Big data · Predictive Analytics · Retail Analytics · technology

The Beginner’s Guide to Predictive Analytics

What Is Predictive Analytics

Predictive analytics is a technology that spans over techniques like data mining, modeling, statistics, artificial intelligence, and machine learning. It uses these techniques, to hold together business management, modeling process, and information technology that help in making decisions about the future.

Companies take up predictive analytics tools for the following reasons-

  • With the help of business history, predictive analytics eliminates or prevents recurring risk factors, like insurance claims and collection-based aspects. It also identifies criminal behavior and enhances the overall pattern detection.
  • Using the Cross-selling, Up-selling and the Next selling techniques, businesses can not only retain customers but also attract new ones.

Basic Models in Predictive Analytics

  • Clustering Model- It helps to create customer segments based on the behavioral patterns, products purchased by different age groups, and brands that need to be pitched to specific customers.
  • Propensity Model- Propensity algorithms can determine how long you can retain a particular customer, which customers are ready for a purchase and also help you identify high-value customers whom you might lose.
  • Collaborative Filters- These predictive analytics techniques are commonly found on most eCommerce websites, where a buyer comes across suggestions of products that other customers bought along with the particular product.

Three of the most commonly used predictive analytics models are:

  • Decision Tree
  • Regression
  • Neural Networks

The advancement in technology and change in markets has led to every business, irrespective of the field, to take to big data predictive analytics. Finance, Retail, Utilities, Government, Healthcare, Manufacturers are some of the fields that use predictive analytics tools to improve their performance and in turn revenue.

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