Droppin’ Off The Charts
Inc. just published a fascinating article about the difficulties that turntable.fm is facing.
In short, the startup that burst onto the scene last year is now facing a problem that many Web 3.0 startups face: users are bored. As a result, return visits to the site have dropped. It’s a problem compounded by the fact that turntable.fm asks the user to be active the entire time (unless you’re just sitting in a room listening to other people DJ – but why not just use Pandora, Rdio, or Spotify for that?). You need to choose your songs for your playlist and you need to actively manage those songs if you want to get points from other people. And most games get boring after a while.
The game is fun, but it involves constant…involvement. And apparently that’s a lot to ask of a user.
Anyway, it’s a fantastic article and I suggest you all read it. But the thing that really caught my eye was the use of data in the article. Take a look at this:
Chasen continues. Since launch, Turntable has 130 million songs played, 65 million Awesomes, and 5.28 hours of music played per minute.
That sounds amazing! 65 million Awesomes in one year! Over 10 million songs played per month! 300 hours of music played each hour!
If those numbers are true, how can there be a problem? Well, take a look at this:
Which, if you do the math, is just 317 people on the site listening at any given time. (Most traffic comes during the workday; there is a lot of dead air at night.)
That’s not so great. 317 people is not a large number.
Regardless of how turntable.fm is doing, the interesting thing here is that numbers tell the story that you want them to tell. How you slice them up and package them has a profound effect on how they’re interpreted. Trends matter (Have the numbers gone up or done since turntable.fm signed licensing deals with the labels? Do people listen when they’re not at work? How steep was the user drop-off after the initial buzz died down?) Time frames, averages, metrics – they all should be chosen with care because they implicitly define the story that you are telling.
People have been critical of G+ usage metrics arguing that the large number of users doesn’t mean anything because 1) Google is so prevalent that they should have a large number of users and 2) a very small number of those users actually go back to the site.
One more example: Facebook announced recently that it has 900 million active users and that 4 billion pieces of information are shared each day on the network. So, that’s roughly 4 pieces of information shared by each user each day. Is that a good number? Depends on the context, right? I’d like to know how Open Graph has accelerated those numbers. What’s the breakdown on shared content is between status, pictures, Open Graph shares (Varun read an article! Varun listened to “Call Me Maybe” on Spotify!)? Do ”likes” count as pieces of shared content? Comments? I’d like to know if the sharing is spread disproportionately throughout the Facebook population and which segments are sharing the most. So on and so forth. There’s a lot of interesting numbers to play with and a lot of interesting ways to use those numbers.
What do you think?
- This is obviously a major issue and worthy of a separate post. Attention spans are dwindling while demands on attention spans are growing. “Things” are becoming increasingly disposable. Apps will come and go, will be bought for a lot if they’re hot and lucky, but they’re transient at the end of the day. Apps are temporary, platforms are forever (kind of).↵