Predictive Analytics

The complexity of business environments today means that identifying rules and connections between different features is often beyond human processing capabilities. Predictive analytics brings together advanced analytics capabilities including statistical analysis, predictive modeling, data mining, text analytics, entity analytics, optimization, real-time scoring and machine learning for the prediction of data or events in the future. Bell.One™ predictive analytics solutions help organizations to discover patterns and trends in structured and unstructured data so they can go beyond knowing what has happened to anticipating what is likely to happen next.

Key Features

Predictive analytics is based on applying known results to develop (or train) a model that can be used to predict values for new data. Simulation provides results in the form of forecast that represent a probability of the target variable (for example, revenue or income) based on estimated value from a set of input variables.
There are two types of predictive models:
  • Classification models
  • Regression models
Three of the most widely used predictive modeling techniques are decision trees, regression and neural networks:
  • Regression (linear and logistic) is one of the most popular methods in statistics.Regression analysis estimates relationships among variables
  • Decision trees are classification models that partition data into subsets based on categories of input variables
  • Neural networks are powerful and flexible techniques capable of modeling extremely complex relationships. The power lies in their ability to handle nonlinear relationships in data, which is increasingly common as we collect more data.


  • Increased market potential
  • Revenue growth based on pricing policy optimization
  • Improved business performance
  • Risk reduction