PEGAPCDC87V1 Exam Dumps : Certified Pega Decisioning Consultant (PCDC) 87V1

To address the issue where customers have ignored a particular mortgage offer and the bank wants to offer the action 30% more frequently, we need to configure the appropriate arbitration factor in Pega Customer Decision Hub.

Arbitration Overview: Arbitration in Pega CDH determines how actions are prioritized and selected for presentation to the customer. It involves factors like business value, propensity, and action weighting.

Action Weighting: Action weighting is used to adjust the frequency and priority of specific actions. By increasing the action weighting, you effectively make the action more likely to be selected during the arbitration process.

Implementation:

Navigate to the Next-Best-Action Designer in Pega CDH.

Locate the action for the mortgage offer that needs increased frequency.

Adjust the action weighting to increase by 30%. This adjustment modifies the priority calculation, making the action more likely to be selected.

Next-Best-Action Strategy Application: Pega's Next-Best-Action strategy can be applied to various business issues to optimize decision-making and enhance customer interactions.

Collections: Used to manage and optimize debt collection processes by determining the best actions to take with customers who have outstanding debts.

Service: Enhances customer service interactions by suggesting the most relevant actions or responses to customer inquiries and issues.

Retention: Focuses on retaining customers by identifying and executing actions that are most likely to keep them engaged and loyal.

Enrichment decision components in Pega Customer Decision Hub provide the ability to set customer properties. These components are used to enhance the customer data within a decision strategy by adding or updating information about the customer. This allows for more personalized and contextually relevant decisions.

Definition and Purpose:

Enrichment components are used within decision strategies to add or update customer data, ensuring that the decisioning process has the most relevant and up-to-date information.

This can include setting properties like customer preferences, behaviors, or other attributes that might influence the decision outcome.

Types of Enrichment Components:

Set Property: This is the primary enrichment component used to set a value to a property within the strategy.

Data Import: Importing data from external sources to enrich the customer profile.

Data Transform: Applying transformations to data to format or manipulate it as needed within the strategy.

Usage in Decision Strategies:

When designing a decision strategy, enrichment components are typically used in the initial stages to ensure that all necessary customer data is available and accurate before proceeding to decision logic components.

For example, before evaluating eligibility or applicability, you might use an enrichment component to update the customer's credit score or recent transaction history.

Implementation Steps:

Step 1: Drag the enrichment component into the decision strategy canvas.

Step 2: Configure the component by selecting the property to set and defining the value or expression to be used.

Step 3: Connect the enrichment component to other components within the strategy to ensure the enriched data is utilized in subsequent decision-making steps.

Example:

Consider a scenario where a bank wants to offer personalized loan options based on the customer's recent financial activities. An enrichment component can be used to update the customer's profile with their latest transaction data, ensuring that the loan offer is tailored to their current financial status.

References:

Pega-Customer-Decision-Hub-User-Guide-85.pdf: "Understanding the Next-Best-Action strategy framework" section, which describes the use of enrichment components in decision strategies.

Pega documentation on "Creating and understanding decision strategies".