The analytical tools you need for your social media campaign

Is this a new science or a new fad?

Social media sites make up at least half of the top 20 websites in most regions of the world, with Facebook this week being named the most searched for search term by Hitwise. However, this has left marketers that primarily judge the health of their brands in the online world by web statistics and click-through rates, in real need of rethinking their social media strategies.

Jennifer Major, Business Development Manager, Communications, Media and Entertainment Practice at business analytics company SAS UK, said, “We’ve seen the blunders big companies have made by not monitoring and, more importantly, not reacting intelligently and expeditiously to social upheaval in the digital space. In today’s current market, businesses and their reputations cannot afford to take any kind of hit.”

Through blogs, message boards, fan pages and the like, the internet is fast rebalancing the relationship between customers and companies, while social media networks, such as YouTube and Twitter, are giving consumers instant and, occasionally, very powerful ways to ’strike back’ and make their voices heard.

Companies from all industries have quickly realised the necessity to go where the conversation is going in order to remain relevant and pertinent to customers. Big companies, such as Ford, Procter & Gamble and Coca-Cola to name but a few, have recognised the need to use the internet and, more importantly, the rich vein of market intelligence that social media sites provide, to ‘listen’, monitor and, if need be, counteract any bad publicity these virtual – and viral – conversations might be generating in order to avoid a PR disaster.

There are a number of alternative social media analysis tools in the marketplace, but knowing which one is the right one your brand is key. Businesses are clearly spoilt for choice. Social media technology and techniques include web crawling APIs that collect keywords and free format text relating to specific criteria, text mining applications that analyze key concepts, features and even segments of common terms, as well as pattern matching, probabilistic modelling and sentiment analysis technologies that evaluate the information as positive, neutral or negative.

Such tools and techniques have been added to the product portfolios of an array of companies including internet/publishing/data mining/research/marketing organisations including the likes of Factiva, Motive Quest and Omniture, to keep on top of the social media game. Is this seemingly ‘Jack of all trade, Master of none’ approach to social media analysis a reliable one or should businesses wanting to monitor, measure and understand social media conversations opt for organisations with an expert analytic heritage at its core?

A combination of the above techniques can undoubtedly provide a richer and more contextual set of data than traditional keyword spotting tools – yet which is the best one to adopt? It is clear that there are companies out there that generally take a couple of different stances to analysing social media information – so it is important to note the distinctions, which will ultimately impact the relevance, reliability and validity a business might be looking for.

The simplest algorithms work by scanning keywords to categorise a statement as positive or negative based on simple binary analysis (‘love’ is good ‘hate’ is bad). However, such an approach fails to capture the subtleties that bring human language to life: irony, sarcasm, slang and other idiomatic expressions. Social media, which are by nature dynamic and based on unstructured forms of information, do not fit neatly into traditional database-driven analytics systems.

Sentiment Analysis is an important, but very hard to master, science and it is still in its infancy.

While it can be quite accurate it does not necessarily make the data valid or useful for making strategic decisions grounded in effective brand monitoring. Also when it comes to languages, things get more complex than simple tweet or text analysis, making success an even more elusive concept for sentiment analysis, where cultural differences – an American ‘quite’ would mean ‘very’ whereas an English ‘quite’ would refer to ‘not at all’ – and linguistics come into play – sinful isn’t always sinfully good chocolate.

Understanding social media is much more sophisticated and demands building in an analysis of sentiment. It is fair to assume that the added value promised from a company whose heritage isn’t a sophisticated analytical one, would not be equipped to analyze and provide as relevant results when translating finer linguistic nuances, cultural factors and the vagaries of human emotion, and might not help avoid what could be very damning social media comments.

In this fast-paced market, it is important to be able to review information in near real-time.Bringing all this data together – research, monitoring, sentiment analysis and other analytical capabilities – can start to provide the grand unified vision that overlays all relevant data sets for correlative analysis.

Only this way will we start to determine an ROI for social media campaigns – finding the meaning within the measurement is critical.