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Developing and managing models

Updated on July 5, 2022

Configure and manage AI capabilities of Pega Platform to predict customer behavior and perform text analysis. Enhance the relevance of decisions by using adaptive models that are self-learning. Incorporate predictive analytics into every process and every interaction with your customers. Analyze texts from various sources, such as e-mail, chat channels, social media, and so on.

  • Adaptive analytics

    Adaptive Decision Manager (ADM) uses self-learning models to predict customer behavior. Adaptive models are used in decision strategies to increase the relevance of decisions.

  • Predictive analytics

    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.

  • Managing data

    Create and manage data sets, Interaction History summaries, and other resources. Make sure that you identify the data that correlates to your business use case and that is aligned with the use problem that you want to solve.

  • Model management

    On the Model Management landing page, you can manage adaptive models that were run and predictive models with responses. You can view the performance of individual models and the number of their responses, or perform various maintenance activities, such as clearing, deleting, and updating models.

  • Best practices for choosing predictors

    When you create an adaptive or predictive model, the input fields that you select as predictor data play a crucial role in the predictive performance of that model. Some data types, such as dates and text, might require preprocessing. Follow best practices when you select predictors and choose data types for adaptive and predictive analytics.

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