Making Music

I’ve been impressed and entranced by the music service Pandora since I first ran across it several online lifetimes ago in 2007.

Two things particularly impressed me about Pandora. First, unlike services such as Spotify that allow you to access music you already know about, Pandora was the first large scale attempt to offer music discovery. Enter the artist or tracks you like most, and Pandora would find more music that was similar. Normally you would expect to learn that Pandora is powered by cutting-edge algorithms.

In fact, Pandora is powered by humans. Music school graduates. Many dozens of them, all methodically classifying individual songs against a master taxonomy of over 400 characteristics. It’s an expensive approach, but it’s organized and returns consistently high quality results. And while Pandora continues to struggle from a profitability standpoint, nobody argues with the quality of its service.

But what if you could create a Pandora-like service without the high labor costs? That’s what a company called 8Tracks set out to do.

Rather than having a paid staff categorize music, 8 Tracks went the social media route. Everyone was invited in essence to become a DJ, and upload their own song lists to the 8Tracks site. These playlists were organized via tags, so users could discover music based on mood or musical style, for example. If users like particular playlists, they can follow the people who uploaded them in order to see all their new playlists right away.

8Tracks is unquestionably providing a music discovery service, just like Pandora. But it’s a fundamentally different experience. Pandora is dependable, seamless and efficient. 8Tracks is hit-and-miss, time-consuming and requires lots of user interaction.

There’s room for both services in the vast music market and indeed, both services have many enthusiastic adherents. Yet by looking at both services side-by-side, you can see the strengths and weaknesses of user-generated content very clearly.

Music is entertainment. There’s no risk or consequence if you don’t discover a certain song by a certain artist. But when you move into the realm of business information, that dynamic changes. Suddenly, getting the right answer starts to matter a lot. That’s where user-generated content can come up short. Users generate whatever content they want, whenever the want, for as long as they want. You have little control. User-generated content works best where there is a massive volume of content (think Yelp or TripAdvisor) and the correct answers will win out, or in situations where there is no alternative information source, making your content the best that is available. But when the quality of your content matters, social approaches to content creation can yield decidedly off-key results.

Finding Business in Discovery

I have spent a lot of time recently with the website Artsy.com. That’s in part because I find it fascinating, and in part because it is trying to accomplish so many things at once. At one level, it’s a fine art discovery service. Artsy has partnered with art galleries nationwide to allow them to post images and descriptions of artwork for sale. That’s a great convenience for someone looking to buy art, but it’s not a new idea. A number of services are already offering similar services.

Where Artsy separates itself from the pack is with its “Art Genome Project.” Essentially, Artsy is categorizing artwork against a taxonomy much as Pandora has done for music. And that’s where the magic happens. If you find a piece of art you like, you can easily explore other artwork with similar characteristics. That’s no small feat when you consider that Artsy already catalogs over 125,000 pieces of art from over 25,000 artists – and it’s still only scratching the surface of what’s available.

Artsy is what might be characterized as a next generation discovery tool. Certainly, there’s value in aggregating artwork from galleries all around the country. But the big breakthrough here is being able to point to a single piece of art and say “I’d like to be able to see art in a similar style by different artists.” That’s a powerful step forward on discovery that benefits both the art buyer and the artist. If you’ve got a market where there are huge numbers of manufacturers, little standardization and prodigious output (music, art and wine are great examples), there’s a next generation discovery opportunity waiting for you.

But what about Amazon and Netflix, you may ask? These companies too have done a lot to improve discovery, but in a very different way. These companies don’t look so much at the product itself as who bought the product and in what combinations. This is a powerful and effective approach, but what Pandora and Artsy did was build taxonomies based on inherent product characteristics and then committed to manually classifying products against them, a significant exercise in metadata creation, but one that yields powerful, proprietary results.

The other interesting aspect of Artsy is one that I initially viewed as an overreach. Artsy includes not only artwork for sale, but artwork in museums and private collections. Certainly this makes artsy more attractive to art lovers, art students and others, but it seemed to confuse somewhat the art buying experience. This is largely explained by the fact that Artsy has a mission-driven aspect to it, but there may be a huge business opportunity here as well.

If Artsy is doing all the work of classifying and posting images of artwork held in museums and private collections, why not go one step further and become an international registry of artwork? Much of the value of art is tied to its provenance – its history of ownership. Artsy could become a central registry, collecting a small fee to re-register a piece of art every time it changed hands. It could then sell subscription-based access to this database to auction houses and galleries. Lots of details to be worked out to be sure, but this notion is only a small jump from Artsy’s current ambitions.

That’s what makes the data business so fascinating and lucrative: there are infinite opportunities to make money. All it takes is some creativity, and in this case, re-framing an existing business model.