Testing engagement policy conditions using an audience simulation

3 Tasks

15 mins

Pega Customer Decision Hub '23

Pega Customer Decision Hub '24.2 Pega Customer Decision Hub '24.1 Pega Customer Decision Hub 8.8 Pega Customer Decision Hub 8.7 Pega Customer Decision Hub 8.6 (English) Pega Customer Decision Hub 8.6 (Français) Pega Customer Decision Hub 8.5

Visible to: All users

Beginner

Pega Customer Decision Hub '23

English

Scenario

U+ Bank is currently cross-selling on the web by showing various credit cards to its customers.

They want to use Pega Customer Decision Hub™ to run an audience simulation to check how many potential actions are filtered out by each component of the policy and discover if a particular criterion is too broad or too narrow for their requirements.

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

Role	User name	Password
Next-Best-Action Designer	NBADesigner	rules

Your assignment consists of the following tasks:

Task 1: Check the configurations

Check the Next-Best-Action Designer configurations that U+ Bank currently uses to present relevant actions to customers.

Task 2: Create and execute an engagement policy audience simulation

Create and execute an audience simulation. Use the SampledCustomers Inbound data set as the audience and only engagement policy as the simulation scope. Inspect the result of the executed simulation by answering the following questions:

• How many customers pass through the eligibility, applicability, and suitability conditions at the group level and the action level?
• How many unique customers qualify for each action?

Task 3: Create and execute an arbitration audience simulation

Create and execute an audience simulation. Use the SampledCustomers_Inbound data set as the audience and engagement policy, and use arbitration as the simulation scope. Inspect the result of the executed simulation by answering the following question.

• How many unique customers qualify for each action?

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 Check the configurations

1. On the exercise system landing page, click Pega Infinity™ to log in to Customer Decision Hub.
2. Log in to Customer Decision Hub as a Next-Best-Action Designer:
   1. In the User name field, enter NBADesigner.
   2. In the Password field, enter rules.
3. In the navigation pane of Customer Decision Hub, click Next-Best-Action > Designer to open Next-Best-Action Designer.
   
4. In the Next-Best-Action Designer, click Engagement policy.
5. In the Grow section, click Credit cards > Customer actions to see the configurations that present customers with relevant actions.
   

2 Create and execute an engagement policy audience simulation

1. In Next Best Action Designer, click the Engagement policy tab.
2. In the Business structure section, in the Grow category, click Credit cards view the engagement policies.
   
3. Click Actions > Audience simulations.
   
4. Click Create simulation to create a new audience simulation.
   
5. In the Create simulation dialog box, configure the following information.
   1. In the Audience list, select SampledCustomers_Inbound.
   2. In the Name field, enter AudienceSimulation_E and click Submit.
   3. In the Next-Best-Action scope section, ensure Engagement policies only is selected.
      
       
6. Click Run to execute the simulation.
7. Question: How many customers pass through the eligibility, applicability, and suitability conditions at the group level and the action level?
   1. Click Customer actions.
   2. In each engagement policy condition, inspect how the customers are filtered at the Next-Best-Action Designer level.
      
      Tip: 805 customers pass through the eligibility, applicability, and suitability conditions at the group level. The eligibility condition is passed by 695 customers. The applicability condition filters out more customers, so 623 customers pass through the action level. 379 customers pass the group- and action-level suitability conditions.

8. Question: How many unique customers qualify for each action?
   
   Tip: When you run a simulation with only engagement policy conditions as the scope, the number of customers who qualify for an action is not unique. All customers who qualify for an action are counted. As a result, the total of customers per action does not tally with the Total unique customers count shown in the Next-Best-Action Designer.
   Note: The Monte Carlo data set generates a mock data set. As a result, different simulation runs have different results.

9. In the Actions section, open a credit card action (for example, click the Premier Rewards card).
   
10. Click the Engagement policy tab.
11. Click Actions > Audience simulation.
    
12. In the Simulation list, select AudienceSimulation_E (S-#).
    
13. Inspect how the customers are filtered at the action level for every engagement policy condition.
    
     
    
     
    

3 Create and execute an arbitration audience simulation

1. In Next-Best-Action Designer, click the Engagement policy tab.
2. In the Business structure section, in the Grow category, click CreditCards.
3. Click Actions > Audience simulations.
4. Click Create simulation to create a new audience simulation.
5. In the Create simulation dialog box, configure the following information:
   1. In the Audience list, select SampledCustomers_Inbound.
   2. In the Name field, enter AudienceSimulation_A.
   3. In the Next-Best-Action scope section, select Engagement policies and arbitration.
   4. Select the Include a second-pass simulation without business levers (for Scenario Planner) check box.
      
       
   5. Click Run to execute the simulation.
6. In each engagement policy condition, inspect how the customers are filtered at the Next-Best-Action Designer level.
   
7. Question: How many unique customers qualify for each action?
   
   Tip: When you run an audience simulation with engagement policy and arbitration as the scope, the number of customers who qualify for an action is unique, as only the top offer is considered by the system. As a result, the total number of unique qualifying customers at the next-best-action level adds up to the total number of unique qualifying customers per action.

8. In the Actions section, open a credit card action (for example, click the Premier Rewards card).
   
9. Click the Engagement policy tab.
10. Click Actions > Audience simulation.
11. In the Simulation list, select AudienceSimulation_A (S-#####).
    
12. For each engagement policy condition, inspect how the customers are filtered at the action level.
    
     
    
     
    
    Note: The Monte Carlo data set generates a mock data set. As a result, different simulation runs have different results.

13. On the right, in the Show population that passed as list, select Percentages to see the filtration effect in percentages.
    

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

• Testing engagement policy conditions using audience simulation v3

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Available in the following mission:

• 1:1 Customer Engagement Optimization v4