Creating a churn prediction using an ML model

2 Tasks

15 mins

Pega Customer Decision Hub '24.1

Pega Customer Decision Hub '23 Pega Customer Decision Hub 8.8

Visible to: All users

Beginner Pega Customer Decision Hub '24.1 English

Scenario

U+ Bank implements Pega Customer Decision Hub™ to personalize the credit card offer a customer is presented on their website. If a customer is eligible for multiple offers, artificial intelligence (AI) decides which offer to show.

To customers that are likely to leave the bank soon, the bank wants to make a proactive retention offer instead of a credit card offer. The bank has recorded historical churn data for its customer base, which a data scientist used to create a churn model. You create a prediction that is driven by the churn model. This prediction can then be used by a decisioning architect in an engagement strategy.

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

Role	User name	Password
Data scientist	DataScientist	rules

Your assignment consists of the following tasks:

Task 1: Create a new prediction

As a data scientist, create a new prediction to calculate churn risk.

Task 2: Replace the scorecard with the churn model in the new prediction

Replace the placeholder scorecard with the ChurnH2O model.

 

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

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

• Log In

Challenge Walkthrough

Detailed Tasks

1 Create a new prediction

1. On the exercise system landing page, click Launch Pega Infinity™ to log in to Customer Decision Hub.
2. Log in as a data scientist:
   1. In the User name field, enter DataScientist
   2. In the Password field, enter rules.
3. In the navigation pane on the left, click Intelligence > Prediction Studio.
4. In the upper-right corner, click New to create a prediction.
5. Ensure that Customer Decision Hub is selected, and then click Next.
6. In the Prediction name field, enter Predict Churn Risk.
7. In the Outcome field, select Churn.
8. In the Object field, select Customer.
   
9. Click Create.
10. In the upper-right corner, click Save.

2 Replace the scorecard with the churn model in the new prediction

1. Download the ChurnH2O.zip file to your local machine, and then extract the ChurnH2O.mojo file.
2. On the Models tab, in the Churn section, click the More icon for the Predict Churn Risk prediction.
3. Click Replace scorecard.
   
4. In the Introduce challenger model dialog box, select Model, and then click Next.
5. Select Upload model file, and then select the ChurnH2O.mojo model file.
6. Click Next, ensure that Compare models is not selected, and then click Next.
7. In the Candidate model name field, enter ChurnH2O, and then click Add challenger model to add the ChurnH2O model to the prediction.
8. When the status of the ChurnH2O model changes to Challenger (pending review), click the More icon in the ChurnH2O row, and then select Approve challenger model.
9. Ensure that Approve candidate model and replace current active model is selected.
10. Enter appropriate comments, and then click Approve.

Confirm your work

1. Confirm that the ChurnH2O model has replaced the placeholder scorecard as Active in the prediction.
   
2. In the upper-right corner, click Submit for deployment.
3. In the Submit prediction for deployment dialog box, enter appropriate comments, and then click Submit.
   Note: When you create or modify a prediction, an automatic change request is generated in the current revision. Once the Revision Manager deploys the revision, the changes take effect.

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

• Creating predictions v4

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Available in the following mission:

• AI for 1:1 Customer Engagement v4