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Challenge

Testing engagement policy conditions using an audience simulation

4 Tasks

15 mins

Visible to all users
Beginner Pega Customer Decision Hub 8.7 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: Prepare data set for simulation run

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

Note: The SampledCustomers_Inbound is not available in a persisted store. To initialize the customer data, first run the PrepareSimulationData data flow.
If you already run the PrepareSimulationData in this exercise system as part of a previous challenge, you do not need perform this task.

Task 3: 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 4: 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?

 

Challenge Walkthrough

You must initiate your own Pega instance to complete this Challenge.

Initialization may take up to 5 minutes so please be patient.

Detailed Tasks

1 Check the configurations

  1. On the exercise system landing page, click Pega CRM suite to log in to Customer Decision Hub.
  2. Log in as the Next-Best-Action Designer with User name NBADesigner and Password rules.
  3. In the navigation pane of Customer Decision Hub, click Next-Best-Action > Designer to open Next-Best-Action Designer.
    NBA Designer
  4. In the Grow section, click Credit cards > Customer actions to see the configurations that present customers with relevant actions.
    NBA Designer tabs

2 Prepare data set for simulation run

  1. In the navigation pane of Pega Customer Decision Hub, click Data > Data Flows to view the list of data flows.
    Data Flows
  2. In the list of data flows, search for and then open the PrepareSimulationData 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.
  1. Click Actions > Run to open the data flow work item.
    Run a data flow
  2. Click Submit.
  3. Click Start to process the data, and then wait for the processing to complete.
    Note: Notice that the prospect data is populated once the test run is complete.
  1. Close the data flow 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, in the Grow category, click Credit cards view the engagement policies.
    Business structure
  3. Click Actions > Audience simulations.
    Audience simulation menu
  4. Click Create simulation to create a new audience simulation.
    Create an 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.
    3. In the Next-Best-Action scope section, select Engagement policies only.
      Audience simulation creation window
  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.
      NBA level audience simulation results
      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.
  1. Question: How many unique customers qualify for each action?
    Per action audience simulation results
    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.
  1. In the Actions section, open a credit card action (for example, click the Premier Rewards card).
    Open an action
  2. Click the Engagement policy tab.
  3. Click Actions > Audience simulation.
    Audience simulation for action
  4. In the Simulation list, select AudienceSimulation_E (S-#).
    Select a simulation run
  5. Inspect how the customers are filtered at the action level for every engagement policy condition.
    Action level eligibility results
     
    Action level applicability results
     
    Action level suitability results

4 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.
      Audience simulation with arbitration enabled
    4. 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.
    NBA level audience simulation results
  7. Question: How many unique customers qualify for each action?
    Per action audience simulation results
    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.
  1. In the Actions section, open a credit card action (for example, click the Premier Rewards card).
    Open an action
  2. Click the Engagement policy tab.
  3. Click Actions > Audience simulation.
  4. In the Simulation list, select AudienceSimulation_A (S-#####).
    Select a simulation run
  5. For each engagement policy condition, inspect how the customers are filtered at the action level.
    Action level eligibility results
     
    Action level applicability results
     
    Action level suitability results
    Note: The Monte Carlo data set generates a mock data set. As a result, different simulation runs have different results.
  1. On the right, in the Show population that passed as list, select Percentages to see the filtration effect in percentages.
    Simulation results in

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


Available in the following mission:

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