Big Data Mining and Social Media Analytics

Big Data Mining and Social Media Analytics

Given the explosion of social media, the opinions and feedback on the social media is also becoming paramount importance to the business. CATT Ltd exposure to social media analytics technologies can help your business to quickly identify several business actions such as

Customer Churn Analysis

  • Understanding the factors leading to customer leaving your business.
  • Determining a customer’s readiness to churn, carriers can focus campaigns and deliver services at risk level.
  • Fast access to the relevant Data.
  • Identify the customers who are worth with customer data and use audience clustering to influence similar to high value.

Sentimental Analysis 

  • How your consumers and general public’s sentiment about your business, offerings, services or products.
  • Data used in this paper is a set of review of products which belongs to main categories.
  • Identification of process flow in Big Data.
  • Value of data with scalability of processing.

Opinion mining 

  • Mine the opinion of consumers and get more insight to improve your business.
  • Opinion Mining is rather than at the individual level, since in most applications the interest is rather than individual sentiment. 
  • The goal is specific, sentiment preverance rather than determining opinions of individuals.
  • Opinion Mining in social media depends on the Growth of social media and user generated content or text.

Customer basketing

  • Market segmentation based on the data available. 
  • Customer basketing helps your business to provide services/products according to the consumer segments.
  • Assortment planning.
  • Collaborative filtering for online purchasing shops.

Creating recommendation systems

  • The online e-commerce business can get more business with recommending further products or services based on the consumer buying pattern on line. 
  • Big Data is driving force with creating recommendation systems.
  • Recommendation systems can give ratings on user actions.
  • Several reputed brands like LinkedIn, Facebook uses this model more effectively.

Fraud detection 

  • The financial transaction business can scan abnormal user behavior to detect the fraudulent intent or actions.
  • By developing predictive models many insurance companies are in a better position to identify the fraud analysis.
  • Big Data analytics delivers a huge impact for fraud detection in many organizations.
  • Many Companies implemented integration and fraud analysis by using Big Data.

These analytics will provide deep insights into performance of your business and take proactive actions to correct the negative course or take leap growth. The data from social media is unstructured unlike from a database. But Big data can process both structured and unstructured data. Hence in these cases Big data can be used to process faster and efficiently.