Creating a churn prediction using an ML model

2 Tâches

15 mins

Pega Customer Decision Hub '24.1

Pega Customer Decision Hub '25 Pega Customer Decision Hub '24.2 Pega Customer Decision Hub '23 Pega Customer Decision Hub 8.8

Visible par : All users Applies to: Pega Customer Decision Hub '24.1

Débutant

Anglais

Scénario

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.

 

Vous devez initier votre votre propre instance Pega pour compléter ce Défi.

L'initialisation peut prendre jusqu'à 5 minutes, donc soyez patient.

• Se connecter

Présentation du défi

Détail des tâches

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.

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Ce défi vise à appliquer ce que vous avez appris dans le Module suivant :

• Creating predictions v4

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Disponible dans la mission suivante :

• AI for 1:1 Customer Engagement v4