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Challenge

Using a model score as a predictor

4 Tasks

10 mins

Pega Customer Decision Hub 8.7
Visible to all users
Beginner Pega Customer Decision Hub 8.7 English

Scenario

U+ Bank is implementing cross-selling of its credit cards on the web by using Pega Customer Decision Hub™. To further enhance the predictive power of the adaptive models, you create a parameterized predictor that is the on-the-fly score of a predictive churn model that runs in Customer Decision Hub.

Use the following credentials to log in to the exercise system:

Role User name Password
Decisioning architect DecisioningArchitect rules

Your assignment consists of the following tasks:

Task 1: Add a parameter to the adaptive model

In the Web_Click_Through_Rate model, create a parameterized predictor that calculates the churn risk for a customer.

Task 2: Add a substrategy to the NBA Framework decision strategy

In the WebTreatmentModelImpl strategy, create an external substrategy.

Task 3: Create a decision strategy that references a churn model

Create a decision strategy that references the Churn model, and then set the decision strategy as the external strategy in the substrategy component.

Task 4: Configure the adaptive model component

Configure the adaptive model component with the new parameterized predictor.

Note: A data scientist does not have the right to create a decision strategy in this system. Therefore, you log in as a decisioning architect. If you have completed the previous challenge, Using behavioral data as predictors, you can re-use your exercise system, but some screenshots might look slightly different.

 

Challenge Walkthrough

You must initiate your own Pega instance to complete this Challenge.

Initialization may take up to 5 minutes so please be patient.

Detailed Tasks

1 Add a parameter to the adaptive model

  1. On the exercise system landing page, click Pega CRM suite to log in to Customer Decision Hub.
  2. Log in to Customer Decision Hub as a decisioning architect with User name DecisioningArchitect using Password rules.
  3. In the navigation pane of Customer Decision Hub, click Intelligence > Prediction Studio to open Prediction Studio.
  4. On the Predict Web Propensity tile, click Open prediction to edit the web propensity prediction.
  5. On the Models tab of the prediction, in the Supporting models section, click Web_Click Through_Rate to open the adaptive model rule.
    Supporting models
  6. In the adaptive model rule, click the Predictors tab.
  7. On the Predictors tab, click the Parameters tab, and then click Add parameter.
  8. In the Name field, enter ChurnRisk.
  9. In the Data type list, select Double.
  10. Confirm that the predictor type is Numeric.
    ChurnRisk parameter
  11. In the upper-right corner, click Save to save the new parameter.
  12. In the upper-left corner, click the Back icon to return to the prediction.
    Back to the prediction

2 Add a sub strategy to the NBA Framework decision strategy

  1. On the Models tab, in the Supporting models section, click Web_Click Through_Rate_Customers to open the strategy.
  2. In the upper-right corner, click Check out to check out the strategy for editing.
  3. Minimize the Account area.
    Minimize the Account area
  4. On the canvas, in the Customers area, click + > Sub strategy > External to add a substrategy component to the canvas.
    Add external Sub strategy
  5. On the canvas, connect the components as shown in the following image:
    Strategy with new sub strategy
  6. Right-click the Sub Strategy component, and then select Properties to open the External strategy properties dialog box.
  7. In the External strategy properties dialog box, in the Name field, enter Churn Risk.
  8. On the External strategy tab, click Another page to run the strategy on a defined page.
  9. In the Page field, enter Customer.
  10. Confirm that the Class field auto-populates to UBank-Data-Customer.

3 Create a decision strategy that references a churn model

  1. In the External strategy properties dialog box, in the External strategy field, enter ChurnRisk.
  2. Next to the External strategy field, click the Open icon to open the Strategy Record Configuration landing page.
    External strategy properties
  3. On the Strategy Record Configuration landing page, in the upper-right corner, click Create and open to create the strategy.
    1. On the canvas, right-click, and then select Enable external input to add the input component to the canvas.
    2. On the canvas, right-click, and then select Decision analytics > Predictive model to add a predictive model component to the canvas.
    3. Connect the external input to the predictive model component and the predictive model component to the results, as shown in the following image:
      Add predictive model
    4. Right-click the Predictive Model component, and then select Properties to open the Predictive model properties dialog box.
    5. In the Predictive model properties dialog box, in the Predictive model field, press the down arrow key, and then double-click ChurnPML to select the model and auto-populate the Name field.
    6. Click the Output mapping tab, and then select Add item.
    7. In the Target field, enter ChurnRisk.
    8. In the Source (ChurnPML) list, select Score.
    9. Next to the Target field, click the Open icon to open the Property Record Configuration landing page.
    10. On the Property Record Configuration landing page, in the upper-right corner, click Create and open to edit the property.
      1. In the Property type section, click change.
      2. In the Single Value column, select Double.
      3. In the upper-right corner, click Save.
  4. Close the ChurnRisk property.
  5. Click Submit to close the Predictive model properties dialog box.
  6. In the upper-right corner of the canvas, click Save.
  7. Close the canvas.
  8. Click Submit to close the External strategy properties dialog box.

4 Configure the adaptive model component

  1. On the canvas, right-click the adaptive model component, and then select Properties to open the Adaptive model properties dialog box.
  2. In the Parameterized predictors section, in the ChurnRisk field, enter or select .ChurnRisk.
    ChurnRisk strategy property
  3. Click Submit.
  4. In the upper-right corner, check in the strategy with appropriate comments.
  5. In the lower-left corner, click Back to Pega Prediction Studio.

Confirm your work

  1. On the exercise system landing page, click U+ Bank to launch the U+ Bank website.
    UBank tile
  2. On the U+ Bank website, in the upper-right corner, click Log in to access the site as Troy and display the marketing banner.
  3. In the marketing banner, click Learn more to record a positive response.
  4. In the upper right, click the user image, and then click Log out.
    Troy logout
  5. Repeat step 2 to log in as Barbara.
  6. As Barbara, repeat steps 3-4 to record a positive response, and then log out of the U+ Bank website.
    Tip: Simulating customer interactions triggers the creation of the underlying adaptive models.
  1. In Prediction Studio, in the upper-right corner, click Actions > Refresh.
  2. On the Models tab, in the Supporting models list, confirm that the ChurnPML model is present.
  3. Click Web_Click_Through_Rate to open the adaptive model.
    ChurnPLM is now a supporting model
  4. On the Monitor tab, click Refresh data.
  5. On the Predictors tab, click Predictor name to sort the list of predictors column in descending order.
  6. Confirm that the ChurnRisk parameter is listed and currently inactive in all models.

This Challenge is to practice what you learned in the following Modules:


Available in the following missions:

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