GoodGuide has a simple goal — to become the one website you visit to understand whether a product is “good,” however you define good. That could mean safe for your kids, environmentally friendly or manufactured in an ethical way, whatever matters to you.
The company just finished its presentation at the TechCrunch50 conference in San Francisco, and it looks like another audience favorite.
If you’re looking for a good quality laundry detergent, for example, it’ll show you that Seventh Generation ranks highest overall, but it will also let you drill down to show you how that laundry does specifically on an environmental measure (its ranked only 8.6 out of ten), personal health measure (it ranks higher, at 9.6 out ten) and socially (8.8.)
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Initially, this sounds like a no-brainer idea, and the big question from the panel of judges was, “Why hasn’t someone does this already?” Chief executive Dara O’Rourke, who is also an associate professor of environmental and labor policy at UC Berkeley, says there are sites that provide product guides around individual issues — animal rights, human rights and so on — but none that try to pull everything together.
The site provides data at whatever level you’re interested in. There is an overall score, then an overall health socre, overall environmental score and overall social score, then you can drill down and find out specific data points. O’Rourke says GoodGuide measures 140 criteria in all.
The other really exciting thing about GoodGuide is its mobile functionality, which allows you to do research while you’re at the store. GoodGuide just launched a text messaging feature, where customers can send out the bar code of a product via text message, then GoodGuide replies with information. The company also plans to launch an iPhone app in three weeks.
I haven’t been at every TechCrunch50 session, but this is my favorite of the companies I’ve seen. The judges were unanimous in their support too. They pointed out that the idea may sound basic at first, but compiling and weighing this data actually requires a lot of work and expertise.