Hunch is the new startup of Flickr co-founder Caterina Fake; as such, it received quite a lot of attention when it was launched in March 2009 as a tool to help users make decisions. A kind of Yahoo! Answers-like, powered by the community, with an assistant approach: a series of questions that lead to a recommendation at the end. In Hunch’s own words:
In 10 questions or less, Hunch will offer you a great solution to your problem, concern or dilemma, on hundreds of topics. Hunch’s answers are based on the collective knowledge of the entire Hunch community, narrowed down to people like you, or just enough like you that you might be mistaken for each other in a dark room. Hunch is designed so that every time it’s used, it learns something new. That means Hunch’s hunches are always getting better.
Among all the questions available on Hunch (“What’s a good way to go green?”, “Which NY Times bestseller should I read?”…), a good amount are shopping-related… What camera / camcorders / Mac / credit cards / women’s shoes… to buy? A way for Hunch to monetize its audience, as recommended products are enhanced with sponsored links powered by BizRate or Amazon.
In early December, Hunch launched a new interface; as Chris Dixon, Hunch’s co-founder, explains:
The main idea behind the new interface is that you now see Hunch’s top recommendations updated in real time as you answer questions.
Besides giving instant feedback, this also lets you see how each answer you give affects the recommendations. We think it does a better job exposing Hunch’s intelligence, particularly the statistical intelligence the system has acquired from the tens of millions of user feedback clicks collected over the past few months.
It is time to do a quick review of Hunch as a shopping recommendation tool.
The “Cameras” topic is pretty popular on Hunch; how convenient for me :) It is organized around a list of questions that enable to filter, sort and rank results among 114 camera models. The topic homepage lets you start right away – first question on the left-hand side column, result set in the main column.
Three types of questions are asked:
As soon as a question is answered, the list of relevant products is refreshed; and in the end, if enough questions have been answered, you get a list of products that you can manually review and keep/remove from the result set.
The result set can then be viewed in a specific “product page”-like view, which contains some extra modules and offers access to a “why did Hunch pick this product” page.
When logged in, a set of tools that offer write access to Hunch content becomes available. I encourage you to register on Hunch, and test those tools to create new topic, enrich existing topics, enhance your profile and the statistical engine of Hunch by answering various questions: all this stuff is really clever.
Overall, Hunch provides an original experience to choose products. As all the content is produced by the community, it gives a particular flavor to the result set, even if the relevancy is not always optimal: users may be more keen to understand the results than in the case of pure algorithmic-produced result set.
A drawback of the Hunch experience for the shopping-related topics is that the community must create and maintain the product database. I think it would be an improvement to let users start new topics from an existing product database (i.e: licensing BizRate database, or using Freebase or ProductWiki databases). This would avoid users to spend time on managing product definition; and allow them to focus on questions and mapping products with answers.
CSEs are really good when it comes to find the best deal for a specific product. But, when doing product research (i.e: I want to buy a digital camera), the experience is not always satisfying. Sure, faceted navigation on tech features and user/expert reviews are convenient ways to narrow down results; buying guides can also guide users to some extent. But CSEs lack an integrated search/browse experience where tech features and product usage are mixed, and which can be appealing for expert and non-expert shoppers. Quite a challenge indeed :)
Hunch demonstrates the social dimension can improve the buying process, in a far more subtle way than what “social shopping” sites are doing. It could be wise for CSEs to get inspiration from Hunch: