Content Matters

“Big brother is listening.”

In light of recent news of NSA wiretaps and FBI “raids” on libraries that may sound ominous. But, what if you asked “big brother” to listen in?

That’s the idea behind last.fm. Last.fm, formerly AudioScrobbler, is an iTunes plugin that drives a music recommendation engine. The concept is pretty simple. The plugin monitors all the music you listen to on iTunes, generating lists of your favorite performers and songs, then compares those to others in their database.

Using various matching algorithms, Last.fm is able to create music neighbors, people whose listening habits are similar. The more interesting information comes when you click on a performer you like. The Last.fm engine quickly shows you other “similar” bands. I have found this a great way to discover new bands. For example, if you look at the Arctic Monkeys, you’ll see Maximo Park, the Libertines, the Subways, the Rakes and other UK indie bands. Another useful feature is that you’ll see the songs for each band or performer ranked by the number of times they are listened to. Want to sample a few songs from the Subways? With just a click you’ll see their most popular songs on Last.fm are Oh Yeah and Rock and Roll Queen.

Last.fm also supports tagging of songs and artists, as well as the ability to recommend an artist to another last.fm user. Outside of the “neighbor recommendations”, the community aspects of Last.fm are modest today, but there’s certainly opportunity for growth.

Last.fm has also launched the last.fm player, a personalized online radio station. With the Last.fm player you can listen to your own favorites or choose to listen to what they call neighbor radio. And now, my biggest complaint about last.fm has been addressed. Most of us listen to a lot more music on our iPod than on our PC. Yet Last.fm only picked up what was played on your PC. Now third parties have developed free plugins that will take your iPod usage and include that in your Last.fm results.

Last.fm also makes your listening habits accessible via an RSS feed. The feed itself doesn’t serve any great purposes today (not sure anyone out there needs real-time reporting of what I’m listening to), but could lend itself to an interesting mashup. What if VNU’s Billboard were to layer their sales rankings on top of Last.fm play lists, then cross-reference those by country? It might make for some interesting predictive capabilities for the music industry. Fred Wilson, in the Music section of his blog, includes his weekly and all-time top 10 playlists from Last.fm.

So, what’s the message for content providers?

First, monitoring customer behavior is not a bad thing. In fact, if you are upfront about what you’re doing and offer clear and compelling benefits to your users, they will welcome the monitoring.

Second, recommendation engines are not simply for e-tailers. If you can leverage community usage behavior to cluster similar content together, it can create a powerful and compelling recommendation engine. Combine that with simple distribution tools such as RSS and you have a low-cost, high value tool to promote new content to existing users. 

P.S. For those interested in knowing what I am listening to, you can visit my last.fm profile.