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Business operations use cases

Suggest edit Updated on September 21, 2021

Use the Business Operations Environment (BOE) to test the configuration, request changes, review and approve revisions, and deploy the revisions to the Production environment. Learn more about common tasks that the business team does in the BOE environment.

The following diagram represents the BOE workflow:

BOE workflow
Task sequence in the Business Operations Environment.

Value Finder analysis

EnvironmentPortalUser
BOECustomer Decision HubData scientist
  1. A data scientist runs monthly Value Finder simulations with the latest data that they sampled from the Production environment.
  2. The data scientist analyzes the results, looking for value opportunities.
  3. The data scientist identifies groups of under-served customers (without relevant actions) or customers without any actions.
  4. The data scientist raises findings with the team lead, Next-Best-Action designer, and Next-Best-Action analyst in the governance forum.

Scenario Planner analysis

EnvironmentPortalUser
BOE

Customer Decision Hub

Pega 1:1 Operations Manager

Next-Best-Action designer
  1. An Next-Best-Action designer reviews the insights from Value Finder and uses the Scenario Planner to investigate the opportunities to improve customer engagement.
  2. The Next-Best-Action designer runs a Scenario Planner simulation with the latest data that they sampled from the Production environment.
  3. The Next-Best-Action designer analyzes the results to see how close the revenue is to meeting the target. The Scenario Planner results confirm that revenue is not on target and targeting the group of underserved customers could help meet the target.
  4. The Next-Best-Action designer gives feedback to business owners for consideration.

Idea, plan, and build

EnvironmentPortalUser
BOEPega 1:1 Operations ManagerBusiness owner, team lead, Next-Best-Action specialist
  1. The business owner produces and submits an idea to a team lead.
  2. The team lead considers whether the idea is a business-as-usual (BAU) change that the business operations team can process and requests the confirmation of requirements.
  3. The business lead supplies the requirements and the team lead approves them.
  4. The Next-Best-Action specialist completes the build.
  5. The team lead approves the build for deployment. A Pega 1:1 Operations Manager change request is pushed to Revision Manager.

Testing

EnvironmentPortalUser
BOECustomer Decision HubNext-Best-Action specialist, team lead (revision manager)
  1. The Next-Best-Action specialist tests the revision by running the following simulations:
    • A decision funnel simulation to ensure that pass rates of engagement policies are in the right area
    • A distribution simulation to check overall distribution of actions
    • An ethical bias simulation to look for bias in the decision framework
    • A value finder simulation to verify whether value is now targeted correctly
  2. The team lead runs a revision performance test to identify any overhead.
  3. The team lead approves the revision.
  4. The team lead initiates the deployment of the revision.
  5. The revision is merged successfully.

Deployment

EnvironmentPortalUser
OrchestratorDeployment ManagerDeployment manager
BOECustomer Decision HubTeam lead (revision manager)
  1. The deployment manager monitors the progress of the revision through the pipeline.
  2. The revision awaits deployment to the Production environment.
  3. The deployment manager initiates the deployment of the revision to Production.
  4. The revision is deployed to the Production environment successfully.
  5. After the successful deployment of the revision to Production, the team lead marks the revision as completed.

Verification

EnvironmentPortalUser
BOECustomer Decision HubNext-Best-Action Designer
  1. The data scientist runs a Value Finder simulation with the latest data that they sampled from the Production environment.
  2. The data scientist analyzes the results to ensure that the value opportunity that the revision was to address no longer appears.
  3. If the data scientist identifies any new gaps in customer engagement, the change request and revision process repeats.
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