Leveraging predictive models

Archived

3 Tasks

25 mins

Pega Customer Decision Hub 8.5 Visible to: All users

Beginner Pega Customer Decision Hub 8.5 English

Scenario

U+ Bank uses AI to determine which credit card offers to show to customers on its website. The bank wants to reduce the number of clients that leave the bank by leveraging an existing analytics model that predicts which customers are likely to go. This model is based on historical data of the interactions of the bank with its customers. The bank wants to show the potential churners a retention offer instead of a credit card offer.

As a data scientist, your task is to create an engagement strategy that leverages the churn model. Next, you configure the Next-Best-Action Designer to decide between a credit card offer to a customer based on the outcome of the churn model.

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

Role	User name	Password
Decisioning Analyst	CDHAnalyst	rules

Your assignment consists of the following tasks:

Task 1: Edit the RetentionStrategy to implement the new applicability rule

In Pega Customer Decision Hub™, modify the skeleton RetentionStrategy to accommodate the following applicability rule: the retention offer ExtraMiles5K is applicable for customers who are high risk to churn.

Task 2: Configure the Next-Best-Action Designer

Configure the engagement policies with the newly created engagement strategy as group-level applicability rules. Ensure that the following rules are implemented:

1. The retention offer ExtraMiles5K is applicable for customers who are at high risk of churn.
2. Sales offers are inapplicable for customers who are at high risk of churn.

Task 3: Confirm your work

On the U+ Bank website, verify that customer Troy, predicted to churn soon, receives a retention offer.  Verify that customer Barbara, expected to remain loyal for now, receives a credit card offer.

Challenge Walkthrough

Detailed Tasks

1 Edit the RetentionStrategy to implement the applicability rule

1. Log in as a Decisioning Analyst with username CDHAnalyst and password rules.
2. In the navigation pane on the left, click Intelligence > Strategies.
3. Search for and double-click RetentionStrategy.
4. Check out the strategy.
5. Right-click the canvas, and then select Enable external input.
6. Right-click the canvas, and from the Decision analytics category, add a Predictive Model component.
7. Right-click the component, and then select Properties.
8. Select the predictive ChurnH2O model to auto populate the Name field.
   
9. From the Arbitration category, add a Filter component.
10. Right-click the Filter component, and then select Properties.
    1. In the Name field, enter High Churn Risk.
    2. In the Filter condition field, enter ChurnH2O.pxSegment=="churned". Do not copy-paste.
       
    3. Click Submit.
11. Connect the External Input to the Filter component and the Filter component to the Results. The strategy resembles the following image.
    
12. In the upper right, save the strategy configuration.
13. On the right, click the arrow to open the Test run panel.
14. In the Setting section, enter or select the following information:
    1. Data transform: Barbara
    2. For external inputs use strategy: RetentionOffers
15. Click Save & Run.
16. Ensure that the Results component does not contain a retention offer.
17. On the canvas, click the Predictive Model and confirm that the segment for Barbara is loyal.
    
     
18. Repeat steps 14 and15 for Troy and confirm that the segment for Troy is churned.
    
19. Ensure that the Results component contains a retention offer for Troy.
    
     
20. Check in the decision strategy.

2 Configure the Next-Best-Action Designer

1. In the navigation pane on the left, click Next-Best-Action > Designer.
2. Click the Engagement policy tab.
3. In the Business structure section, click the ExtraMiles group.
4. Click Edit.
5. In the Applicability section of the engagement policy, click the Add icon to add an applicability condition.
   1. In the first list, ensure that Customer is selected.
   2. In the second list, in the Strategy section, click RetentionStrategy.
   3. In the third list, ensure that has results for is selected.
   4. In the last list, select the High Churn Risk strategy component.
      
6. Save the configuration of the ExtraMiles group.
7. In the Business structure section, select the CreditCards group.
8. Click Edit.
9. In the Applicability section of the engagement policy, click the Add icon to add a new applicability condition.
   
   1. In the first list, ensure that Customer is selected.
   2. In the second list, in the Strategy section, click RetentionStrategy.
   3. In the third list, select doesn’t have results for.
   4. In the last list, select the High Churn Risk strategy component.
      
10. Save the configuration of the CreditCards group.
11. In Next-Best-Action Designer, click Channels to configure the website integration.
12. Click Edit.
13. In the Triggers area, in the Real-time containers section, configure the Business structure level to All issues / All groups.
    
14. Save the Channels configuration.

3 Confirm your work

1. On the Customer Engagement & Analytics landing page, click U+ Bank to open the website.
   
2. On the main page of the website, in the upper right, click Log in to log in as a customer.
3. Log in as Troy, who has a high churn risk, and verify that a retention offer is displayed.
   
   Note: Allow some time for the offer to display. Subsequent offers are displayed immediately.

4. In the upper right, click on the profile icon and log out.
5. On the U+ Bank website, log in as Barbara, who is expected to remain loyal, and verify that a credit card offer is displayed.
   

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Available in the following missions:

• Data Scientist v2
• Cross-Sell on the Web Extended v2