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

 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.

 Understanding Refreshable Segment Option:

This option ensures that the segment is updated automatically whenever it is referenced.

 Functionality:

When enabled, all segments that reference the refreshable segment are automatically updated, ensuring that the latest data is used.

 Purpose:

This helps maintain data accuracy and relevance across all segments and ensures that the decisioning process is based on current data.

 Verification from Pega Documentation:

Pega documentation on using and configuring refreshable segments in outbound schedules.

 Understanding Refresh the Audience Option:

This option ensures the decision process uses the most current data available.

 Functionality:

Before each run of the schedule, the audience data is refreshed to reflect any changes or updates in customer information.

 Purpose:

This ensures decisions are made based on the latest customer profiles, improving the relevance and accuracy of recommendations.

 Verification from Pega Documentation:

Pega documentation on scheduling and audience refresh options for ensuring up-to-date customer data is used.

 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 ensure that the Platinum Plus offer is prioritized over other offers, you can increase the "business weight" of the Platinum Plus card. The business weight is a lever used in the arbitration process to influence the prioritization of actions. By increasing the business weight, you make the Platinum Plus offer more likely to be selected over other competing offers.

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.

Best practices for designing an action flow in Pega Customer Decision Hub emphasize reusability and efficient configuration:

Reusable Flows:

Step 1: When creating a flow that will be reused across multiple actions, it is recommended to use the "Specify Treatment" option.

Step 2: This approach allows you to configure the flow with a specific treatment, making it easier to apply the same flow logic to different actions without having to redesign the flow each time.

Flow Configuration:

Using the "Specify Treatment" option ensures that the treatment details are dynamically inserted when the action is executed, providing flexibility and consistency across various actions.

This method helps maintain a modular design, where flows can be reused and updated independently of the actions they are associated with.

Avoid Redundancy:

Step 1: Reusing flows across multiple actions reduces redundancy and simplifies maintenance.

Step 2: Any changes to the flow logic need to be made only once, and these changes will be reflected across all actions using that flow.

Example:

If you have a standard email flow for promotional offers, you can configure this flow with the "Specify Treatment" option and reuse it for different promotional actions such as discounts, new product launches, or seasonal offers.

References:

Pega-Customer-Decision-Hub-User-Guide-85.pdf: "Configuring the delivery of an email treatment with the Send Email shape" section.

Customer Properties - These are attributes related to the customer, such as demographics, behavior, and engagement history.

Expression Syntax:

When defining expressions in decision strategies, customer properties must be prefixed with "Customer."

For example, to use the customer's age, you write Customer.Age.

Steps:

In the decision strategy, select the component where you need to use the customer property.

Write the expression using the "Customer" prefix.

 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.

Score Mapping Tab - This tab in the scorecard rule is used to map the raw score to a property that can be used in decision strategies.

Steps to Map a New Property:

Open the scorecard rule in Dev Studio.

Navigate to the Score Mapping tab.

Add a new mapping for the raw score to a property (e.g., pyRawScore).

Save the scorecard rule.

Using the Mapped Property:

In the decision strategy, reference the mapped property to utilize the raw score value.

Apply this property in conditions, filters, or other decision components as needed.

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.

Real-Time Channels for Next-Best-Actions: Pega Customer Decision Hub supports presenting Next-Best-Actions through various real-time channels.

Retail Store: Actions can be presented to customers directly in the store through POS systems or store associates.

Call Center: Customer Service Representatives can receive real-time Next-Best-Action recommendations to present to customers during interactions.

SMS: Automated messages can be sent to customers with personalized Next-Best-Action offers via SMS.

Scorecard Rule - A scorecard in Pega is used to calculate a score based on various customer attributes. To include a new attribute (customer income), the scorecard rule must be updated.

Steps to Update Scorecard:

Navigate to the scorecard rule in Dev Studio.

Add a new input for customer income.

Define the scoring logic for the new input.

Save the updated scorecard.

Decision Strategy - The decision strategy that uses this scorecard must also be updated to ensure it correctly utilizes the new score calculations.

Steps to Update Decision Strategy:

Locate the decision strategy in the Next-Best-Action Designer.

Ensure the updated scorecard is referenced correctly.

Adjust any logic or thresholds that depend on the scorecard output.

Test the strategy to ensure it works as expected with the updated scorecard.

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.

 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.

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).

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.

 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.

 Finalization of Action Details:

Finalization ensures that all data from a specific run is written to a file or database.

This step is crucial for preparing the data to be shared with third parties.

 Single Run Data:

Finalizing at the end of each run means the file will only contain data from that particular run.

This approach ensures accuracy and relevance of the data being shared.

 Verification:

The documentation confirms that finalization at the end of each run results in the data being specific to that run.

Next-Best-Action Communication: Effective Next-Best-Action communication is both contextual and timely.

Contextual: Ensures that the actions or offers presented are relevant to the customer's current situation and preferences.

Timely: Ensures that the actions or offers are presented at the most appropriate time for maximum impact and engagement.

Referencing Customer Properties: In Pega decision strategies, customer properties are accessed by prefixing the property name with "Customer." This convention helps in clearly identifying the context of the properties being used.

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.

In Pega Customer Decision Hub, the property that is automatically recomputed for each decision component is "Priority." This property determines the order in which actions are evaluated and presented to the customer. The Priority property ensures that the most relevant and valuable actions are prioritized based on the defined criteria, including customer relevance, business rules, and real-time contextual data.

 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.

Pega Customer Decision Hub supports writing action details directly to cloud storage solutions such as Microsoft Azure and Amazon S3. This capability ensures that the offer messages selected by the Decision Hub can be seamlessly transferred to the preferred cloud storage location of the email service provider, facilitating efficient and secure delivery of customer communications.

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.

To re-implement the bank's traditional marketing campaign using the always-on outbound customer engagement paradigm in Pega, follow these steps:

Understand the Traditional Campaign Requirements:

Day 1: Send an offer email to qualified customers.

Day 3: Send a reminder email to customers who haven't responded to the first email.

Day 7: Send a second reminder to customers who haven't responded to the first two emails.

Set Up Next-Best-Action Designer:

Use the Next-Best-Action Designer to define engagement policies, arbitration, and channels to manage customer interactions.

The always-on approach continuously evaluates customer interactions and selects the best action based on real-time data and AI-driven insights.

Configure the Primary Schedule:

Set up a daily run schedule for the Next-Best-Action strategy. This allows the AI to evaluate customer interactions daily and decide the best action to take.

Leverage AI for Decisioning:

Configure the AI models to determine the best action for each customer based on their interaction history and engagement policies. The AI will automatically handle:

Sending the initial offer email on Day 1.

Sending a reminder email on Day 3 to those who haven't responded.

Sending a second reminder on Day 7 to those who still haven't responded.

Define Engagement Policies:

Eligibility: Define which customers qualify for the offer.

Applicability: Ensure the offer is relevant to the customer.

Suitability: Verify that sending the offer is in the best interest of the customer.

Contact Policies: Set rules to manage the frequency and timing of emails to avoid over-communication.

Implement AI-Driven Actions:

Use the Next-Best-Action strategy framework to select actions (e.g., sending emails) based on the AI model's recommendations.

Configure treatments (email content) and channels (email) within the Next-Best-Action Designer.

Monitor and Adjust:

Continuously monitor the performance of the campaign using Pega's analytics and adjust the strategy as needed to optimize engagement and response rates.

References:

Pega Customer Decision Hub User Guide 8.5: Understanding Next-Best-Action Designer basics, Configuring the Next-Best-Action Designer for Pega Customer Service, Setting constraints contact policy limits and controls​​.

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.

Contact Policy - Contact policies control the number and frequency of customer contacts to prevent overexposure and irritation.

Steps to Configure Suppression Rule:

In the Next-Best-Action Designer, navigate to Engagement Policy -> Contact Policy.

Add a new contact policy and name it appropriately.

In the Scope section, select "Track Rejects" for any channel.

Set the suppression criteria: if there are 2 rejects in the last 15 days.

Specify the action suppression period as 30 days.

Implementation - This ensures that actions are paused if they are consistently rejected, reducing negative customer experiences.

 Business Structure: Setting up the business structure to Sales/CreditCards ensures that the actions and offers are correctly categorized and managed within the appropriate business context.

 Real-Time Container: Creating and configuring a real-time container is necessary for presenting the credit card offer to customers in real-time as they log in to the website.

 Action and Web Treatment: Defining the action and configuring its web treatment ensures that the offer is ready to be displayed to the customers, complete with the necessary details and presentation format.

In a Prioritize component in Pega Customer Decision Hub, the top action is determined based on the propensity value. Propensity refers to the likelihood that a customer will respond positively to a given action. Here’s a detailed explanation:

Definition of Propensity:

Propensity is a score that indicates the probability of a customer taking a desired action, such as accepting an offer or responding to a campaign. It is calculated using predictive models that analyze historical data and customer behavior.

Role in Prioritize Component:

The Prioritize component in a decision strategy uses the propensity values to rank actions. Actions with higher propensity values are given higher priority, ensuring that the most relevant and likely-to-succeed actions are selected for the customer.

Configuration Steps:

Step 1: In the strategy canvas, add a Prioritize component.

Step 2: Configure the component to use the propensity value for ranking actions. This can be done by selecting the appropriate property that holds the propensity score, typically provided by a prediction model.

Step 3: Connect the Prioritize component to other components in the strategy to ensure the highest priority actions are selected and offered to the customer.

Benefits:

Using propensity to determine the top action helps in making data-driven decisions, increasing the chances of customer engagement and improving overall campaign effectiveness.

References:

Pega-Customer-Decision-Hub-User-Guide-85.pdf: "Understanding the Next-Best-Action strategy framework" section.

Pega documentation on "Creating and understanding decision strategies".

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.

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.

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.

 Requirement:

U+ wants to show a retention offer to customers likely to leave the bank based on historical interaction data.

 Model Types:

Entity Model: Used for recognizing entities within data.

Text Analytics Model: Used for analyzing text data.

Predictive Model: Used for making predictions based on historical data.

Adaptive Model: Used for real-time adaptation based on new data.

 Suitable Model:

For predicting customer churn (likelihood of leaving), a predictive model is best suited because it leverages historical data to make future predictions.

 Verification from Pega Documentation:

Pega documentation on leveraging predictive models for customer retention and churn analysis.

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.

 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.

 Understanding the Context: Myco, a telco, wants to introduce new plans without handsets. Currently, their business structure under the sales issue/plans group contains actions that are handsets.

 Business Structure: In Pega Customer Decision Hub, the business structure is organized into issues and groups.

 Action Naming: To reflect the new situation where plans do not include handsets, the term "handsets" needs to be changed.

 Level of Change: The appropriate level for renaming the plans to reflect this new structure is at the Issue level. At this level, the business context for actions is defined, and changes here will apply to all actions under that issue.

 Reference: As per the documentation, changes to business issues and groups should be done at the issue level to ensure that the new context is accurately represented across all relevant actions​​.

In the always-on outbound customer engagement paradigm, leveraging AI and continuous decisioning is crucial:

AI-Driven Decisioning:

Step 1: Configure the primary schedule to run daily, allowing the AI engine to continuously evaluate and select the best action for each customer.

Step 2: The AI considers engagement policies, customer behavior, and contextual data to determine the most relevant action.

Benefits of Always-On Engagement:

Continuous evaluation of customer interactions ensures that the actions presented are timely and contextually relevant.

It allows for dynamic adjustment of offers based on real-time data and customer responses.

Implementation Steps:

Step 1: In Next-Best-Action Designer, set up a primary schedule to run daily.

Step 2: Define engagement policies and configure AI models to select actions based on customer interactions and preferences.

Step 3: Allow the AI to decide whether to resend the original offer or present a new offer based on the customer’s response or lack thereof.

Example:

If the customer ignores the first email, the AI might decide to send a reminder email or a different offer based on updated engagement data and policy rules.

References:

Pega-Customer-Decision-Hub-User-Guide-85.pdf: "Configuring a recurring schedule for Next-Best-Action" section.

Pega documentation on "Using AI in decisioning".

To use a customer property in an expression within a decision strategy in Pega, follow these steps:

Identify the Customer Property:

Determine the specific customer property you want to use in your strategy.

Prefix with 'Customer':

In Pega, customer properties are accessed by prefixing them with the keyword Customer. This ensures that the property is correctly referenced from the customer data.

Use the Property in Expression:

When defining an expression in a decision strategy, include the Customer prefix. For example, if the property is Age, use Customer.Age.

Verify Property Definition:

Ensure that the property is correctly defined in the data model and accessible within the strategy context.

References:

Pega Customer Decision Hub User Guide 8.6: Understanding the Context Dictionary, Configuring the Context Dictionary​​.

When a customer is offered an action they have already accepted, it is typically because the decision strategy has not been configured to exclude such offers:

Strategy Customization:

Step 1: Ensure that the decision strategy includes logic to filter out actions that the customer has already accepted.

Step 2: Use customer interaction history to identify previously accepted offers and exclude them from the list of eligible actions.

Implementation Steps:

Step 1: Add a filter component in the strategy to check the customer’s interaction history for accepted offers.

Step 2: Configure the filter to exclude any actions that match the accepted offers.

Step 3: Connect this filter to the rest of the strategy to ensure that only new or unaccepted actions are considered.

Benefits:

Customizing the strategy to exclude previously accepted offers improves customer experience by preventing redundant or irrelevant offers.

It ensures that customers are always presented with new and relevant opportunities, enhancing engagement and satisfaction.

Example:

If a customer has already accepted a credit card offer, the strategy should filter out this offer in future interactions to avoid presenting the same offer again.

References:

Pega-Customer-Decision-Hub-User-Guide-85.pdf: "Configuring Next-Best-Action Designer for Pega Customer Service" section.

Pega documentation on "Creating and understanding decision strategies".

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.

 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.

 Ensuring the Email Channel is Enabled:

Navigate to the Channels tab in the Next-Best-Action Designer.

Ensure that the toggle for the Email channel is set to ON.

This action enables the email channel, making it available for use in Next-Best-Action strategies.

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.

 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 .

To configure an ethical bias policy that allows no bias based on gender, use the 0.9-1.11 Rate Ratio:

Understanding Rate Ratios:

Step 1: Rate ratios are used to detect and measure bias in decision-making processes.

Step 2: A rate ratio of 1 indicates perfect parity, meaning there is no bias between groups.

Configuring Bias Detection:

Step 1: In Pega Customer Decision Hub, you can configure bias detection by setting an acceptable rate ratio range.

Step 2: To ensure no bias based on gender, set the rate ratio to a narrow range close to 1, such as 0.9-1.11. This range ensures that the decisioning process treats different genders fairly, with minimal deviation.

Implementation:

Step 1: Navigate to the Bias Detection settings within the Decision Strategy framework.

Step 2: Set the rate ratio to 0.9-1.11 for gender to monitor and enforce non-discriminatory practices in presenting offers.

Example:

MyCo can use this configuration to ensure that their mobile data package offers for students do not exhibit gender bias, providing equal opportunities for both male and female students.

References:

Pega-Customer-Decision-Hub-User-Guide-85.pdf: "Complying with policies or regulations by detecting unwanted bias" section.

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'.