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How Predictive Analytics Can Make Money for Social Networks

Here are some specific ways how analytics will make social networks money, including Recruiting, Sentiment Analysis, Market Fluctuation, Recommendation Engines, and Location-Based Marketing.

Dr. Rado Kotorov is chief innovation officer at Information Builders, and is responsible for emerging reporting, analytic and visualization technologies. He has developed analytic models and applications for the pharmaceutical, retail, CPG, financial and automotive industries.

social networks As the quantity of irrelevant information has exploded online, so too has the market for the delivery of targeted offers and information. Social networks, in theory and in practice, expose many people to contact and influence. Without precise models, people will continue to be bombarded with ineffective offers and other irrelevant information. Predictive analytics, a branch of data mining concerned with predicting future probabilities and trends, applies a filter to users' online interactions with the aim of delivering more value from a sea of irrelevance.

With increased value comes the potential for social networks to make money as well. Here's a look at some specific ways in which predictive analytics will make social networks money.

Many recruiting sites out there on the web, from LinkedIn (LinkedIn) to SelectMinds to Monster, promise to be able to match candidates with job requirements in unique and increasingly accurate ways. Predictive analytics is at the core of their business model, as it automates the process of making these matches.


Sentiment Analysis
As sites like Twitter (Twitter) and Facebook (Facebook) gain value to the business world, many companies have cropped up to analyze and establish what the sentiment is of the collective online intelligence and also to identify individuals with influence and authority. Companies including Klout, ViralHeat and Radian6 (Radian6) all scan blogs and other social media channels with predictive models to determine if the content surrounding a brand or person is negative, positive or neutral.

Market Fluctuation
Social media channels are open to everyone. Day traders, retail investors and analysts are cruising around on Twitter and Facebook. What these types of people say and do online is not insignificant in an era when [Flash Crashes and Fat Fingers] are being closely scrutinized and regulated. New models are cropping up to predict stock fluctuations based on Twitter posts. Similar to sentiment analysis, these companies are able to look at the total number of tweets, as well as positive and negative comments to predict whether a stock price will go up or down. ...

Recommendation Engines
No one likes to be bombarded with irrelevant offers and content while using their favorite social network. But the more active you are online, the more effectively predictive analytics can work to deliver targeted and relevant offers.

Sometimes it feels like Facebook knows you better than you know yourself. RSVPed "Yes" to that big gala? You may see a discount offer for Saks. ...

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