Using Social Media Analytics for Hidden Business Insights

We live in a data-driven society but how do we capitalize on the information available? Today a foremost data monetization strategy involves using social media to increase brand loyalty, generate leads, drive traffic, and ultimately make good business decisions. Unfortunately, very few utilize this opportunity.

Tapping into Social Media Data

Social media analytics is a relatively new but emerging field, however, as social media has become more mainstream, more people have learned to leverage this data to increase revenue. Textual elements of social media include comments, tweets, blog posts, product reviews, and status updates. Social media text analytics, also known as text mining is a technique to extract, analyze, and interpret hidden business insights from textual elements of social media content. Organizations can use techniques to extract hidden valuable meaning, and patterns from user-generated social media text for business intelligence purposes.

Nearly 80 percent of data in any organization is textual in nature. And text analytics is useful in gaining an accurate understanding of the emotion and sentiment expressed over social media channels related to a brand or a new product launch. Many companies get a better understanding of their products by simply mining textual feedback that current and potential customers provide. The volume and speed at which comments over social media are generated doesn’t allow for manual reading, so advanced text analysis allows for this information to be collected and processed.

What’s the Purpose of Social Media Text Analytics?

Many companies today are looking for “intent mining” to discover user’s intentions, such as to buy, sell, recommend, quit, etc. You can utilize social media text, such as users’ comments, product reviews, tweets and blog posts to see their intentions. Social media as the integral part of our contemporary lives and is widely used by millions of customers to express desires and needs. Companies may use intent mining to find new potential customers who intend to buy a product or use a service.

For example, an analysis of company-related tweets may detect purchase intention based on the presence of the word “buy” or “purchase.” Similarly detecting “quit” may identify and service the customers at risk of leaving. This offers companies the ability to improve customer satisfaction, as well as know what their next move will be.

Text analytics, like any other form of social media analytics, is the art and science of getting the desired business intelligence from text posted across social media. The information posted is dynamic, diverse, and multilingual. Thus, finding the right source for the purpose of text analytics is crucial for gaining useful business insight.

Using Data for Business Goals

It’s not enough to simply run out and get analytics tools that’s ready to mine data. Analytics should be strategically coordinated to support existing business goals. Without a well-crafted and aligned social media strategy, your business will struggle to get the desired outcomes from analytics. Your starting point should be aligning your objectives and goals with your social media objectives. They should always complement and reinforce each other, and you’ll get the most out of the data you receive.