- In the navigation pane of Prediction Studio, click Predictions.
- In the header of the Predictions work area, click New.
- In the Create a prediction window, select Customer Decision Hub, and then click Next.
- In the Prediction name field, enter a name for the prediction.
- Specify what you want to predict.
To predict whether a customer is likely to accept an offer, select the following settings:
- In the Outcome list, select Accept.
- In the Subject list, select Customer.
To predict whether a customer who is likely to click a web banner is also likely to accept the corresponding offer and convert, select the following settings:
- In the Outcome list, select Click + Conversion.
- In the Subject list, select
Note: A multistage prediction requires long-term feedback. You can define how long to wait for the customer to respond to your offer by configuring the response time-out in the prediction settings.
To predict an outcome that does not match any of the available templates, select the following settings:
- In the Outcome list, select Custom.
- In the Outcome name field, enter a name for the outcome, for example, Upsell sensitivity.
- In the Subject list, select the subject of the prediction.
- If you work with branches and want to save the prediction to a specific branch, select Save to branch, and then select a rule set and a branch.
- Review your settings, and perform one of the following actions:
- If you selected Churn as the outcome that you want to predict, click Create, and then go to step 14.
- If you selected other outcomes, start the Prediction wizard by clicking Start wizard.
- In the Select data wizard step, choose one of the
- To create a prediction without historical data, select I do
not have historical data.
In most cases, historical data is not available or necessary. You can continue without it.
- To create a prediction with historical data, select I have
historical data, and then select the data set that contains
your historical data.
Historical data informs the prediction so that it performs better right from the start. To use this option, you need to import your historical 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. For more information, see Importing data into a data set.
- To create a prediction without historical data, select I do not have historical data.
- Click Next.
- In the Prediction configuration wizard step, review the response labels for the prediction, and then click Next.
- 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 choosing predictors.
- Confirm your choice of predictors by clicking Next.
- In the Review prediction wizard step, review the prediction settings, and then complete the model creation process by clicking Create.
- To change the prediction properties at this stage, in the Prediction window, click Configure, and then make your changes.
- Click Save.
Result: The prediction is now available in the Predictions workspace. Depending on the settings that you selected, Prediction Studio created an adaptive model or a scorecard as the basis of the prediction.
- If you are using parameterized predictors, add them to the adaptive model that
is the basis of the prediction.For more information, see Adding parameterized predictors.
Tip: To open the model that is the basis of the prediction, in the Prediction workspace, on the Models tab, click the name of the corresponding model.
- Optional: To customize your prediction, for example, by changing the control group settings or by configuring the response time-out, see Customizing predictions.
- Optional: To replace the model that is the basis of the prediction with a different model, see Replacing models in predictions with MLOps.
Pega Customer Decision Hub