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".

The Action Distribution Report is designed to test how new offers will be distributed by running a distribution test that only considers eligibility without arbitration. This report helps in understanding how different actions or offers are being allocated to customers based on the eligibility criteria defined in the engagement policies, without taking into account the arbitration logic.

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".