Pega Scenario Planner is the best tool for estimating the business value of a next-best-action run with the selected configuration:

Purpose of Scenario Planner :

Step 1 : Pega Scenario Planner is designed to simulate and analyze the potential impact of business changes before they are implemented.

Step 2 : It provides insights into how changes in decision strategies, engagement policies, or other configurations affect business outcomes.

Simulation Setup :

Step 1 : Define the scenarios you want to test, including changes to the decision strategies or engagement policies.

Step 2 : Configure the simulation to reflect the real-world conditions and customer data relevant to the business case.

Analyzing Results :

Step 1 : Run the simulation and review the results to understand the estimated business value generated by the next-best-action run.

Step 2 : Analyze key performance indicators (KPIs) such as acceptance rates, response rates, and financial metrics to evaluate the impact of the changes.

Example :

U+ Bank can use Pega Scenario Planner to simulate the presentation of Credit Card offers on their self-service portals, allowing them to estimate the business value generated by these offers based on the selected configuration.

References :

Pega-Customer-Decision-Hub-User-Guide-85.pdf: " Increasing your understanding of the effect of your business changes with the Scenario Planner " section.

 Addressing Test Failures: When persona tests fail after adding new engagement policy conditions, the appropriate step is to analyze the test case failures, update the tests as necessary, and re-execute them until all tests pass. This ensures that the changes made are correctly reflected in the tests and the system functions as expected.

 Implementation: Review the failing tests to understand why they failed, adjust the conditions or the expected results, and run the tests again to validate the changes.

Identify the Requirement : U+ Bank wants to offer credit cards only to customers with specific risk segments and also ensure that the age of the customer is greater than 25 years. This involves setting eligibility criteria for credit card offers.

Understand Engagement Policies : Engagement policies in Pega CDH are used to define rules that govern when specific actions are relevant for a customer. This includes eligibility, applicability, and suitability conditions.

Access Next-Best-Action Designer : To implement these criteria, navigate to the Next-Best-Action Designer within Pega Customer Decision Hub.

Engagement Policy Tab :

Eligibility Criteria : Set the criteria to include customers with an age greater than 25 years.

Define the other conditions based on Credit Score and Average Balance to segment customers into Good, Fair, Poor, and Very Poor risk categories.

According to the Pega Customer Decision Hub User Guide, the engagement policy tab allows users to define when specific actions or groups of actions are appropriate for customers (Reference: Pega-Customer-Decision-Hub-User-Guide-85.pdf, Chapter on " Understanding Next-Best-Action Designer engagement policies " ).

Implementing the Criteria :

Navigate to the Engagement Policy tab : This is within the Next-Best-Action Designer.

Add a New Policy : Create a new engagement policy where you define the eligibility criteria for the credit card offer.

Set Age Condition : Ensure the age condition (Age > 25) is part of the eligibility criteria.

Validation :

Ensure the conditions are tested thoroughly to verify that the credit card offers are only presented to customers meeting the set criteria.

Additional Resources :

For detailed steps on configuring engagement policies, refer to the Pega documentation on " Configuring engagement policies for actions " (Reference: Pega-Customer-Decision-Hub-User-Guide-85.pdf).

Conclusion : The best practice to ensure that credit cards are offered only to customers with age greater than 25 is to use the Engagement Policy tab in the Next-Best-Action Designer, where you can set the eligibility conditions appropriately.

When MyCo updates their scorecard to include a new property, such as customer annual income, in the calculation of the postpaid credit limit, the decision strategy itself does not need to be changed to reflect the updated scorecard. This is because the scorecard rule already encapsulates the logic for calculating the credit limit, including any new properties added.

Scorecard Rule Update :

The scorecard rule in Pega is responsible for computing scores based on various properties.

If the scorecard rule is updated to include a new property, the decision strategy referencing this scorecard automatically uses the updated scorecard without requiring any additional changes to the strategy itself.

Decision Strategy :

The decision strategy references the scorecard rule, and as long as the reference remains unchanged, any updates to the scorecard are automatically applied.

There is no need to remap properties, add proposition filters, or set new properties within the strategy.

Implementation :

Ensure that the scorecard rule is correctly updated and tested to include the new property.

The decision strategy continues to use the updated scorecard without any modifications.

Next-Best-Action in Pega Customer Decision Hub is primarily applied to customer engagement and decisioning scenarios:

Purpose of Next-Best-Action :

Step 1 : Next-Best-Action aims to provide personalized recommendations and decisions that are most relevant to the customer at any given moment.

Step 2 : It uses predictive and adaptive analytics to determine the most appropriate actions based on customer data and business rules.

Application in Loan Decisions :

Step 1 : In the context of determining if a borrower gets a loan, Next-Best-Action evaluates various factors such as creditworthiness, loan history, and current financial status.

Step 2 : It leverages AI models to assess the risk and likelihood of repayment, ensuring that loan offers are extended to eligible and suitable customers.

Implementation Steps :

Step 1 : Set up decision strategies in Pega Customer Decision Hub to include criteria for loan eligibility, such as credit scores and income levels.

Step 2 : Configure predictive models to analyze customer data and predict loan approval probabilities.

Step 3 : Use engagement policies to ensure that the loan offers are aligned with business objectives and regulatory requirements.

Benefits :

Applying Next-Best-Action to loan decisions helps in making data-driven and objective decisions, reducing the risk of defaults and improving customer satisfaction by offering relevant financial products.

References :

Pega-Customer-Decision-Hub-User-Guide-85.pdf: " Understanding Next-Best-Action Designer basics " section.

Pega documentation on " Decision management and AI in financial services " .