The enormous appetite for social media presence and interaction from companies and consumers alike continues to grow with every tweet, every like, each pin and comment. An audience that many businesses once viewed as an abstract and unpredictable collection of idiosyncratic communities has successfully morphed into an invaluable and influential collective of online conversations. Many businesses today live and die by consumer opinion and word-of-mouth reputation. And that’s why the smartest businesses will monitor, measure, analyse and tap this ecosystem for every relevant insight that might shape their business strategy.
Modern business social media monitoring and analysis demands a disciplined, scientific approach, rather than a non-scalable, miscategorised overview of online opinions. Companies must systematically implement a scientific methodology that moves from mere social listening to social intelligence. This means analysing the content of Big Data, respectfully extracting meaning from public conversations, and making actionable sense out of what is being said. Only in this way can social sources be used smartly to activate and recalibrate marketing, customer service, competitive insights, fruitful prospecting and new business programmes.
Straight answers to smart questions
Forrester Research recently claimed that social media analysis, now labelled social intelligence, is: “the concept of turning social media data into actionable marketing and business strategy”. It is no longer enough to just report what is being said out there, but also why it’s being said, how, and by whom.
The science behind all this is serious stuff, and we’ve moved firmly beyond merely brainstorming ideas with creative teams.
By revealing actual consumer sentiments about the brands and products they use rather than just keywords, businesses can answer vital questions about their own market position as well as that of their competitors. Questions most businesses would like to be answered include: ‘How do consumers really feel about our brand and products?’, ‘What don’t they like about us?’, ‘Why do they sometimes choose other brands?’, ‘What is most important to them?’ and ‘What can we do to make them like us more?’
The science behind all this is serious stuff, and we’ve moved firmly beyond merely brainstorming ideas with creative teams. Social media is now sufficiently mature and pervasive that the only sensible approach is to develop quantitative analysis methods with teams of mathematicians, social scientists and statisticians; applying statistical theory to analysing huge volumes of unstructured text. We first started tackling these issues using a unique algorithm developed at Harvard University to measure not only positive and negative sentiment, but to go further to uncover exact themes, opinions and drivers of sentiment inside social media conversation. Instead of just learning what people are saying, business can now learn what their customers are thinking, which means they can solve problems more quickly and efficiently than their competitors can.
Here comes the science bit
Of course it’s also important to refine scientific analysis by bringing human judgement into every equation, incorporating a ‘teachable’ algorithm that finds real meaning behind Big Data. It’s critical not to misinterpret artificial results that might skew business intelligence and result in bad decisions. For example, an algorithm alone cannot always determine context, nuance or shifting social rules. Deeper insight is required, if only to determine whether a ‘wicked’ company is a good or bad thing, or whether seemingly innocent terms like ‘class’, ‘safe’, ‘smart’, ‘traditional’ or ‘cheap’ are complimentary or pejorative.
The good news for forward-thinking businesses at this moment is that the largest source of unfiltered information about consumer opinions ever available is yet to be efficiently tapped by the vast majority of companies. But it’s definitely high time to get scientific with social media. It’s important to find out precisely what consumers are saying about you, why they’re saying it and how you should already be using this intelligence to drive better business decisions.