© 2017 James Gaubert - The Digital Diary - Berkshire, United Kingdom

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Social Analytics

April 9, 2018

 

So today is the day that our friends at Facebook reach out to all those who had their data and information compromised - slightly disappointed that they haven't reached out to me yet - but its still early!

 

Anyway, this got me thinking about data and more importantly analytics. Clearly Cambridge Analytica know a thing or two about data, analytics and insight - but do you?

 

Social Media analytics should be an integral part of your social activity - insights and data inform creativity and decisions, without it you are relying purely on hunches and guess work!

 

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.

 

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 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.

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