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Service aims to mine social networks for consumer insight

By Chris Kanaracus , IDG News Service , 03/26/2008
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The market is ripe for services like that of Networked Insights, which mines data in social networks and discovers how companies' brands are being talked about by those sites' users and visitors, according to the company's CEO and founder Daniel Neely.

The situation is part of an overall trend in which companies are trying to squeeze customer insights out of megasites like MySpace or of their own social network implementations built with "white-label" products, he said.

"In 2008, companies are getting smart about it. ... We think we've cracked the code on helping you know what's going on across the social networking landscape," he said Wednesday.

The Madison, Wisconsin startup announced a set of upgrades on Wednesday to its Customer Insight Platform, which crunches social network data to draw insights into factors ranging from its customers' brand recognition and reputation -- or a competing one's -- to the relative "influence" of a given user.

The hosted offering works either in concert with Networked Insights' white-label social-networking platform or with data drawn from third-party sites such as MySpace. However, user data stored on Facebook, the world's second most popular social network, is unavailable to companies like Networked Insights.

Neely acknowledged that the inability to access Facebook data is a significant stumbling block. However, he asserted, "I think the pressure is going to be on Facebook to open up a piece of what they're doing." He also speculated on the possibility of a partnership with the company.

The update adds a redesigned interface through which users can tap analytics grouped around six focus areas: customer needs, content, competition, brand, product and customer loyalty. Another new feature is an "influence metric" given to each member of a community.

Networked Insights' software uses natural-language processing technologies developed in-house, which work together with its "interactivity algorithms" to serve up results, Neely said.

It is sophisticated enough to determine whether users are male or female, and avoid obviously incorrect data, he claimed. For example, he said, "[Listed user] ages that are way off the chart, we kick out."

A user's "influence" can be gleaned based on how they are engaging with other users, sharing information and other factors, he said.

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