I’ve been following Hunch for a while, and my impression has evolved from the initial skepticism with which I greeted it a year ago (to the day!). Given the track records of co-founders Caterina Fake and Chris Dixon, perhaps I should have expected their success at obtaining traffic and funding. But what interests me more is that they are doing interesting things with data mining and putting a new twist on social media analytics.
For those unfamiliar with Hunch, it is a decision engine (cf. [real decision engine] vs. [decision engine]). For example, it can help you decide whether to buy an iPad or how to name your baby. While it’s not clear to me how much people are using Hunch for utility vs. entertainment, Hunch is certainly accumulating users–as well as the data that those users volunteer.
Hunch recently released two applications that mash up that data with the Twitter follower graph. The first is a “Twitter Predictor Game” that attempts to calculate your taste profile from your Twitter id and then predict how you’ll answer Hunch’s taste questions. Just to keep the game honest, you can look at the Hunch’s guess either before or after you provide your answer. The second is called “Twitter Follower Stats“: given a Twitter user, it reports the salient information it has inferred about that user’s followers (e.g., @maddow vs. @karlrove).
I think this stuff is neat, and a great testament to the “unreasonable effectiveness of data“. The question-answer data still feels a bit sparse for my taste, and I suspect there’s still room for more dimensionality reduction. I’m sure Hunch CTO Matt Gattis and colleagues are working on it! Also, it would be neat to direct the follower analytics rather than simply see the ones that Hunch deems most salient.
In summary, Hunch is keeping it interesting. Definitely a startup to watch and learn from.