Spotlight: Social Media Analytics

6This forms the basic mantra of social media analytics. Data is mined from blogs and social media platforms like Twitter, Facebook, LinkedIn, YouTube or Foursquare; measured for various metrics; analysed and applied to make business decisions.

Social media is a powerful instrument of market research and business solutions when used strategically. It is all about conversations and community interactions, where information and opinion is created, shared and discussed on an open platform. Taken together, this forms the pool of unstructured data. So whether a national University, local restaurant or branded sportswear, business goals factor-in conversations across social media, to make decisions. So current opinion, popularity or critique of the institution goods or service is measured and analysed. To enable effective actions based on the social media metrics, Key Performance Indicators (KPIs) are defined for evaluation of the unstructured data contained in blogs, social media websites and forums. For instance, customer engagement of a restaurant is measured by mentions in twitter accounts, followers and likes in social media and reviews in online platforms like Zomato.

Various metrics are used for social media analytics.

Sentiment Analysis / Opinion Mining

As the term suggests, the sentiments or opinion of the social media user is tracked to understand the mood about say, a product or maybe a new service like segregated (recycled and non-recycled) garbage disposal introduction in certain section of a city. Opinions are mined from blog posts, social media platforms, tweets or comments using tools or machine learning methodology to evaluate the success of new launches, product variations or even analyse by demographics. Sentiment Analysis  ‘identifies the Fan or Follower’s attitude towards a brand by using variables such as context, tone, emotion, among others’.

Text Analytics

A component of Social Media Analysis, Text Analytics is a technique that derives patterns and trends using statistical, linguistic and machine learning methods. It uses text categorisation, text clustering and sentiment analysis from unstructured textual information for B.I., Exploratory Data Analysis (E.D.A), market research or other study. More on IVY Blog

Social CRM (Customer Relationship Management) Analytics

CRM Analytics is a more sophisticated method of data mining and OLAP (Online Analytics Processing) that delves deep into online conversations to provide detailed and customised analytics of customers. Some metrics examined aimed at improved customer relations are profitability analysis, demographic segregation, what-if scenarios and predictive modeling. What value does this bring to an organisation? CRM analytics helps improve sales and servicing, smoother supply-chain management, lower costs and competitive pricing.

So how do you define Social Media Analytics?

Social Media Analytics or Social Analytics is the technique applied to collection of unstructured data from social media sites and blogs and evaluating such data for business decisions, in a real-time environment to develop an in-depth idea of the social consumer, to apply such insights for business decisions.

Why Social Analytics?

      #Benefits of measuring and monetizing Social Media

 For competitive advantage by evaluating customer / user preference
 Finding effective solutions from texts contained within comments
 Improving product or service
 Maximize business earnings
 Optimising customer service
 Obtaining feedback on product or service changes

#Getting the figures on a single Dashboard


#Enhancing / controlling public opinion, with insertion of contextual opinions

As organizations are learning the value of social media in ‘making or breaking’ a product or service, or

managing social media for gains; social media analytics is increasingly being applied to new domains like

the recent New Delhi elections, where the AAP win was attributed to a sophisticated use of analytics, of

which social media analytics was a prime component.


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