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
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.
As more social media analytics rely on machine learning, popular open platforms like R, Python and TensorFlow serve as social media analytics tools.