Creating a churn prediction using a scorecard

3 Tasks

20 mins

Pega Customer Decision Hub 8.8

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

Visible to: All users

Beginner Pega Customer Decision Hub 8.8 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 keep them satisfied. 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

As a Data Scientist, create a new prediction in Pega Customer Decision Hub™ to calculate churn risk by using a scorecard.

Task 2: Edit the scorecard

To customize the default scorecard in the new prediction, edit the 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 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 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

Task 3: Configure the scorecard Cutoff value

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.

Confirm your work

Verify the scorecard for customers Barbara and Robert.

 

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 Pega CRM suite to log in to Pega 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 of Customer Decision Hub, click Intelligence > Prediction Studio to open the Prediction Studio landing page.
4. In the upper-right corner, click New to create a prediction.
5. Ensure that Customer Decision Hub is the active selection, and then click Next.
6. In the Prediction name field, enter Predict Churn Risk.
7. In the Outcome field, select Churn.
8. In the Subject field, select Customer.
   The following figure shows the completed prediction configuration:
   
9. Click Create.
10. In the upper-right corner, click Save.

2 Edit 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, 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:
   

3 Configure the scorecard Cutoff value

1. On the scorecard rule form, click the Results tab to edit the segmentation.
2. In the Result column, in the first field, enter Loyal.
3. In the second field, enter Churn.
4. In the first row, in the Cutoff value field, enter 228.
   
5. Click Save to save the changes to the scorecard.

Confirm your work

1. In the upper-right corner, click Actions > Run.
2. In the Run window, in the Thread list, select the PredictChurnRisk thread.
3. Select the Apply data transform checkbox.
4. In the Data transform field, enter or select Barbara.
   
5. In the upper-right corner, click Run.
   The following figure shows the execution results of the prediction for Barbara:
   
   Note: The values of the properties populate the execution details after you apply a data transform. Barbara gets a score of 120, so she is not likely to churn.

6. Repeat steps 4 and 5 for Robert.

The following figure shows the execution results of the prediction for Robert

 

Note: Robert gets a score of 280, so he is likely to churn.

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Available in the following mission:

• AI for 1:1 Customer Engagement v2