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Social media analytics technology promises the ability to find out what customers really think

People have always talked about the businesses they patronise, the public services they use and the politicians that govern them, whether it is to make recommendations or to air grievances.

In the past decade, however, it has become possible for organisations to eavesdrop on those conversations. That is because, more than ever before, they are taking place in public, through social networks, personal blogs, video-sharing sites and all other forms of social media.

The nature of the web means that the content of these sites can be analysed systematically, providing marketers with an unprecedented window into public opinion and customer sentiment. The value of this is self-evident, and it comes as no surprise that the nascent field of social media analytics has already attracted a gaggle of software vendors, among them IBM, the SAS Institute and Autonomy, all of whom have announced their entrance to the market this year.

“Social media analytics is an important way to track your brand’s health and the issues that are associated with certain products and your competition,” explains Gartner analyst Andrew Frank. “It is a necessary piece of the puzzle for how social media can be leveraged by marketers and how investments can be tracked and verified.”

However, it is still an emerging field, and there are issues to be resolved. What, for example, is the best way to interpret social media content? How accurate are the available tools? And does it place consumer privacy under threat?

Buzz and sentiment

There are two common styles of social media analytics tool – those that assess the ‘buzz’ around a given topic and those that assess ‘sentiment’.

Put simply, the former is purely a measurement of how frequently a brand is mentioned. The surprising power of this simple metric was seen when a political social media analysis website used it to predict the outcome of the 2010 UK general election. The final prediction – made two days before the polls opened – was strikingly accurate, despite being based on little more than word counting (see box ‘How Twitter predicted the election result’).

Measuring sentiment requires more complex analysis. The most common approach is to judge each word or phrase in a blog post, Twitter message or Facebook status update as positive, negative or neutral in tone. These judgements are then collated into an overall assessment of the sentiment of the content.

Most systems work by analysing the language algorithmically. These algorithms are developed by ‘training’ them against human subjects – i.e. employing people to assess the accuracy of judgements until the algorithm ‘learns’ the correct interpretation of words or phrases.

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