Predictive analytics predict customer behavior, such as the propensity of a customer to take up an offer or to cancel a subscription (churn), or the probability of a customer defaulting on a personal loan. Create predictive models in Prediction Studio by applying its machine learning capabilities or importing PMML models that were built in third-party tools.
You can create the following types of predictive models in Prediction Studio:
- Binary models
- Extended binary modelsThis predictive model type requires a license for extended scoring. Contact your account executive for licensing information.
- Continuous models
- Creating a Pega predictive model
Use customer data to develop powerful and reliable models that can predict customer behavior, such as offer acceptance, churn rate, credit risk, or other types of behavior.
- Importing a predictive model
Import predictive models from third-party tools to predict customer actions. You can import PMML and H2O models.
- Connecting to an external model
You can run your custom artificial intelligence (AI) and machine learning (ML) models externally in third-party machine learning services. This way, you can implement custom predictive models in your decision strategies by connecting to models in the Google AI Platform and Amazon SageMaker machine learning services.
- Configuring a predictive model
After you create a predictive model, configure the model outcome and source data settings to ensure that the predictions are accurate.
- Developing models
The Model development step helps you create models for further analysis. You group predictors based on their behavior and create models to compare their key characteristics.
- Analyzing models
In the Model analysis step you can compare and view scores of one or more predictive models in a graphical representation, analyze predictive models' score distribution, and compare the classification of scores of one or more predictive models.
- Selecting a model for deployment
Export your generated predictive models into instances of the Predictive Model rule and use them in strategies.
- Predictive models monitoring
Monitor the performance of your predictive models to detect when they stop making accurate predictions, and to re-create or adjust the models for better business results, such as higher accept rates or decreased customer churn.
- Predictive models management
Learn about the common maintenance activities for predictive models in your application.
- Types of predictive models
Predictive models are optimized to predict different types of outcome.
- Prediction Studio overview
Prediction Studio is an authoring environment in which you can control the life cycle of AI and machine-learning models (such as model building, monitoring, and update). From Prediction Studio, you can also manage additional resources, such as data sets, taxonomies, and sentiment lexicons.
- Enabling outcome inferencing
When enabled, the outcome inferencing feature allows you to support Prediction Studio projects with additional data analysis steps that help you to handle unknown behavior.