A new Hunch points to the money with behavioral targeting

Hunch, the “decision engine” startup cofounded by Chris Dixon (pictured) and Flickr’s Caterina Fake, today announced it has refined its homepage and service to include users’ personalized recommendations front and center.

With this change to the website, Hunch appears to be moving away from the decision engine concept and refocusing on personalized content recommendations powered by behavioral data.

Companies targeting behavioral data appear to be gaining significant funding and acquisition momentum. Online shopping network WeShop just landed $4.25 million to get its members to provide behavioral shopping data to retailers and brands willing to offer deals and promotions. Professional network LinkedIn recently agreed to acquire recommendation engine technology mSpoke to provide its members with more relevant content. Finally, Hunch itself recently secured a round of funding for $12 million.

Hunch’s personalization engine appears to be on target. After I logged onto the site using my Twitter credentials (Facebook Connect is available as well) and answering 20 simple questions, I was provided recommendations for magazines, TV shows, and new books. The fun doesn’t stop there, by clicking “show more” the user can continue to get recommendations on various categories. I also received recommendations for vacations (Las Vegas!) and even credit card companies (see image below).

Ultimately, it would appear that behavioral data, though beneficial to consumers in finding more relevant content, is even more beneficial to advertisers and brands. These companies can now utilize these services to find a more engaged and relevant group of consumers. As I clicked at a new book recommendation, the next page not surprisingly had two links to where I could make a purchase, one at Amazon and one at Barnes & Nobles — both sponsored links.


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  1. [...] Hunch, founded by serial entrepreneurs Caterina Fake, Chris Dixon and Tom Pinckney, launched in 2009 as a question-and-answer decision engine. The startup later switched gears to focus on building a “taste graph” that connected web users around their affinities. The site uses this data to make predictions about its users and provide them with personalized content recommendations. [...]