Pegasystems PEGAPCDC87V1 Exam Dumps

Propensity Increase: In Pega Customer Decision Hub, propensity indicates the likelihood of a customer responding positively to an offer. A propensity value of 0.83 signifies a high likelihood of customers showing interest in the Gold credit card offer.

Customer Behavior Analysis: The increase to 0.83 suggests that similar customers have demonstrated interest, such as clicking on the offer or engaging with it in some way.

Understanding the Requirement:

U+ Bank wants to test the engagement policy conditions to ensure they are neither too broad nor too narrow.

Audience Simulation:

Audience simulation tests the engagement policy conditions by simulating how many customers would receive each action based on the current policy rules.

It helps identify if the policies are effectively filtering the intended customers.

Running the Simulation:

Configure the simulation to include the engagement policy conditions.

Analyze the results to see the percentage of the audience that meets each criterion and receives the actions.

Steps to Run Audience Simulation:

Log in to the Pega Customer Decision Hub portal.

Navigate to the Engagement policy tab.

Select Actions -> Audience simulation.

Create or select a simulation and analyze the results.

Verification from Pega Documentation:

The Pega Customer Decision Hub User Guide outlines the use of audience simulations to test and refine engagement policies.

Introduction to Group By and Filter Components:

The Group By component groups data based on specified properties, allowing aggregation of data points.

The Filter component is used to include or exclude actions based on specific criteria.

Dotted Line Relationship:

In Pega's visual strategy canvas, a dotted line between two components indicates a reference relationship rather than a direct data flow.

When a dotted line connects a Group By component to a Filter component, it signifies that the Filter component is using a property defined or aggregated in the Group By component.

Functionality Explanation:

For example, if the Group By component aggregates customer data by region, the Filter component can then use this aggregated data to filter actions for specific regions.

This relationship ensures that the filtering process considers the aggregated data, making the decision strategy more dynamic and contextually aware.

Verification from Pega Documentation:

As outlined in Pega's documentation, a dotted line signifies that the Filter component references a property from the Group By component, enabling more complex and accurate decision-making processes.

Finalization of Action Details:

Finalization ensures that all data from a specific run is written to a file or database.

This step is crucial for preparing the data to be shared with third parties.

Single Run Data:

Finalizing at the end of each run means the file will only contain data from that particular run.

This approach ensures accuracy and relevance of the data being shared.

Verification:

The documentation confirms that finalization at the end of each run results in the data being specific to that run.

Next-Best-Action Framework - This framework in Pega CDH is designed to decide the best action for each customer interaction by evaluating eligibility, suitability, and propensity.

Complex Eligibility Criteria - To define complex eligibility criteria, modifications are needed within the Next-Best-Action decision strategy to handle multiple conditions and business rules.

Steps:

Access the Next-Best-Action Designer.

Navigate to the decision strategy.

Add or modify components to incorporate complex eligibility conditions.

Test and validate the changes using persona tests and simulations.

Pega Customer Decision Hub User Guide 8.5, Section 'Next-Best-Action strategy framework,' describes how to modify decision strategies to include complex eligibility criteria .