Predictive Analytics

A part of advanced analytics to make future forecasts.
Predictive analytics are a part of advanced analytics that provide a probable picture of what might happen in the future. Considering the previous behaviors from the descriptive analytics, predictive analytics might help in setting the real-time goals in an effort to optimize planning within an organization.

Predictive analytics capture the likelihood of a future outcome using different statistical techniques including, machine learning, root-cause analysis, predictive modeling, artificial intelligence, data mining and other forms of analysis based upon predictive models.

Predictive analytics identify future trends and patterns, and they provide insights about what may come in the future. When a company forecasts future outcomes and plans accordingly, then there is a far higher probability that it will be able to measurably improve the operations throughout the various internal structures of the organization.

Real World Examples:

• Airlines: Based on past customer behaviors, travel patterns and peak travel times, an airlines company can appropriately price the ticket fares to increase revenue.
• Credit Score: Predictive Analytics help to determine the predictive score of an individual. A credit score helps in identifying, whether or not a customer can pay bills on time and how much of a financial risk they will be to the crediting company.

Predictive analytics can be very complex and require a much deeper understanding than traditional descriptive analytics. The majority of predictive analytics are conducted by data scientists, business analysts, and statisticians. The success or failure of an organization heavily depends on proper future analysis.