As one example, Joe Stanganelli, a consultant in brand management and marketing and AllAnalytics.com community member, pointed us to this post on the spring launch of a Derwent Capital Markets hedge fund that predicts fluctuations in the stock market by monitoring Twitter.
"Gathering and analyzing sentiment on social media is important because it's getting the information you need directly from the horse's mouth -- that is, the general public," Stanganelli told me in an email exchange.
On the All Analytics message board, Stanganelli indicated he thought it interesting that for the hedge fund Derwent is evaluating everyday communications from 10 percent of Twitter's estimated 10 million daily users and not Tweets specific to the stock market or made by stock market experts.
Derwent reportedly based its approach on a recent scientific article titled, "Twitter mood predicts the stock market," written by Johan Bollen and Huina Mao, with the School of Informatics and Computing at Indiana University, Bloomington, and Xiao-Jun Zeng, with the School of Computer Science at the University of Manchester in the UK.
In the article, the scientists suggest that fluctuations in the stock market may be predicted by levels of calm in the average Tweet. They're not alone in this belief.
For example, researchers at the University of Illinois at Urbana-Champaign predicted S&P 500 performance based on anxious or worried feelings expressed over LiveJournal blogs, and researchers at a German institution discovered Tweet volume and bullishness was predictive of market returns, Stanganelli said. In this latter case, however, researchers only used stock-related Tweets.
Of course, using social media to gauge the sentiments of your audience is nothing new. Media marketers have done this for years using various channels to get a better feel for their followers and customers. They've even done so to develop relationships and connections with their customer bases to get an additional sense of the group dynamics.
What is new is the degree to which companies can reduce these sentiments to pure data, using it to predict specific outcomes. Also unusual is the use of data gathered from the public at large and not deliberately from customers or interested people with any real understanding of the business being measured.
To what degree can this type of analysis be adapted to other industries and with what level of success? Could the sentiments of the general user on the social Web hold the key to predicting the success or failure of other businesses or ventures some day? Tell us what you think on the board below.