April 20, 2010 – Forrester analysts introduced a new analytical framework, surrounding peer influence, to assist marketers in reaching a critical segment of social media influencers.

Forrester’s new report, “Peer Influence Analysis,” emphasizes that peer influence analysis can be applied by companies to identify characteristics of mass influencers and social network preferences, helping to guide word-of-mouth marketing strategies, according to Augie Ray, Forrester senior analyst and report author.

Forrester has identified three types of influencers, including elite broadcasters, mass influencers and those with potential influence. According to Ray, it’s the mass influencers that should not be ignored.

Mass influencers not only make up 16 percent of Americans online, but they are responsible for 80 percent of brand impressions in online social media, according to the report. Forrester estimates that they were responsible for more that 500 billion consumer-generated brand impressions in 2009 alone.

Mass influencers come in two different flavors: mass connectors and mass mavens, according to Ray. Mass connectors are frequent users of social networks, like Twitter and Facebook.  Mass Mavens are more likely to share information in forums or blogs, with more detail description and discussion.

Finding a way to appeal to both sets of these mass impression creators is key to marketing efforts, whether through a hash-tag program or added website functionality, says Ray. “It’s up to the brands to create the content that will engage them.”

By tailoring company websites to the majority, they may be omitting key elements that appeal to the segment of the market that creates the majority of brand impressions. “We tend to build websites for the wants and needs of the majority. But peer influence analysis and the discovery of these mass influencers lead us to recognize what [influencers] need on a website,” says Ray. “And, I think that will guide some different development and content strategies.”

There are many use cases for text analytics: combining insights with traditional BI reports to add more context, determining the mood of a community or social network, finding the trajectory of opinion on a subject area, and compiling a critical mass of insights about a concept or product. Listen to this episode of DM Radio to learn more.