Good lord, what has science done?!
(via hiten: iomegadrive agentmlovestacos)
As a startup, Vark.com is doing a lot of interesting things. I really dig their IM interface, the breadth of ways they store, share, & answer questions, the interesting statistics regarding “median time to answer” and other fun facts about how well the service is humming.
Ultimately, though, it has a single fatal flaw: it fails to provide personal value to the nucleus of expert users that provide value to the wider user-base.
Josh Porter (aka Bokardo) explains this problem succinctly in a post title “The Delicious Lesson”:
The one major idea behind the Del.icio.us Lesson is that personal value precedes network value. What this means is that if we are to build networks of value, then each person on the network needs to find value for themselves before they can contribute value to the network. In the case of Del.icio.us, people find value saving their personal bookmarks first and foremost. All other usage is secondary.
Vark.com fails to provide experts any lasting and meaningful value… “Why should I spend my time and energy answering these questions?”
This isn’t unrecoverable. Vark could provide experts value in the form of notoriety by ‘scoring’ their contributions and providing a category leader-board, appealing to their ego or compulsion to beat the ‘game’.
Perhaps there is some other clever offering, but the lesson is: there has to be something.
Just your typical Roger-Rabit-esque morning here in Brookline.
We launched a neat new site called “my.doink.com” last week. Let’s you make animations with imported sounds and imagery. It doesn’t require registration to play with the tool, so check it out! We’re gluttons for feedback.
Internet pundits are (rightfully) really excited about this PhotoSketch demo where you draw stick figures, and it generates real composite photos to match your drawing.
Netflix data shows shifting demand down the Long Tail
The vertical axis is percentage of total demand (with ratings used as a rough estimate of rentals), and the horizontal axis is the popularity rank of the DVD titles. Between 2000 and 2005, the Netflix selection grew from 4,500 DVDs to 18,000, and the effect on the demand of this increase in variety is shown above.
Seen at The Long Tail
Thanks for posting this*.
Chris Anderson misses a huge point: the shift in demand is due to the Netflix recommendation system. People did not become magically more eclectic and less interested in new releases in 5 years. The recommendation system is built with one goal in mind: encouraging customers to rent older movies they will like to shift demand away from new releases in order to reduce the amount of new releases Netflix needs to purchase upfront (and be stuck with 3 months later).
Compare Amazon’s recommendation system and Netflix’s.
- Amazon wants to recommend to you whatever it is you are most likely to buy. The more popular the item is, the better, because Amazon can negotiate better prices when they can buy in large volumes.
- Netflix wants to recommend to you the movie that makes you say, “Oh yeah, I’ve always wanted to see that movie!” Unlike Amazon, Netflix’s business model is not predicated on inventory turnover. If you buy 1 million copies of the latest new release, you’re going to get 1 million copies returned to you once people are done watching it. Now you’ve got to either warehouse 1 million copies of a no-longer-new-release movie or sell / destroy the used DVDs.
So Netflix’s goal is to distract you into choosing an older movie over a new release, because that allows them to distribute that demand over their entire inventory.
Interesting data! Yes, I’d agree that’s Netflix’s goal (to shift demand down the curve for distribution efficiency) but I’d bet their customer approval is rising as well, so perhaps they are ‘allowing’ people to deviate from the herd. Either way, interesting stuff.
Here’s the link to the zip: http://euclid.poundcs.org/~kevin/cleanGaeWithLift.zip There’s a readme there explaining what to do to get it to work (it should be simple). I’ve sprinkled the pom.xml file with comments describing why I’ve done certain things the way I did. Any comments on it would be greatly appreciated. The project includes a single, simple datastore object (User.java - yes, I made the datastore objects in Java since I had some troubles injecting Scala classes. I’ve read it’s doable, but I wanted to tackle one thing at a time) which can be used as an example
Kevin has been doing some awesome bleeding-edge infrastructure work lately. His goal is to get continuous deployment + GAE + Scala + Lift working, and he’s almost on top of it. Really looking forward to leveraging this for extreme rapid development. Great work, Kevin!
It’s time for us to start thinking of every piece of content – books, blogs, albums, TV shows, movies, everything – as a new little startup. We have to look at fundamental business questions right from the start: what is the right audience? What is the right revenue model? And, most importantly, what could we do right now to answer the riskiest of these questions. In other words, what is the minimum viable product?
Just like with startups, this is a hybrid question. If our goal is just to create a blog or a YouTube video as a hobby, there’s no need for this kind of rigorous process. And if you want to write the great American novel – and don’t care if anyone reads it – you don’t need this either. But for the rest of us, who create content because we care passionately about having an impact on the world, we need to rethink the process by which we do it. We can’t just delegate the business questions to some media executive.
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