Analysing data sources and deriving business insights through them has become a norm for a majority of organisations today. Corporations are well aware of the potential value that big data represents, and are more than willing to invest in systems that can make sense of all this information and deliver predictive business insights. Interestingly though, there is a form of intelligence that doesn’t get nearly as much attention as it deserves – and that is sentiment analysis.
It is primarily an automated process that discerns the intent or opinion behind a spoken or written statement. This, in turn, allows companies to clearly visualise and gauge overall public opinions regarding various aspects of their business – from their products and services to campaigns and brand image. So how can companies use this information to formulate their strategies, and if they haven’t adopted sentiment analysis why should they? Let’s find out.
In essence, the whole process involves using a machine learning algorithms into models – like the ones SAS offers. These models are then able to sift through unstructured text via a number of varying approaches. This includes a combination of natural language processing, machine learning and linguistic rules. Through this process, businesses are able to assign certain values to sentiments that are held regarding them. Generally, the categorisation boils down to whether it’s positive, negative or neutral.
Now, you might be inclined to ask what exactly ‘unstructured text’ is. This pretty much refers to the vast amount of data that is stored in textual form; this includes emails, social media posts, chat interactions, website tickets and an array of other items and platforms.
All of this has a great many advantages for businesses. For one, employing tools for sentiment analysis allows for scalability, as you’re processing large amounts of data at relatively low cost. What’s more, the real-time analytical potential of these platforms can be vital to businesses when faced with a crisis situation or a sudden PR issue. But this isn’t all that sentiment analysis offers a business, it has quite a few active applications as well.
A company’s products are at the heart of their success. Businesses are always looking to create the best products possible – ones that best meet customer expectations while staying superior to the alternatives the competition offers. It should be obvious then how sentiment analysis can be a major advantage for businesses with regards to product development and optimisation.
Think of all the thousands of reviews left on websites and social media that your analytics tools will scour through, and imagine all the opinion pieces, tweets and social posts that it will process. All of this will inform your business about what is and what isn’t working with your products. For example, think of a company that develops smartphones – sentiment analysis could reveal that users are extremely happy with the speed and performance, are content with the design, and are unhappy with camera quality. Now you know exactly what to optimise, what to completely change and what to keep as is for the next round of product development.
There’s a myriad of communication channels that connect consumers with businesses these days. Not only does this mean that customers have more power than ever to directly express their opinions about your business – but it also means that all the communications you put out are subject to judgement by your consumers, as well as the public at large. The sheer ambiguity of all this – compounded by the vast volume of opinions floating about – can make both using and analysing these platforms seem like a daunting task.
Sentiment analysis is able to demystify all of this, however. By combining sentiment intelligence with descriptive analytic techniques, businesses are able to clearly visualise what’s being said about them on all these different platforms. In fact, you’ll even be able to see how users feel about your competition.
All of this works on a real-time basis, of course, so you’ll be able to identify how your messages, offers and campaigns are performing on the fly and course-correct as needed. This makes the decision-making process simpler as well – there’s less room for doubt and anxiety now as you’re clearly aware of what your users’ opinions are. What’s more, you have a backlog of information regarding user opinions on past decisions to base your new choices on!
All of this intel about user sentiments can be incredibly useful for a business’ customer support strategies. After all, you are aware of what your users think about you, your products and the messages you’ve put out. Now, should they decide to get in contact with your organisation, your customer care personnel are better prepared to engage with users who might have complaints or issues. And of course, these interactions are a great source of data as well!
Businesses can log all the responses users have given in response to their customer support. Then, once again through sentiment analysis, you can begin to categorise which responses worked best and continuously streamline your customer support process.
As we’ve established, utilising sentiment analysis can yield a great many benefits for your business. However, as we mentioned earlier, the accuracy of your analysis depends on how intricate your algorithm-based models are. What’s more, there is an element of human supervision that is crucial for deriving precise intelligence about user intent.
This is why it’s imperative that you get experts involved in the whole data transcribing process. This frees you up to strategise new business processes, products and campaigns, instead of worrying about all the intricacies of data processing. You can learn about all the services Selerity offers that help you utilise sentiment analysis via SAS tools, right here.