Widgit-based and semantic- and behavior-focused recommendation network f>>forward the first half of this year aims to take its social analytics capabilities in new directions, a pre-cursor to the Holland-based semantic technology provider’s planned entrance to the U.S. market this year.

The company expected by the end of last year to have some 250,000 to 300,000 blogs in its part of the world using its widgit to help deliver personalized recommendations in real time to readers, both of internal content and those from the network-at-large of content creators who are part of the f»dforward network. Its first swipe at social analytics was designed to help these content providers discover how engaged readers were across the months in the topics they and other partners in the f>>forward network pursued. “This was analytics for publishers, so the editorial staff could see what people are reading about but maybe you haven’t written about yourself yet,” says Lucien Burm, CEO and co-founder of Kimengi, which provides the technology. “If you learn from dashboard analytics it will help you in the front end.”

Work on a commercial version of the social analytics widgit is now underway that will enable it to be leveraged by corporate brands, to help them learn what other kinds of information their buyers are most apt to gravitate to, and how that may change over the course of a week or a month or longer. Brands and ad networks that provide the advertising for blogs and other content sites gave f>>forward a strong message that if they had this kind of information, they could change how they looked at products and product communications, Burm says. “They look at it from a research perspective,” he says, which can help them fine-tune focus, figure out where to reach the audience based on those revisions, and continue checking back with the network to see how it's playing out.

New Version, Same Foundation

The new solution is basically the same tool publishers can use to gain real-time insight into article consumption and direction, but now tailored for and utilized in the context of product pages, such as landing pages. “We can look at our network and tell you what other kinds of topics or values people have who are interested in your kind of product, and tell you what other things they like to read, what the potential of your product is in the network," says Burm.

Those who deploy it can use it to better understand the profiles of people are visiting product information on their site, and then look at the network and see what they find to be interesting reading next to your product positioning. "From this you get interest profiles, all aggregated, and we can tell you what keeps them occupied," he says. "This kind of information comes from outside your web site, and you normaly wouldn’t be able to get it without some old-fashioned research."

This is a standard view of the widget, showing recommendations based on tracks of all users reading this article.

"We are a big recommendation network so in the end it’s all a big network. All the companies and publishers are working together in this new way,” he notes.

More broadly speaking, over the next few months Kimengi plans to focus mainly on the social trail side of its network – how people are moving around it, and what does that mean to users, publishers and businesses. “The semantic web isn’t moving forward at the pace people were hoping for a few years ago, though it is moving forward,” he says. “People were saying that recommendation technology would come after the semantic web. I think it is concurrent, so we are moving forward in the recommendation space and the semantic space, and each will constantly influence the other.”