Social media analytics is the practice of gathering data from social media websites and analyzing that data using social media analytics tools to make business decisions. The most common use of social media analytics is to mine customer sentiment to support marketing and customer service activities.
The first step in a social media intelligence initiative is to determine which business goals the data that is gathered and analyzed will benefit. Typical objectives include increasing revenues, reducing customer service costs, getting feedback on products and services, and improving public opinion of a particular product or business division.
Metrics to track
Business metrics derived from social media analytics may include customer engagement, which could be measured by the number of followers for a Twitter account and number of retweets and mentions of a company's name. With social media monitoring, businesses can also look at how many people follow their presence on Facebook and the number of times people interact with their social profile by sharing or liking their posts.
More advanced types of social media analysis involve sentiment analytics. This practice involves sophisticated natural-language-processing machine learning algorithms parsing the text in a person's social media post about a company to understand the meaning behind that person's statement. These algorithms can create a quantified score of the public's feelings toward a company based on social media interactions and give reports to management on how well the company interacts with customers.
There are a number of types of social media analytics tools for analyzing unstructured data found in tweets and Facebook posts. In addition to text analysis, many enterprise-level social media analytics tools will harvest and store the data. Some of these tools come from niche players, while more traditional enterprise analytics software vendors offer packages dedicated to social media intelligence.
Importance of social media analytics
There is a tremendous amount of information in social media data. In decades past, enterprises paid market research companies to poll consumers and conduct focus groups to get the kind of information that consumers now willingly post to public social media platforms.
The problem is this information is in the form of free text and natural language, the kind of unstructured data that analytics algorithms have traditionally. But as machine learning and artificial intelligence have advanced, it's become easier for businesses to quantify in a scalable way the information in social media posts.
This allows enterprises to extract information about how the public perceives their brand, what kind of products consumers like and dislike and generally where markets are going. Social media analytics makes it possible for businesses to quantify all this without using less reliable polling and focus groups.