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Advanced Technology: Analyzing The Human Language, Social Media, Consumer Sentiment And More

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Whether it's through comments posted on Twitter, Facebook, LinkedIn or other social media sites, companies these days are listening to what we're saying. They realize that our complaints -- or compliments -- can be very public. What we say can be read, shared and re-posted by thousands of others via social networks.

Recently, I ordered a gift online from one of my favorite retailers. Unfortunately, it arrived two days later than promised. I had the option of complaining to my Facebook friends and Twitter followers, but I didn't need to. When I called the customer service hot-line, the retailer took good care of me, waiving the shipping charges and providing a decent credit for a future purchase.

Companies are getting smarter. They realize that it costs six to seven times more to acquire a new customer versus retaining existing customers. Lots of companies are using analytics to better understand social conversations and improve customer service.

We are drowning in data. It's estimated that there will be one trillion devices operating in the world by 2015. IDC predicts that the amount of digital data in 2020 will be almost 50 times larger than that of a decade earlier. Eighty percent of the information will be what's called "unstructured" data, including all those YouTube videos, baby pictures, and "songs of the day" floating around the Internet in emails and instant messages. This massive amount of data is something we call "big data" because, well, there's so much of it.

And to make sense of it, we need to analyze it.

In fact, by using advanced analytics and technology to analyze human language, (in other words, the tons of big data associated with language) we can harness that information.

Recently, in the build up to the Oscars awards, we used an algorithm, or mathematical formula, to analyze millions of "tweets" on Twitter to see what people were saying about potential Oscar winners. Turns out "Girl with the Dragon Tattoo" was mentioned very positively in Twitter chatter, yet did not receive a nomination for "Best Picture" category.

The so-called "Oscars Senti-meter" combed through a high volume of tweets daily and used language-recognition technology to gauge positive, negative and neutral public opinion contained in the 140-character messages. We call this process "sentiment analysis."

A similar project to analyze sentiment found that Quarterback Eli Manning of the New York Giants was more popular in social media than Quarterback Tom Brady of the New England Patriots -- before the first play of this year's Superbowl Game, won by the New York Giants. While popularity didn't determine the game's outcome, one can envision how it can influence future player contract negotiations and sponsorship valuations.

In addition to sentiment analysis, organizations are also doing social network analysis, which starts to measure influence. By doing so, companies can begin to pay more attention to the voices that are likely to have the greatest influence.

At the same time, companies are also looking at broader sentiment trends to predict how confident people are feeling, so that they can more accurately predict consumer confidence, say, ahead of a shopping season. We are only experiencing the introductory effects of the analytics that will soon grasp more and more influence in our world. We've only just begun to harness the power of big data and analytics.

For more information on how business analytics can help organizations use the power of insights to shape business outcomes, click here.


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