Reference module: Creating and understanding decision strategies.
What does a dotted line from a Group By component to a Filter component mean?
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.
U+ Bank, a retail bank, uses the always-on outbound approach to send outbound messages on different channels such as email, SMS, and push notifications. There are a variety of action flow patterns in use to meet various business and channel integrations requirements.
Due to technical reasons, the bank wants to temporarily suspend sending outbound messages and instead write the selected customers and action details to a database table for later offline processing.
What is the most efficient way to meet this requirement?
To temporarily suspend sending outbound messages and instead write the selected customers and action details to a database table for later offline processing, the most efficient way is to update the Send shape with Finalization in all the action flows. This approach allows the system to complete the processing of actions without sending the messages, and instead, store the necessary details in a database for later use.
To calculate the total number of customer responses of four actions in a group, you must use________________.
Grouping Actions: To calculate the total number of customer responses of four actions in a group, you need to use one Group By component. This component can aggregate responses across multiple actions within a strategy.
Implementation: Add a Group By component to the decision strategy, configure it to group by the desired property, and aggregate the responses from the four actions.
Pega customer Decision Hub enables organizations to make Next-Best decisions. To which type of a decision is Next-Best-Action applied?
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.
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'.
Reference module: Creating eligibility rules using customer risk segments.
U+ Bank uses a decision table to return a label for a customer. Examine this decision table and select which label is returned for a customer with a credit score of 115 and an average balance of 15000.
To determine the label returned by a decision table for a customer with a credit score of 115 and an average balance of 15000, you need to examine the decision table rules. Based on the typical structure of decision tables, a credit score of 115 and an average balance of 15000 would fall within the ranges specified for the label 'B'.
Creating and using decision tables for customer segmentation (Page 72-73)
Example decision table configurations (Page 74-75)
