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Predicting customer behavior and business events


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Better address your customers' needs by predicting customer behavior and business events with predictions. For example, you can determine the likelihood of customer churn, or chances of successful case completion.

With Pega Platform, you can predict events in your business activity by creating predictions in Prediction Studio. To create a prediction, you answer several questions about what you want to predict. Based on your answers, Prediction Studio creates a self-learning adaptive model that is the basis of the prediction. You can then include the prediction in your decision strategy, to help you better adjust to your customers' needs and achieve your business goals at the same time.

For example, you can create a prediction that calculates whether a customer is likely to accept an offer, and then add the prediction to a next-best-action strategy. The next-best-action strategy prepares several propositions for a customer, and then selects the one that the customer is most likely to accept.

At any time, you can replace a model in a prediction with a different model, for example, a predictive model that you created in a third-party machine learning platform, as well as a scorecard or a field in the data model that represents a score. As a data scientist, you can add and approve the new model in a non-production environment, and start a deployment process to migrate the new model to production.

  • Creating predictions

    Create predictions to predict customer behavior, such as the probability of a customer accepting an offer. You can then increase the accuracy of your decisions by including predictions in your decision strategy.

  • Customizing predictions

    Customize the predictions that you created in Prediction Studio. For example, define criteria for the control group to measure the accuracy of your prediction or to specify the response time-out for your offers.

  • Monitoring predictions

    Analyze how successful your predictions are in predicting the outcomes that bring value to your business. Gain insights by reviewing performance charts for predictions and the models that drive them.

  • Updating active models in predictions

    As a data scientist, you can approve changes to models that are used in predictions for deployment to the production environment. You can change models independently or by responding to a Prediction Studio notification that a prediction does not generate enough lift.

  • Updating active models in predictions through API

    You can create and deploy models directly from your modeling tool to Pega Platform, by using a scripting language of your choice and the V2 Prediction API endpoints. The API provides you with options to remotely add a model to your application, review (approve or reject) a model update, and retrieve the status of a model update.

  • Understanding the model update process

    The model update workflow is a standardized process that data scientists can use to replace active models in predictions with other models, scorecards, or fields that represent scores, and to deploy the new models to the production environment.

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