PEGAPCDC87V1 Certified Pega Decisioning Consultant (PCDC) 87V1 Questions and Answers

Scorecard Component - This component in Pega CDH is designed to evaluate attributes and produce a score based on those attributes. It is commonly used for calculating credit scores, risk scores, etc.

Steps to Use Scorecard:

In the decision strategy, add a Scorecard component.

Configure the scorecard to include relevant customer attributes (e.g., payment history, credit utilization).

Set the rules and weights for each attribute to calculate the final credit score.

Use the score in further decision logic (e.g., filtering out customers with a score below 500).

Designing Reusable Action Flows:

When creating an action flow intended for reuse, it's important to configure it with specific treatments.

The Specify Treatment option allows the flow to dynamically apply the appropriate treatment based on the action context.

This approach ensures consistency and flexibility, making it easier to manage and update the flows.

Business Requirement: The need is to implement risk segmentation rules based on age and credit score.

Decision Table Integration: The decision table can be added to the decision strategy where it will evaluate the credit score and age, and then assign the appropriate risk segment.

Parameter Passing: The decision table within the strategy will use the credit score passed to it as a parameter to determine the segmentation.

References: Pega's methodology for incorporating decision tables into strategies for evaluating and segmenting customers based on specific criteria is well documented in the user guides .

In a call-center application that receives Next-Best-Actions from Pega Customer Decision Hub, the customer service representative (CSR) is guided on the next important conversation to have with the customer. The Next-Best-Action recommendations help CSRs make informed decisions and provide relevant, personalized service to customers by suggesting the most appropriate actions based on the customer's profile, interaction history, and current context.

 Next-Best-Action Offers Ranking: When a Customer Service Representative (CSR) is presented with a set of sales offers, the Next-Best-Action feature ranks these offers based on their relevance and priority.

 Decision Making: This ranking helps the CSR to decide which offer to present to the customer, ensuring that the most appropriate and effective offer is chosen.

To update the cashback offer to be presented through a new channel (SMS) and to update the suitability condition, you need to perform two main tasks: adding a new treatment and editing the engagement policy.

Add a New Treatment:

Step 1: Navigate to the "Actions" section in the Pega Customer Decision Hub.

Step 2: Locate the existing cashback offer action.

Step 3: Add a new treatment specifically for the SMS channel. This involves creating a new SMS treatment that defines the message content, format, and any other relevant details.

Step 4: Ensure the new treatment is linked to the cashback offer action.

Edit the Engagement Policy:

Step 1: In the Pega Customer Decision Hub, go to the "Engagement Policies" section.

Step 2: Locate the engagement policy associated with the cashback offer.

Step 3: Update the suitability condition by lowering the debt-to-income ratio threshold from 48 to 45. This ensures that the new condition is applied when evaluating customer eligibility for the cashback offer.

Step 4: Save and publish the updated engagement policy to ensure it takes effect.

Verify the Changes:

Step 1: Test the new SMS treatment by sending test messages to a seed list to verify the content and formatting.

Step 2: Ensure that the updated engagement policy correctly filters customers based on the new debt-to-income ratio threshold.

References:

Pega-Customer-Decision-Hub-User-Guide-85.pdf: "Defining treatments for actions" and "Configuring engagement policies" sections.

Pega documentation on "Creating and managing actions" and "Editing engagement policies".

Updating Scorecard in Decision Strategy: When a scorecard rule is updated to include a new property such as customer income, it is essential to reflect this change in the decision strategy. The scorecard properties must be correctly mapped to ensure the new calculations are utilized.

To select the best offer from both groups based on customer behavior, you should ensure that the propensity is enabled in the Arbitration tab. Propensity scores predict the likelihood of a customer accepting a particular offer, and by enabling propensity, you can prioritize offers that have a higher likelihood of acceptance, thus aligning offers with customer behavior.

 Action Availability Configuration: To ensure that a specific action is not selected for any customers, you need to set its availability to "Never." This setting ensures that the action will not be available for selection in any campaign or strategy, effectively preventing its use.

 Implementation: Navigate to the action configuration settings and set the availability to "Never." This will ensure that the action is not picked up by the decision strategy or any outbound run.

Interaction History Summary - This rule aggregates data like customer responses, action outcomes, and interactions over a set time period.

Steps to Create the Rule:

In Dev Studio, go to Configure -> Decisioning -> Decisions -> Data Sources -> Interaction History Summaries.

Create a new summary rule.

Set the time period to 20 days.

Define the interaction data to be tracked (e.g., accepts, rejects).

Benefits - Enables precise tracking of customer engagement and responses, facilitating better decision strategies based on historical data.

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.

With the volume constraints applied (75 maximum daily actions for StandardCard, 50 maximum daily actions for Email, and 50 maximum daily actions for SMS), the outbound run will deliver a total of 75 actions. This includes actions across different channels while adhering to the specified constraints.

Identifying the Requirement:

The bank has chosen an email service provider that prefers receiving customer batches through a cloud storage location on either Microsoft Azure or Amazon S3.

The task is to find out how Pega Customer Decision Hub can integrate with these cloud storage solutions.

Evaluating Pega CDH Capabilities:

Pega Customer Decision Hub provides robust capabilities for integrating with various external systems and cloud storage solutions.

The documentation for Pega CDH supports writing action details directly to cloud storage solutions like Microsoft Azure and Amazon S3, which is in line with modern cloud infrastructure practices.

Process Overview:

Configuration:

Configure the external storage location (Azure Blob Storage or Amazon S3) within Pega CDH.

Action Flow:

Define the action flow to include steps for writing data to the specified cloud storage.

External Integration:

Use built-in connectors or custom integration points to handle data transfer securely and efficiently.

References and Supporting Documentation:

According to the Pega Customer Decision Hub User Guide, you can configure Pega CDH to write action details directly to cloud storage solutions like Microsoft Azure and Amazon S3 (refer to the section on configuring the delivery of multiple treatments and external integration points).

References:

Pega-Customer-Decision-Hub-User-Guide-85.pdf

Pega-Customer-Decision-Hub-User-Guide-86.pdf

Pega-Customer-Decision-Hub-User-Guide-87.pdf​

To access a property from an unconnected component in Pega Customer Decision Hub, you use the component-dot-property construct. This allows you to reference properties from other components within your strategy by specifying the component name followed by the property name. This construct helps in accessing and using data across different components without directly connecting them.

 Understanding the Decision Table:

The decision table provided returns a label based on a customer's credit score and average balance.

The rules are evaluated in order, and the first matching rule determines the result.

 Evaluating the Customer Data:

The customer has a credit score of 115 and an average balance of 15000.

We will evaluate each row of the decision table to see where these values fit.

 Rule Evaluation:

First Rule: Credit Score >= 130 and Average Balance >= 30000

Customer's Credit Score: 115 (not >= 130)

This rule does not match.

Second Rule: Credit Score >= 110 and Average Balance >= 20000

Customer's Credit Score: 115 (>= 110) but Average Balance: 15000 (not >= 20000)

This rule does not match.

Third Rule: Credit Score >= 90 and Average Balance >= 10000

Customer's Credit Score: 115 (>= 90) and Average Balance: 15000 (>= 10000)

Both conditions match, so the result is "C".

Fourth Rule: Credit Score >= 70 and Average Balance >= 1000

This rule is not evaluated because the previous rule matched.

 Conclusion:

According to the decision table, the result for a customer with a credit score of 115 and an average balance of 15000 is "B".

To ensure that there is no gender bias when presenting mobile data packages for students, you should configure the ethical bias policy to allow a 1 Rate Ratio. This setting ensures that the system will show a warning if there is any deviation from an equal distribution between genders, thereby preventing any gender-based discrimination in offer presentation.

Set-Property Component: This component performs the calculation of profit for each of the four brands (Nike, Adidas, Puma, Reebok). Since the Set-Property component is configured to calculate profit for each brand individually, it processes four inputs, resulting in four outputs.

To promote various auto loan offers to qualified customers on its website using Pega Customer Decision Hub, you need to:

Create a decision strategy: This involves defining the logic that determines which auto loan offers are most relevant to each customer based on their profile, behavior, and eligibility criteria.

Set up the business structure: This involves organizing the offers into a hierarchical structure of business issues and groups, such as categorizing them under a "Loans" issue and an "Auto Loans" group. This structure helps in managing and prioritizing the offers effectively.

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.

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.

 Volume Constraints Implementation: To implement a policy that limits the sending of more than 500 emails per day, you need to configure volume constraints. Volume constraints allow you to set limits on the number of actions delivered within a specified period.

 Configuration: Create a volume constraint rule and set the maximum volume to 500 emails per day.

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.

 Engagement Policy Condition: For offering a Fiber Optic Cable package only to customers in towns with fiber optic availability, the best engagement policy condition to use is "Eligibility." Eligibility conditions are used to ensure that only customers who meet specific criteria (e.g., geographic location) are considered for the offer.

 Implementation: Define an eligibility condition in the engagement policy that checks whether the customer resides in a town with fiber optic cable availability.

 Requirement:

The bank wants to avoid sending promotional emails related to credit card offers to a customer if they have already received one within a limited time period.

 Configuring Suppression Policy:

A suppression policy needs to be defined to prevent over-contacting customers.

Suppression policies automatically put an action on hold after specific criteria are met, such as a customer having already received a promotional email.

This ensures that customers are not bombarded with repeated offers, improving customer experience and adhering to communication limits.

Initial Configuration: U+ Bank has implemented Pega Customer Decision Hub for the contact center and now wants to extend it to the web channel.

Understand Channel Configuration: Pega CDH requires the configuration of channels in the Next-Best-Action Designer to ensure that treatments are correctly recommended.

Modify the Strategy Framework:

Next-Best-Action Framework: This strategy framework is used to determine the best actions for customers across various channels. It needs to be modified to include configurations specific to the web channel.

Steps to Modify:

Access Next-Best-Action Designer: Navigate to the Next-Best-Action Designer.

Channels Tab: Configure the new web channel in the Channels tab.

Modify the Strategy: Update the Next-Best-Action Framework strategy to incorporate the new web channel. This involves ensuring the new actions and treatments are included and prioritized correctly for the web channel.

According to the Pega Customer Decision Hub User Guide, modifying the framework strategy to cater to new channels is necessary for extending customer engagement (Reference: Pega-Customer-Decision-Hub-User-Guide-85.pdf, Chapter on "Understanding Next-Best-Action Designer channels").

Testing and Validation:

Test the new configurations to ensure treatments for the web channel are correctly recommended in the next-best-action suggestions.

Conclusion: To configure the new treatments for the web channel in the next-best-action recommendations, the Next-Best-Action Framework strategy must be modified to cater to the new web channel.

To re-implement the requirement using the always-on outbound customer engagement paradigm, configure the primary schedule to run daily and let the AI choose the best action from all the actions that a customer qualifies for based on engagement policies. This approach leverages AI to dynamically select the most relevant next best actions, ensuring timely and personalized customer interactions.

To determine which of the new credit card offers (Diamond, Platinum, and Gold) will be the best performing action, the Action Distribution Report is used. This report helps in running a distribution test to analyze how the different offers are being distributed among the customer base, providing insights into which offer might perform the best based on initial distribution and eligibility criteria.

 Arbitration Factors: In Pega, arbitration is the process of prioritizing actions based on multiple factors.

 Action Value: This is a specific arbitration factor that allows assigning financial values to actions.

 Purpose: Action value helps in determining the prioritization of actions based on their financial impact or benefit to the business.

 Reference: According to Pega Customer Decision Hub documentation, the action value is indeed the term used for this specific arbitration factor​​.

The best way to organize customer actions applicable to various business issues in Pega Customer Decision Hub is into a three-level hierarchy: Business issue > Group > Actions. This structure allows for clear categorization and management of actions based on their business purpose and relevance. The "Business issue" represents the high-level goal (e.g., Retention, Acquisition), "Group" categorizes the actions under each issue (e.g., Credit Cards, Mortgages), and "Actions" are the specific offers or recommendations.

Compute SUM of PROFIT = Group BY

Determine target budget to retain a customer = Set Property

Determine customer credit score = Scorecard

Divide customers into risk segment = Decision Table

To configure the arbitration factor for nudging the Platinum Plus credit card to customers who visit the home page, you need to focus on action value. Action value takes into account the business value or importance of an action, which aligns with the goal of promoting a specific credit card to customers.

Arbitration in Pega CDH - Arbitration is the process of ranking actions based on their priority. Factors like propensity, context weighting, action value, and business levers are considered during arbitration.

Action Value - This factor assesses the intrinsic value of an action, which includes its business value. It is crucial when a business objective, like promoting a specific credit card, needs to be prioritized.

Implementation - By configuring action value, you ensure that the Platinum Plus credit card offer has a higher priority over other actions for customers visiting the homepage.

To prioritize mortgages over credit cards for the fourth quarter, you should configure the business levers in Pega Customer Decision Hub. Business levers allow you to adjust the weight or priority of different actions based on business goals. By increasing the weight for mortgage offers, you can ensure that these offers are prioritized over credit card offers, even if a credit card may create more value.

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".

In Pega Customer Decision Hub, the Next-Best-Action is re-evaluated in a call-center context when:

The service representative completes a task: Completing a task can change the context or status of the interaction, triggering a re-evaluation of the next best action to ensure the recommendations are still relevant.

A customer responds to an offer: Customer responses provide new information that can influence subsequent recommendations. A positive or negative response to an offer can change the customer's engagement status and preferences.

A customer changes the call context: Changes in the context of the call, such as switching topics or expressing new concerns, can prompt a re-evaluation to provide the most appropriate next actions based on the updated context.

To ensure that Silver credit cards are not offered to customers under 27 years of age and that Platinum cards are offered only to customers who had a positive balance in the last year, you need to configure "Engagement policies" in the Next-Best-Action Designer. Engagement policies allow you to set criteria such as eligibility, applicability, and suitability for actions. For this scenario, you would set the eligibility criteria to ensure the age and balance conditions are met before the offers are presented.

 Understanding the Scenario Planner Output:

The image shows the projected reach and projected responses for several credit card offers, including the Standard card.

The "Projected responses" column indicates the number of customers expected to respond positively to each offer.

 Interpreting the Data for the Standard Card:

For the Standard card, the "Projected responses" is 21, with a change indicator showing a decrease of 7.

This decrease is factored into the projected response, meaning the expected number of positive responses after considering the applied business weight.

 Step-by-Step Calculation:

The base projected responses for the Standard card is 21.

The decrease of 7 has already been factored into this projection.

Therefore, the number of customers likely to accept the Standard card remains 21, as it represents the final projected response after adjustments.

 Verification from Pega Documentation:

The Scenario Planner in Pega Customer Decision Hub provides these projections to help visualize the impact of business weight adjustments and other lever changes on customer actions.

To automatically pause actions that are shown too often for a specific time period, you need to define suppression rules in Pega Customer Decision Hub. Suppression rules allow you to put an action on hold after a specific number of outcomes, such as views or clicks, have been recorded over a defined period. This helps prevent overexposure and ensures a better customer experience by limiting how frequently the same action is shown to a customer.

 Context: A bank wants to use the Next-Best-Action capability of Pega Customer Decision Hub to promote offers on their website.

 Real-time Container Request: When a real-time container request is made, Pega Customer Decision Hub processes this request.

 Response Information: The response sent back to the website includes comprehensive details about the actions.

 Details Provided: According to Pega documentation, the response to a real-time container request includes treatment details (like images and descriptions) and relevant action attributes that personalize the offer for the customer​​.

 Context:

U+ Bank wants to use outbound communication channels for Next-Best-Action recommendations.

 Available Outbound Channels:

Mobile Application: Typically considered an inbound channel.

Google: Used for online advertising but not a direct outbound channel.

SMS: Commonly used outbound channel for direct customer communication.

Email: Widely used outbound channel for direct customer communication.

Contact Center: Usually an inbound channel where customers call in.

 Selection:

SMS and Email are the most suitable outbound channels for sending Next-Best-Action recommendations directly to customers.

 Verification from Pega Documentation:

Pega documentation on configuring and using SMS and Email as outbound channels for Next-Best-Action delivery.

To target customers living in a specific geographic area for advertising the new fiber optic service, you need to configure a "Customer segment" in the Next-Best-Action Designer. This involves defining the segment of customers based on their geographic location, ensuring that only those customers in the northern region are included in this segment. By doing so, you can ensure that the banner advertisement for the fiber optic service is displayed only to those customers.

 Context of Scorecard Update:

MyCo updated their scorecard rule to include a new property, customer annual income, for computing the postpaid credit limit.

This update affects how the scorecard calculates the credit limit.

 Impact on Decision Strategy:

The scorecard rule automatically includes the new property in its calculations.

The decision strategy references the scorecard rule as a whole, not individual properties.

 Reason for No Change Required:

Since the scorecard rule is updated independently, the decision strategy using this rule will automatically reflect the new calculations.

There is no need to manually update the decision strategy to accommodate this change.

 Verification from Pega Documentation:

Pega's documentation confirms that updates to scorecard rules are automatically integrated into decision strategies using those rules.

 Understanding Customer Events:

Customer events are interactions triggered by specific actions or behaviors of the customer.

In this case, an incomplete transaction due to a slow internet connection is an event that prompts further action.

 Email with Link to Continue Transaction:

Sending an email with a link to continue an incomplete transaction is a response to a customer event.

This type of communication is designed to help the customer complete their intended action, improving user experience.

 Verification:

The documentation categorizes interactions like these as customer events.

Business Levers: These are used to apply weights to different actions or groups of actions to influence their prioritization based on business objectives.

Context Weighting: This factor is used to adjust the priority of actions based on the specific context of the customer interaction, ensuring that both business objectives and customer needs are balanced.

References: The configuration and utilization of business levers and context weighting within the Next-Best-Action Designer are key components in aligning the decision strategy with both business and customer goals .

Task: Create a new action

Portal: Pega Customer Decision Hub

Task: Modify a decision table

Portal: Operations Manager

Task: Create a decision strategy

Portal: Pega Customer Decision Hub

Task: Edit an engagement policy for an action

Portal: Pega Customer Decision Hub

Task: Edit a group level engagement policy

Portal: Pega Customer Decision Hub

Task: Edit an email treatment for an action

Portal: Pega Customer Decision Hub

Create a new action:

Pega Customer Decision Hub: Creating new actions is typically performed within the Pega Customer Decision Hub where users can manage and configure actions that will be part of the decisioning strategies.

In Pega Customer Decision Hub, the characteristics of an action are defined by using properties. Properties are used to capture specific details about an action, such as the type of offer, the conditions under which it should be presented, and other relevant data that influence decision-making. These properties ensure that the actions are personalized and relevant to the customer.

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'.

To ensure that the Gold credit card offer is presented only to customers who have an annual income of more than USD 150,000, you need to configure an engagement policy condition in the Next-Best-Action Designer. Here’s why:

Engagement Policy Condition:

Engagement policies in the Next-Best-Action Designer are used to define conditions that determine whether a particular action (such as offering a Gold credit card) is suitable or eligible for a customer.

By setting an engagement policy condition based on the customer's annual income, you ensure that only those customers who meet the specified income threshold are eligible to receive the offer.

This condition acts as a filter that evaluates each customer’s income and includes only those who earn more than USD 150,000.

Aggregation components in Pega Customer Decision Hub provide the ability to make calculations based upon a list of actions. These components are used to perform operations such as summing values, averaging scores, or other statistical calculations that aggregate data from multiple actions. This is crucial for generating insights and making informed decisions based on the collective data of several actions.

Understanding Aggregation Components: Aggregation components in Pega decision strategies are used to perform calculations based on a list of actions. This can include summing values, calculating averages, or other statistical measures.

Use Case of Aggregation Components:

Set a Text Value: This is typically handled by Set Property or Data Transform components.

Filter Actions: Filters are used for filtering actions based on priority and relevance.

Choose Between Actions: This is typically handled by a Decision or Filter component.

Aggregation is specifically used to perform calculations based on a group of actions. It can be used to sum the total value of actions, calculate the average propensity, etc.

Implementation in Decision Strategy:

Add Aggregation Component: In the decision strategy, add an Aggregation component.

Define Calculation: Specify the type of calculation (sum, average, count, etc.) and the actions to be included in the calculation.

The Pega Customer Decision Hub User Guide mentions the use of aggregation components to perform calculations on action lists, confirming their primary function is for making calculations based on a list of actions (Reference: Pega-Customer-Decision-Hub-User-Guide-85.pdf, Chapter on "Using aggregation components in decision strategies").

Conclusion: In a decision strategy, aggregation components are used to make calculations based upon a list of actions, enabling the strategy to derive meaningful metrics from groups of actions.

If the volume constraint shown in the image (Maximum 75 Daily with Channel: Action: StandardCard, Maximum 50 Daily with Channel: Email, Maximum 50 Daily with Channel: SMS) is applied, the outbound run will deliver 75 actions in total. This includes actions across different channels, adhering to the specified limits.