genre, for example–it pulls its information from responses to the music. The app mines data from concertgoers, amateur musicians and professional reviewers. This gets the site closer to providing recommendations based on the subjective experience of listening to the music, not the substance of the music itself.
Of course none of these apps have truly mastered the serendipity of a great record store experience. Music lovers know the tactile pleasures of flipping through albums in the bins, making discoveries based purely on the album art or the band name. Mainstream music discovery sites claim they offer this experience as well but their versions are sorely lacking. The album art usually appears on a tiny JPEG somewhere in the midst of a crowded interface and there is no way to do a search based on funny or unusual band names. Sites could better simulate the record store experience by allowing users to enter a virtual space, giving them access to album art in large format and high resolution.
Even more important: I haven’t seen any music sites really bring together the full entertainment experience by offering merchandise, concert tickets, and other relevant contextual information on the bands (tour schedules, Twitter feeds, Wikipedia pages, etc.).
Perhaps all this is a lesson that the art of music discovery can never truly be engineered. Still, we should take inspiration from these, and other, experiments on display. Through them, we can take away larger lessons about user discovery in all product categories. Some day soon, I hope all my consumer sites are bringing me into contact with meaningful products and experiences. Until then, I’ll be the one scrolling through my own library on iTunes for my favorite drone metal bands.