Why is Predictive Analytics important?

Predictive Analytics

Although predictive analytics is known to people for decades, the idea of leveraging it for the growth of the organization has come into the picture recently. Organizations are now turning to predictive analytics to increase their income and competitive advantage.

Why are Organizations planning to leverage it now?

  • Evolving volumes and types of data, and more emphasis on using data to provide valuable insights
  • Software that is easier to use
  • Need for competitive differentiation to stand out

Predictive analytics is no longer a domain restricted to mathematicians and statisticians. Business analysts and line-of-business experts are using this technology to develop their business with the help of valuable insights.

Why is predictive analytics important

Organizations are taking to predictive analytics to enhance their bottom line and competitive advantage. The most common uses include:

Detecting fraud

We can improve pattern detection and prevent criminal behavior by combining several analytics methods. High-performance behavioral analytics examines all actions on a network in real time. It helps in spotting abnormalities that may indicate fraud, zero-day vulnerabilities, and advanced persistent threats.

Optimizing marketing campaigns

We can use predictive analytics to determine the customer responses or purchase behavior, as well as promote cross-selling opportunities. These models help organizations attract, retain and grow customers and facilitate the growth of the organization.

predictive analytics

Improving operations

Managing resources and inventory has become easier for many companies due to predictive analytics. While the airlines are leveraging predictive models to set the ticket fare, the hotel industry is using it to maximize the occupancy. Hotels try to predict the occupancy rate for any given night and in turn increase their revenue. Predictive models also empower organizations to operate more efficiently.

Reducing risk

The banking industry uses predictive analytics to calculate the credit scores to assess a buyer’s likelihood of default for purchases. The predictive model incorporates all the data that is relevant to determine the creditworthiness of a person. The number generated by the predictive model is known as the credit score. Some of the other risk-related uses include insurance claims and collections.

So, Is your enterprise ready to leverage Predictive Analytics for your business? Talk to us today.

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