To understand correctly what customers want, when, why and how they want it, retailers must move towards sentiment analysis, a rapidly expanding technology that draws on consumer demand based on natural language processing.
Ironically thus emulating one of the main cornerstones of social media: understanding if people like you or not. This is the promise of sentiment analysis – it tells companies what people think – and ultimately how they act – their brands.
In raw form, the analysis of feelings has been around for some years. But with advances in data collection technology, social media analysis is coming up like gangbusters. Using high-level data collection technologies such as natural language processing, text extraction and data mining, sentiment analysis collects, categorizes and analyzes the comments consumers make about a given brand – all in one question of sectors. It makes no distinction between bad news and goods.
The “Opinion Mining” era
Gathering useful data from the whims and moods of consumers who are often irritable is not easy, but it does
“The analysis of sentiment is also defined as opinion mining: the science of exploiting and analyzing the consumer’s conversation to understand whether consumers feel “positive”, “negative” or “neutral” with respect to a given brand, product or topic”, says Maxime-Samuel Nie-Rouquette, a successful manager of Semeon Analytics, a data analysis company based in Montreal, Canada specializing in sentiment analysis.
If the goal is struck dead, sentiment analysis “can do wonders for retailers in providing better knowledge and experience of customers,” says Nie-Rouquette. “By listening to online conversations (such as social media, blogs, forums, etc.), a company can understand consumers’ emotions and provide them with a connection that goes far beyond whether a product simply sells or not.”
Nie-Rouquette notes that the applications for the analysis of opinions in the world of retailers are numerous.
“Retailers can monitor customer reactions and feedback to push content for “virality”or exercise a damage control strategy when dealing with asparagus water crisis problems affecting Whole Food,” he says. “Retailers like Walmart, Target and Costco use sentiment analysis to understand what their customers are interested in and exploit this information to re-position their products, create new content or even provide new products and / or services.””
In a technological sense, sentiment analysis is a unique blend of machine learning and artificial intelligence, which allows companies to use digital data tools to identify useful and actionable actions that direct social media consumers towards their products and services.
But for companies that are really digging social media data deep within the consumer, sentiment analysis really gives them valid options.
“Short of biometric data or Neurosky headsets at all, there are three general measurement areas that retailers can use to detect emotion or feeling in their customers: voice, text and facial analysis,” says Sean MacPhedran, one e-commerce specialist at Smith.co who has worked with heavyweights like AT&T and Microsoft to better guide the transaction with high-tech tools like artificial intelligence and cognitive data sets.
The most direct use of sentiment analysis tools for marketers is the measurement of trends in general social media sentiment, MacPhedran states. For example, the tracking of “Macy’s” mentions and looks at the words around it for emotions and modifiers. Emotional words are intuitive enough to understand. “Crappy” or “hate” are bad. “Fantastic” and “fantastic” are good.”
But there is obviously more nuance than this, he said: The most complex intuitions derive from the modifiers.
“For example, is there a specific position associated with groups of negative feelings? Is there a specific problem that is associated? “Resi” for example, could indicate that people are generally unhappy with a return policy,” MacPhedran said.
Within the larger data sets, there will be many trends (think of them as moving vectors) that operate independently, and only using strong multivariate analysis (such as artificial intelligence or machine learning) will make the trends clear and workable, notes MacPhedran. “It is not enough to know the “average feeling” related to a brand – it would be like knowing the “average time” for the entire planet tomorrow,” he notes.
A new era in sentiment analysis
MacPhedran says that the “next generation” of sentiment analysis, which will be released in the next five years, is very exciting.
“The Microservice APIs are able to measure emotion in written content, but also in vocal and facial expressions,” he says. “For example, let’s say we have a CRM system that knows the users’ social handles and has a usable customer image, with the client’s permission, for face recognition customization.”
But it’s not all sunny skies for sentiment analysis – especially if companies don’t arm themselves appropriately, from a technology perspective.
“There is a problem,” notes Nie-Rouquette. “Because the backbone of sentiment analysis uses Big Data, using data sets that include thousands and thousands of data points, retailers need to have enough data (including customer conversations and reviews) to get useful information.”
“So in some cases where data is scarce, sentiment analysis may not provide good insights due to the lack of statistical validity. Resellers must also make sure that they involve their communities to promote certain conversations.”
This is a solvable problem, however, and a company should face if they want to receive the maximum benefits of sentiment analysis.
“It’s a good idea,” adds Nie-Rouquette. “With the availability of data on various online sources, companies (and retailers in particular) can take advantage of sentiment analysis to gather information that would not be possible using traditional marketing methods.”