A new way of selling music for independent artists ..
TuneCore is a
music delivery and distribution service that lets you put your music up
for sale on iTunes and Rhapsody. For less than $10.00 you can get
an album put on sale at iTunes for a year. You keep all of the rights
and all of the profits from sale. Getting music in the stores has
always been one of the biggest barriers for independents, but now with
TuneCore, independents have a low cost way of getting their music in one
of the largest music stores
in the world. Of course, distribution and sale is only one aspect
of selling music. Independents still have to find a way to promote
their music, to get it heard by people so new listeners will want
to buy it. That's still something that they'll have to do on their
own.
For iTunes and Rhapsody, being the main distribution point for independent music will be a key distinguishing feature over the every shrinking music department in the local WalMart. Offering 10,000,000 or 100,000,000 different songs, including songs by the band that plays in the local eatery every friday night will drive new customers to the online store. The key problem, however, for the iTune and Rhapsody is to figure out how to drive people into the long tail, to find new music that they'll like.
Already, there has been quite a bit of effort made around social recommendation and collaborative filtering to help people find new music. Last.fm is one of the most successful examples of this. However, social recommendation suffers from a 'popularity bias'. Social systems tend to favor what is popular, so what is popular gets recommended more and becomes even more popular. It is hard for an unknown independent artist to make headway with this type of recommendation. That's where content-based recommendation can be helpful. Content-based systems recommend music based on 'what it sounds like', instead of 'who is listening to it'. With a content-based recommender I can say 'find me music that sounds like the song "Take five"' and I should get a list of recommendations or songs that sound similar.
Currently there are very few content-based recommendation engines. There's Predixis, that uses automated, computer analysis to determine similarity and Pandora that uses musicians to characterize music conent. It's still early days for content-based recommenders, and there's plenty of room for improvement in the quality of recommendations. I suspect, though that soon these content-based recommenders will be key to driving listeners into the long tail, to help people find that independent artist.
20 years ago a new artist needed a record company to be successful for three reasons: the recording studio, the record promotion and the distribution channel. Today, a recording studio is cheap and distribution is easy. The last piece of the puzzle is helping listeners find the new artist's music.
For iTunes and Rhapsody, being the main distribution point for independent music will be a key distinguishing feature over the every shrinking music department in the local WalMart. Offering 10,000,000 or 100,000,000 different songs, including songs by the band that plays in the local eatery every friday night will drive new customers to the online store. The key problem, however, for the iTune and Rhapsody is to figure out how to drive people into the long tail, to find new music that they'll like.
Already, there has been quite a bit of effort made around social recommendation and collaborative filtering to help people find new music. Last.fm is one of the most successful examples of this. However, social recommendation suffers from a 'popularity bias'. Social systems tend to favor what is popular, so what is popular gets recommended more and becomes even more popular. It is hard for an unknown independent artist to make headway with this type of recommendation. That's where content-based recommendation can be helpful. Content-based systems recommend music based on 'what it sounds like', instead of 'who is listening to it'. With a content-based recommender I can say 'find me music that sounds like the song "Take five"' and I should get a list of recommendations or songs that sound similar.
Currently there are very few content-based recommendation engines. There's Predixis, that uses automated, computer analysis to determine similarity and Pandora that uses musicians to characterize music conent. It's still early days for content-based recommenders, and there's plenty of room for improvement in the quality of recommendations. I suspect, though that soon these content-based recommenders will be key to driving listeners into the long tail, to help people find that independent artist.
20 years ago a new artist needed a record company to be successful for three reasons: the recording studio, the record promotion and the distribution channel. Today, a recording studio is cheap and distribution is easy. The last piece of the puzzle is helping listeners find the new artist's music.
Posted by gameguy on January 29, 2006 at 05:56 PM EST #
Btw Im glad you mentioned the "last piece of the puzzle" but I think you didn't stress it strongly enough.
"The last piece of the puzzle is helping listeners find the new artist's music." In other words, marketing. Without spending serious marketing dollars its doubtful you can find much of an audience. And thats the bigget value the big publishers bring today.
Posted by gameguy on January 29, 2006 at 05:58 PM EST #
Posted by tristanf on January 31, 2006 at 05:26 AM EST #
Posted by Paul on January 31, 2006 at 06:00 AM EST #