Predictive analytics is the use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data. The goal is to go beyond knowing what has happened to providing a best assessment of what will happen in the future.
Why is it important?
- Detecting fraud
- Optimizing marketing campaigns
- Improving operations
- Reducing risk
Who’s using it?
- Banking and Financial Services
- Oil, Gas and Utilities
- Governments and the Public Sector
- Health Insurance
How it works?
Predictive models use known results to develop (or train) a model that can be used to predict values for different or new data. Modeling provides results in the form of predictions that represent a probability of the target variable (for example, revenue) based on estimated significance from a set of input variables.
There are two types of predictive models:
- Classification models predict class membership
- Regression models predict a number
- Growing volumes and types of data, and more interest in using data to produce valuable insights
- Faster, cheaper computers
- Easier-to-use software
- Tougher economic conditions and a need for competitive differentiation.