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Creating predictions with historical data

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Anticipate business events and customer behavior by creating predictions that learn based on the outcomes of previous and incoming customer interactions.

Import your historical data into a data set. For more information, see Importing data into a data set.
Ensure that the data set contains a pyOutcome property for the outcome of each customer interaction, as well as properties that describe the context of each interaction, for example, pyIssue, pyGroup, pyName, pyDirection, and pyChannel.
  1. In the navigation pane of Prediction Studio, click Predictions.

  2. In the header of the Predictions work area, click New.

  3. In the New prediction window, specify the subject and objective of the prediction.

    To predict whether a customer is likely to accept an offer, select the following settings:
    1. In the Subject of the prediction list, select Customer.
    2. In the The objective is to predict list, select Acceptance.
  4. Start the Prediction wizard by clicking Start.

  5. In the Select data wizard step, click I have historical data, and then select the data set that you create in the Before you begin section.

  6. Confirm your settings by clicking Next.

  7. In the Define outcomes wizard step, specify the possible outcomes of the prediction by clicking the Properties icon.

    To predict whether a customer is likely to accept an offer, specify the followingoutcomes:
    1. In the Predict the likelihood to field, enter Accept.
    2. In the With alternate outcome field, enter Reject.
    Ensure that the outcomes match the values of the pyOutcome fields in the data set that you selected in step 5.
  8. Confirm the outcomes and choice of historical data by clicking Next.

  9. In the Select predictors wizard step, select the fields that you want to use as input for the prediction.

    To increase the accuracy of your prediction, select a wide range of fields to use as predictors. Do not include fields that are not suitable as predictors, for example, the Identifier and Date Time fields. For more information, see Best practices for adaptive and predictive model predictors.
  10. Confirm your choice of predictors by clicking Next.

    When you create a prediction, Prediction Studio creates an adaptive model as the basis of the prediction. For more information, see Adaptive analytics.
  11. Optional:

    To change the name of the adaptive model, in the Review model wizard step, click the Edit icon, and then enter a model name, for example, Predict offer acceptance.

  12. Create the prediction by clicking Create.

  13. Review the prediction configuration, and then click Save.

  14. Optional:

    To review the adaptive model that is the basis of the prediction, on the Outcome definition tab, click Open model.

The prediction is now available in the Predictions workspace.
Include the prediction in your application, for example, as part of a decision strategy.

For more information, see Defining a Prediction shape.

  • Anticipating customer behavior and business events by using predictions

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

  • 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.

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