Creating a churn prediction using a scorecard

2 Tasks

20 mins

Pega Customer Decision Hub '25

Pega Customer Decision Hub '24.2 Pega Customer Decision Hub '24.1 Pega Customer Decision Hub '23 Pega Customer Decision Hub 8.8

Visible to: All users Applies to: Pega Customer Decision Hub '25

Beginner

English

Scenario

U+ Bank wants to predict and avoid potential customer churn before it happens. When customers leave a bank, the result is costly in terms of lost revenue and acquiring new customers. By detecting customers who might be at risk of churning, the bank can take proactive measures, such as providing incentives or personalized marketing offers, to satisfy them. To allow the bank to identify vulnerable customers, as a Data Scientist, you must create a Prediction based on a scorecard that evaluates churn risk and verify the accuracy of the scorecard for Barbara and Robert.

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

Create a new Prediction in Pega Customer Decision Hub™ to calculate churn risk.

Task 2: Configure the scorecard

To create a scorecard that predicts churn, edit the placeholder scorecard to include the conditions and scores of four customer fields:

1. CreditScore:
   Split the values for CreditScore into five ranges, and then assign scores to each range, as shown in the following table:

   Condition	Score
   <=200	65
   <=400	50
   <=700	35
   <=900	15
   Otherwise	5

2. Age:
   Split the values for Age into five ranges, and then assign a weight of 2 and scores to each range, as shown in the following table:

   Condition	Score
   <=21	90
   <=25	80
   <=30	50
   <=55	20
   Otherwise	10

3. RelationshipLengthDays:
   Split the values for RelationshipLengthDays into four ranges, and then assign a weight of 2 and scores to the ranges, as shown in the following table:

   Condition	Score
   <=180	75
   <=360	60
   <=720	30
   Otherwise	10

4. OwnershipStatus:
   Split the values for OwnershipStatus into three ranges, and then assign scores to the ranges, as shown in the following table:

   Condition	Score
   Rent	25
   Owner	5
   Otherwise	35

Configure the scorecard to output Churn if the churn risk score is equal to or greater than 228 and Loyal if the score is lower than 228.

 

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. In the navigation pane of Customer Decision Hub, click Intelligence > Prediction Studio to open the Prediction Studio landing page.
2. In the upper-right corner, click New to create a Prediction.
3. Ensure that Customer Decision Hub is the active selection, and then click Next.
4. In the Prediction name field, enter Predict Churn Risk.
5. In the Outcome field, select Churn.
6. In the Subject field, select Customer.
   The following figure shows the completed Prediction configuration:
   
7. Click Create.

2 Configure the scorecard

1. On the Models tab, in the Churn section, click Predict Churn Risk to open the default scorecard.
   
2. In the Predictor expression field, enter or select .CreditScore, and then define the conditions with the values, as shown in the following figure:
   
3. Click the Add icon to add another predictor expression.
4. In the Predictor expression field, enter .Age, and then define the conditions with the values, as shown in the following figure:
   
   Note: Notice the weight value of 2. The weight value indicates the relative importance of a particular predictor in the outcome of the model.

5. Click the Add icon to add another predictor expression.
6. In the Predictor expression field, enter or select .RelationshipLengthDays, and then define the conditions with the values, including the weight, as shown in the following figure:
   
7. Click the Add icon to add another predictor expression.
8. In the Predictor expression field, enter or select .OwnershipStatus, and then define the conditions with the values, as shown in the following figure:
   
9. On the scorecard rule form, click the Results tab to edit the segmentation.
10. In the Loyal row, in the Cutoff value field, enter 228.
    
11. Click Save to save the changes to the scorecard.
12. Click Actions > Run to test the scorecard.
13. Enter the test imputs:
    1. .CreditScore: 300
    2. .Age: 45
    3. .RelationshipLengthDays: 230
    4. .OwnershipStatus: Rent
14. Click Run, and inspect the execution details.
    
15. Click Run, and inspect the execution details.
16. Close the Run windows, and return to the prediction.
    

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

• Creating predictions v6

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Available in the following mission:

• AI for 1:1 Customer Engagement v6