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Adaptive analytics

Updated on July 5, 2022

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.

ADM models are self-learning which means that they are automatically updated after new responses have been received. The ADM service captures predictor data and responses and can therefore start without any historical information. You can use adaptive decision management to identify propositions that your customers are most likely to accept, improve customer acceptance rates, or predict other customer behavior.

Adaptive models work by recording all customer responses (both positive and negative) and correlating them to different customer details (for example, age, gender, region, and so on). For example, if ten people under 35 years of age accept a particular phone offer, the predicted likelihood that more people under 35 years of age will buy the same phone increases. The likelihood can also go down if a negative response is recorded, from this group. Over time, reliable correlations emerge.

  • Defining an adaptive model

    Predict customer behavior and adjust your marketing strategy by configuring an adaptive model.

  • Importing adaptive models to another environment

    You can import trained Adaptive Decision Manager (ADM) models from your production environment to a simulation environment. Synchronizing both environments is useful when you want to run scenarios in your simulation environment and apply the most up-to-date models. Adaptive models in the production environment are constantly processing data and self-learning. By importing these models to your simulation environment, you ensure that the scenarios that you run yield relevant and accurate results.

  • Adaptive models monitoring

    To monitor all models that are part of an adaptive model, use the Monitor tab of an adaptive model in Prediction Studio. The predictive performance and success rate of individual models provide information that can help business users and strategy designers refine decision strategies and adaptive models.

  • Adaptive Decision Manager data model

    The Adaptive Decision Manager (ADM) database tables are part of the PegaDATA schema. The state of the ADM system is maintained in memory and regularly serialized to a database.

  • Delayed learning of adaptive models

    Delayed learning is a way of updating adaptive models by providing input about customer decisions to the Adaptive Decision Manager (ADM) server. ADM collects data about customer behavior that can be used to predict the next best action for customers.

  • Creating adaptive models at different hierarchy levels

    Adaptive models are self-learning models that you can use in strategies to improve predictions about changing customer interests and needs.

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