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

 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.

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.

 Understanding Volume Constraints:

Volume constraints can be applied to limit the number of actions offered to customers.

Constraints can be set to apply to actions as a group or individually.

 Scenario Analysis:

In the given scenario, a customer qualifies for three actions.

The top-ranked action has not reached its volume limit, but the lower-ranked actions have.

Since the constraints are applied as a group, if the top-ranked action is available, it will be selected.

The lower-ranked actions, having reached their limits, will not be selected.

 Conclusion:

Only the top-ranked action will be selected for the customer in the outbound run.

To present a free 2GB data offer when customers contact the call center for mobile number portability, you should configure context weighting in Pega Customer Decision Hub. Context weighting allows you to prioritize actions based on the specific context of the interaction, such as the reason for the call. By setting a higher weight for the free data offer in the context of mobile number portability, you can ensure this offer is prioritized when relevant.

 Pega allows for the personalization of email subjects using customer data and expressions.

 This feature enhances the relevance and engagement of the email content.

 For example, using "Dear " in the subject line can increase open rates and customer engagement.

 Reference: Pega-Customer-Decision-Hub-User-Guide-87.pdf, section on configuring email treatments and personalizing content .

 Volume Constraint Application: Given the volume constraints of a maximum of 100 daily for the StandardCard action, and 75 daily for both Email and SMS channels, the total actions delivered would be limited to the smallest constraint applicable to each channel.

 Calculation:

The StandardCard constraint allows 100 actions per day.

The Email constraint allows 75 actions per day.

The SMS constraint allows 75 actions per day.

Therefore, the total actions delivered = 100 (since the StandardCard action is constrained to 100 but both email and SMS actions can only reach 75 each).

 Understanding Engagement Policies: Pega Customer Decision Hub uses engagement policies like eligibility, applicability, and suitability to determine if an action should be presented to a customer.

 Eligibility Criteria: These criteria define whether a customer qualifies for an action.

 Bank's Requirement: U+ Bank wants to avoid offering a mortgage refinance option to customers who have less than 5% left on their loan.

 Best Fit: The eligibility policy best suits this requirement as it allows the bank to set specific criteria (loan balance percentage) to exclude certain customers from receiving the offer​​.

Enrichment Components - These are used to add additional information or modify properties within a decision strategy.

Setting Text Values:

Enrichment components can be configured to set or modify strategy properties.

This includes setting text values, numerical values, or other types of data.

Steps:

In the decision strategy, add an Enrichment component.

Define the target property and the value to be set.

This value can be static text or dynamically generated based on other strategy components.

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 promote offers in email using Next-Best-Action Designer, U+ Bank needs to ensure that specific configurations are in place.

Enable the Email Channel:

Step 1: Go to Next-Best-Action Designer in Pega Customer Decision Hub.

Step 2: Navigate to the Channels tab and ensure that the Email channel is toggled on.

Step 3: Configure the email channel settings, including the email server and any necessary authentication details.

Define the Primary Schedule:

Step 1: In Next-Best-Action Designer, navigate to the Engagement Policy tab.

Step 2: Define the primary schedule for when email communications should be sent. This includes setting the frequency and timing of emails.

Step 3: Ensure that the schedule aligns with the bank’s overall marketing calendar and customer engagement strategy.

Additional Considerations:

Arbitration Configuration: While not mandatory, configuring arbitration to boost prioritization of actions meant for email can help ensure that email offers are given appropriate priority.

Real-Time Container Triggers: Setting up real-time container triggers to communicate with the email server is essential if real-time email communication is required, though it is not specifically needed for scheduled email campaigns.

Example Implementation:

U+ Bank can start by enabling the email channel and configuring the primary schedule for their email campaigns. Once these steps are completed, they can create email treatments for their offers and test them using seed lists to ensure personalization and content accuracy.

References:

Pega-Customer-Decision-Hub-User-Guide-85.pdf: "Understanding Next-Best-Action Designer channels" and "Configuring email treatments" sections.

Pega documentation on "Sending offer emails" and "Creating treatments for additional channels".

To enable the website to communicate with the Pega Customer Decision Hub and show the Reward Card offer to qualified customers, you need to:

Enable the web channel in the Next-Best-Action Designer's Channel tab. This step activates the web channel for inbound interactions, allowing the system to deliver personalized actions to customers visiting the website.

Map the real-time container to a business structure level. This ensures that the container is correctly linked to the business context, allowing the system to deliver the right offers based on the customer’s interactions with the website.

To test the visualization and rendering of the email content, including the replacement of placeholders with customer information, you use a seed list from the Test Message tab. A seed list allows you to send test emails to a predefined group of recipients, ensuring that the dynamic content is correctly rendered and personalized before the actual campaign is launched.

A solid arrow from a "Set Property" component to a "Filter" component in a Pega decision strategy indicates that a property set by the "Set Property" component is being referenced or used by the "Filter" component. This relationship ensures that the data set in the "Set Property" component influences the filtering criteria in the "Filter" component, allowing for more dynamic and contextually relevant decisions.

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.

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.

To present offers based on the responses of a test group that already received the offer, you should configure the propensity factor in Pega Customer Decision Hub. Propensity models predict the likelihood of a customer accepting an offer based on historical data and responses from similar customers. By leveraging propensity, you can ensure that offers are presented to customers who are most likely to respond positively, based on the behavior of the test group.

Value Finder is a tool in Pega Customer Decision Hub that helps identify and visualize the distribution of under-served customers across different groups. It also highlights opportunities for improvement by pinpointing areas where the engagement policies or strategies can be optimized to better serve these customers. This helps in fine-tuning the next-best-action strategies to enhance overall customer engagement and satisfaction.

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​

 Marketing and Sales Actions: Creating actions like "No annual fee credit card" and "1% cash back credit card" address different customer needs and stages in the customer lifecycle, including acquisition and engagement.

 Full Customer Lifecycle: These actions involve marketing and sales efforts aimed at attracting and retaining customers by offering attractive benefits.

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.

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

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.

Paul cannot see the Reward card offer due to the contact policies defined by U+ Bank:

Contact Policies Defined:

Policy 1: Suppress a group of credit card offers for 30 days if any credit card offer is rejected three times in any channel in the past 15 days.

Policy 2: Suppress the Reward card offer for 7 days if it is rejected twice in any channel in the last 7 days.

Paul’s Rejection History:

Paul has rejected other credit card offers twice on the web channel and once in the contact center within the last 15 days.

Reason for Suppression:

Due to Policy 1, since Paul has rejected any credit card offer (not specifically the Reward card) three times in any channel in the past 15 days, all credit card offers, including the Reward card offer, are suppressed for 30 days.

Understanding the Suppression Logic:

Step 1: The decision strategy evaluates the rejection history of offers.

Step 2: It applies the contact policies to determine which offers should be suppressed.

Step 3: In this case, the policy conditions are met due to multiple rejections of credit card offers, leading to the suppression of the Reward card offer.

References:

Pega-Customer-Decision-Hub-User-Guide-85.pdf: "Setting constraints, contact policy limits, and controls" section.

Pega documentation on "Managing contact policies".

 Ethical Bias Policy Configuration:

To allow minimal bias on age while ensuring eligibility (Age >= 18), an appropriate Gini coefficient threshold needs to be set.

The Gini coefficient measures statistical inequality, with 0 representing perfect equality and higher values indicating more bias.

 Choosing the Gini Threshold:

A 0.1 Gini coefficient is a low threshold that allows minimal bias.

It ensures that the actions are distributed fairly among customers while still respecting the eligibility condition.

 Detailed Explanation:

Setting a 0.1 Gini coefficient allows for slight variations in distribution, which is sufficient to accommodate the Age >= 18 requirement without introducing significant bias.

 Verification from Pega Documentation:

The Pega Customer Decision Hub User Guide explains the use of Gini coefficients for measuring and setting thresholds to control bias in decisioning strategies​​.

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".

Decision Table Evaluation - Decision tables evaluate conditions in a specified order and return a result based on the first matching condition.

Conditions and Labels - Based on the decision table provided, the CLV score of 550 and LOW propensity to leave match the condition for label B.

Process:

Evaluate the CLV score condition.

Evaluate the propensity to leave condition.

The matching row in the decision table specifies the label to return.

 Addressing Test Failures: When persona tests fail after adding new engagement policy conditions, the appropriate step is to analyze the test case failures, update the tests as necessary, and re-execute them until all tests pass. This ensures that the changes made are correctly reflected in the tests and the system functions as expected.

 Implementation: Review the failing tests to understand why they failed, adjust the conditions or the expected results, and run the tests again to validate the changes.

Ensuring Latest Customer Information: To ensure that the outbound run always uses the latest customer information, you need to select the "Refresh the audience" option. This setting ensures that the audience is refreshed with the most recent customer data before the campaign is executed.

Implementation: Navigate to the campaign configuration and select the option to refresh the audience before running the campaign. This guarantees that any changes or updates to the customer data are taken into account.

The condition that possibly prevents Anna from seeing the new Rewards card offer is "Eligibility." Since Anna already holds a higher value card, Premier Rewards, the eligibility criteria might exclude her from receiving offers for the Rewards card. This ensures that customers are not offered products that are less relevant or valuable compared to what they already have.

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.

 Requirement for Total Revenue Calculation:

MyCo needs to calculate the total revenue of three new actions over a specified period.

The goal is to aggregate the revenue generated by these actions.

 Use of Group By Component:

The Group By component is used to aggregate data based on specified properties.

In this scenario, it can be configured to sum the revenue of the three new actions.

 Configuration Steps:

Configure a single Group By component to group the actions by their identifiers.

Use the aggregation function within the Group By component to calculate the sum of the revenue for the grouped actions.

 Efficiency and Simplicity:

Using a single Group By component simplifies the strategy by avoiding redundancy.

It ensures that all actions are considered in a single aggregation process, providing an accurate total revenue calculation.

 Verification from Pega Documentation:

According to Pega's guidelines, a single Group By component can efficiently compute total values such as revenue by grouping and aggregating data.

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.

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 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.

When introducing a new group of offers called Credit cards, valid actions should be specific credit card offers that provide clear value propositions to customers.

Creating Specific Offers:

No Annual Fee Credit Card: This offer targets customers looking to avoid annual fees, making it an attractive option for budget-conscious individuals.

1% Cashback Credit Card: This offer appeals to customers interested in earning cashback rewards on their purchases, adding an incentive for using the card.

Steps to Create Offers:

Step 1: Navigate to the Next-Best-Action Designer in Pega Customer Decision Hub.

Step 2: Define the new group "Credit Cards" under the appropriate business issue.

Step 3: Create individual actions for "No Annual Fee Credit Card" and "1% Cashback Credit Card," specifying the details and conditions for each offer.

Step 4: Configure the engagement policies, eligibility criteria, and treatments for these actions to ensure they are presented to the right customers.

Benefits:

Creating specific and clear offers helps in better targeting and engagement with customers, leading to higher acceptance rates and customer satisfaction.

These offers align with common customer preferences and needs, making them more likely to be accepted.

Example:

U+ Bank creates the "No Annual Fee Credit Card" and "1% Cashback Credit Card" offers in the Pega Customer Decision Hub. Each offer is configured with specific eligibility criteria and personalized email treatments to ensure relevant customers receive these offers.

References:

Pega-Customer-Decision-Hub-User-Guide-85.pdf: "Creating and managing actions" section.

Pega documentation on "Next-Best-Action Designer and offer creation".

Correct Engagement Policy Configuration:

Age

Engagement policy: Group-level eligibility

Opt-out

Engagement policy: Group-level applicability

Debt-to-income ratio

Engagement policy: Action-level suitability

Age

Group-level eligibility: This criterion determines whether the customer is eligible to receive any credit card offers. Eligibility conditions ensure that only customers meeting the basic criteria (e.g., being above the age of 18) are considered for the offers. This condition applies to all credit card offers within the group.

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.

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.

Identifying the Requirement:

The bank wants to leverage Pega CDH’s Next-Best-Action capability to promote new offers on their website.

The task is to determine what information Pega CDH sends back to the website in response to a real-time container request.

Evaluating Pega CDH Capabilities:

Pega CDH’s Next-Best-Action Designer is designed to handle real-time interactions and provides personalized recommendations.

The system returns comprehensive details to ensure the website can properly display and execute the recommended actions.

Process Overview:

Real-Time Container Request:

When the website sends a real-time container request, Pega CDH processes the request and identifies the most relevant actions based on customer data and engagement policies.

Response Details:

The response includes treatment details such as the action’s visual elements (image URLs, descriptions) and relevant action attributes (e.g., offer type, benefits, eligibility criteria).

References and Supporting Documentation:

The Pega Customer Decision Hub User Guide clearly outlines that in response to a real-time container request, the system sends back the treatment details and relevant action attributes to ensure the offer can be presented accurately on the website.

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​

 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".

 Triggering Segment Runs with Next-Best-Action Outbound Schedule:

Segments can be configured to run in alignment with the Next-Best-Action outbound schedule.

This setup ensures that the segment is refreshed and populated with the latest customer data before each outbound run.

 Translating Traditional Segments: When moving away from traditional marketing to always-on outbound capabilities in Pega Customer Decision Hub, translating traditional segments is essential to identify the target audience correctly.

 Segmentation Configuration: In Pega, segmentation rules are configured to define and group customers based on various attributes. This allows the system to identify the right target audience for outbound campaigns.

Business Issues and Groups in Pega CDH - Business issues (like Sales, Service, Retention) categorize customer engagement strategies. Each issue can have multiple groups representing specific goals (e.g., credit cards, loans under Sales).

Retention Issue - Since the objective is to retain customers who are at high risk of leaving, modifying the Retention issue aligns the strategy with the goal of reducing churn.

Steps to Modify Retention Issue:

Navigate to the Next-Best-Action Designer.

Select the Retention issue.

Update or create new applicability settings to identify high churn risk customers.

Configure actions (proactive retention offers) to be presented to these customers.

 Addressing Test Failures: When persona tests fail after adding new engagement policy conditions, the appropriate step is to analyze the test case failures, update the tests as necessary, and re-execute them until all tests pass. This ensures that the changes made are correctly reflected in the tests and the system functions as expected.

 Implementation: Review the failing tests to understand why they failed, adjust the conditions or the expected results, and run the tests again to validate the changes.

 Configuring Ethical Bias Policy:

To configure bias fields, the context of the bias must be selected, which usually pertains to customer-related properties.

 Selecting Bias Fields:

Bias fields such as age, gender, and ethnicity are typically customer properties.

These properties are analyzed to detect and measure bias in decision strategies.

 Detailed Explanation:

By selecting the Customer context, you can add and configure relevant customer properties to the bias policy.

This allows for comprehensive bias testing and ensures fairness in action distribution.

 Verification from Pega Documentation:

Pega documentation confirms that customer-related properties are the primary context for configuring bias fields in ethical bias policies​​​​.

 Identify the issue: When a customer receives an offer they have already accepted, it suggests that the decision strategy did not consider previous interactions.

 Check strategy customization: Strategies in Pega CDH can be customized to include rules that prevent the same offer from being presented again.

 Confirm the exclusion of previous offers: Specifically, you need to ensure that there is a suppression rule or a logic to check the Interaction History to exclude previously accepted offers.

 Reference: Refer to the section on "Next-Best-Action Designer constraints" and "Engagement policies" in the Pega Customer Decision Hub User Guide. These sections explain how to configure rules to prevent repetitive offers​​.

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.

Decision Table Processing: The decision table is configured to evaluate the customer's credit score and average balance to determine the risk segmentation.

Conditions Evaluation:

For a credit score of 240 and an average balance of 35,000:

The first condition checks if the credit score is >= 400 and < 600, and average balance is >= 30,000, which does not match.

The second condition checks if the credit score is >= 200 and < 400, and average balance is >= 20,000, which matches both the credit score (240) and the average balance (35,000).

Therefore, the correct result for these conditions is "Fair".

References: This is derived from typical Pega Decision Table configuration and logical evaluation as described in Pega documentation on using decision tables for such scenarios .