Testing engagement policy conditions using an audience simulation

Archived

4 Tasks

15 mins

Pega Customer Decision Hub 8.5

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

Visible to: All users

Beginner

Pega Customer Decision Hub 8.5

English

Scenario

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

They want 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
Decisioning Analyst	CDHAnalyst	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 customers with relevant offers.

Task 2: Prepare data set for simulation run

Run the PrepareSampledCustomers_REF data flow to prepare the data set for the simulation run.

Task 3: Create and execute an engagement policy audience simulation

Create and execute an audience simulation. Use the Sampled Customers 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 4: Create and execute an arbitration audience simulation

Create and execute an audience simulation. Use the Sampled Customers data set as the audience and engagement policy and 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?

Challenge Walkthrough

Detailed Tasks

1 Check the configurations

1. Log in as Decisioning Analyst with username CDHAnalyst and password rules.
2. In the navigation pane of Pega Customer Decision Hub, click Next-Best-Action > Designer.
   
3. Navigate through the tabs to see the configurations made to present customers with relevant offers.
   

2 Prepare data set for simulation run

1. In the navigation pane of Pega Customer Decision Hub, click Data > Data Flows.
   
2. Search for and then open the PrepareSampledCustomers_REF data flow to prepare the data set used for simulations. This data is based on a Monte Carlo dataset, which is generated.
   Note: The Monte Carlo data set generates a mock data set. As a result, different simulation runs have different results.

3. Click Actions > Run.
   Note: Notice that the prospect data is populated once the test run is complete.

2. Close the data flow Test run window.

3 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, under Sales, 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. Audience: SampledCustomers
   2. Next-Best-Action scope: Engagement policies only
   3. Simulation ID prefix: AudienceSimulation_E

6. Click Run to execute the simulation.
   

Question: How many customers pass through the eligibility, applicability, and suitability conditions at the group-level and the action-level?

7. Inspect how the customers are filtered at Next Best Action Designer level for every engagement policy condition.

Tip: 980 customers pass through the eligibility conditions at group and action levels. As the applicability condition does not filter out customers, 980 passes through the group and action levels. There are no group-level suitability conditions defined and hence 733 customers pass through the action-level suitability conditions.

Question: How many unique customers qualify for each action?

Tip: When a simulation is run with only engagement policy conditions as the scope, the number of customers who qualify for an action are 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 Desinger.

Note: The Monte Carlo data set generates a mock data set. As a result, different simulation runs have different results.

8. In the Offers 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_E-1.
    
12. Inspect how the customers are filtered at action level for every engagement policy condition.
    
    
    

4 Create and execute an arbitration audience simulation

 

1. In Next Best Action Designer, click the Engagement policy tab.
2. 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. Audience: SampledCustomers
   2. Next-Best-Action scope: Engagement policies and arbitration
   3. Simulation ID prefix: AudienceSimulation_A
   

   Question: How many unique customers qualify for each action?

   

6. Click Run to execute the simulation.
7. Inspect how the customers are filtered at the Next Best Action Designer level for every engagement policy condition.
   

Tip: When an audience simulation is run with engagement policy and arbitration as the scope, the number of customers who qualify for an action are unique, as only the top offer is considered. 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 Offers 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-1.
    
12. Inspect how the customers are filtered at action level for every engagement policy condition.
    
    
    

Note: The Monte Carlo data set generates a mock data set. Therefore, different simulation runs will have different results.

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

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Available in the following mission:

• Cross-Sell on the Web Extended v2