Pegasystems PEGAPCDC87V1 Certified Pega Decisioning Consultant (PCDC) 87V1 Exam Practice Test

Next-Best-Action leverages customer lifecycle analysis to anticipate retention issues. By analyzing patterns and trends in customer behavior, Next-Best-Action strategies can proactively address potential retention problems and enhance customer engagement to prevent churn.

 Best Practice for Emails: To ensure that emails load quickly and correctly across various email clients, it's best practice to provide links to images rather than embedding them directly or attaching them. This method ensures better compatibility and avoids large email sizes.

 Implementation: Including image links allows the email client to fetch images from a server, ensuring that the email content remains lightweight and is displayed properly.

Understand the Scenario: A Customer Service Representative (CSR) presents an offer to a customer, and the customer rejects the offer.

Next Step in Pega CDH:

In Pega Customer Decision Hub, when an offer is rejected, the system typically reevaluates the Next-Best-Action. This means it reassesses the available actions to determine the most appropriate next step for the customer based on current context and customer data.

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​​.

To prevent offer overexposure, the appropriate suppression rule is to suppress a group of actions for 10 days if there are three clicks on the web channel in the last 10 days. This ensures that once a customer interacts with any mortgage offer three times within a 10-day period, they will not see any mortgage offers for the next 10 days. This rule helps in maintaining a good customer experience by avoiding overexposure.

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.

 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​​.

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​

 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.

 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.

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.

 Introduction to Group By and Filter Components:

The Group By component groups data based on specified properties, allowing aggregation of data points.

The Filter component is used to include or exclude actions based on specific criteria.

 Dotted Line Relationship:

In Pega's visual strategy canvas, a dotted line between two components indicates a reference relationship rather than a direct data flow.

When a dotted line connects a Group By component to a Filter component, it signifies that the Filter component is using a property defined or aggregated in the Group By component.

 Functionality Explanation:

For example, if the Group By component aggregates customer data by region, the Filter component can then use this aggregated data to filter actions for specific regions.

This relationship ensures that the filtering process considers the aggregated data, making the decision strategy more dynamic and contextually aware.

 Verification from Pega Documentation:

As outlined in Pega's documentation, a dotted line signifies that the Filter component references a property from the Group By component, enabling more complex and accurate decision-making processes.

 Understanding the Requirement:

Myco wants to understand the impact on revenue if a new data plan offer is introduced.

This involves simulating the potential effects of adding a new offer to the existing strategy.

 Pega Scenario Planner:

Scenario Planner is used to simulate "what-if" scenarios to forecast results and understand the impact of changes to the Next-Best-Action Designer configuration.

It allows businesses to compare the effects of different strategies and adjust levers to align with business objectives.

 Running the Simulation:

Create and run an audience simulation with the new data plan offer included.

Use Scenario Planner to compare the projected reach, accepts, and value capture with and without the new offer.

 Steps to Use Scenario Planner:

Log in to the Pega Customer Decision Hub portal.

Navigate to Discovery -> Scenario Planner.

Create a new simulation, including the new data plan offer.

Run the simulation and compare the results to understand the impact on revenue.

 Verification from Pega Documentation:

The Pega Customer Decision Hub User Guide explains how to use Scenario Planner to simulate business changes and understand their impact​​​​.

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.

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 define a suppression rule in Pega for the scenario where a group of mortgage offers should be suppressed for 15 days if a customer ignores three offers from the mortgage group, follow these steps:

Define the Suppression Rule:

Create a contact policy in Pega Customer Decision Hub to suppress the group of mortgage actions.

Specify the Condition:

Set the condition to suppress the group of actions if there are 3 rejects (ignored offers) for any channel. This means the suppression applies regardless of whether the offers were displayed on the web, mobile, email, etc.

Set the Suppression Duration:

Configure the suppression period to be 15 days. This ensures that the group of actions will not be shown to the customer for 15 days after they have ignored three offers.

Apply the Contact Policy:

Ensure the contact policy is associated with the relevant group of actions (mortgage offers) and that it is enforced by the strategy framework.

Test and Validate:

Test the suppression rule to verify it works as expected and that the offers are suppressed correctly after three rejects.

References:

Pega Customer Decision Hub User Guide 8.7: Adding more tracking time periods for contact policies, Creating a contact policy, Contact policy library​​.

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.

 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​​.

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.

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".

 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.

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

 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.

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.

 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.

 Understanding the Requirement:

Myco wants to find new opportunities to improve marketing during the ideation stage of a project for presenting post-paid offers.

This involves identifying gaps and underserved customer segments that can be targeted with new or improved offers.

 Pega Value Finder:

Pega Value Finder is designed to analyze the distribution of actions across different customer segments.

It helps identify customers who are not receiving any actions or are receiving only low-propensity actions, thus highlighting opportunities to improve the strategy.

 Running the Simulation:

In Pega Value Finder, you can run simulations to discover underserved customers.

The tool provides insights into customer groups that are not well-engaged, allowing businesses to adjust their strategies and create more relevant actions or treatments.

 Steps to Run Value Finder:

Log in to the Pega Customer Decision Hub portal.

Navigate to Discovery -> Value Finder.

Create a new simulation and select the relevant issue and group.

Run the simulation to analyze customer distribution and identify underserved segments.

 Verification from Pega Documentation:

The Pega Customer Decision Hub User Guide details the process and benefits of using Value Finder to discover gaps and opportunities in customer engagement strategies​​​​.

 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​​.

 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.

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​

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.

To update the underlying decision strategy of an engagement policy condition, the change request should be created in the 1:1 Operations Manager Portal. This portal is designed for business users to manage changes and revisions in a controlled environment.

Understanding the Portals:

1:1 Operations Manager Portal: This portal allows business users to manage and implement business changes, such as updating decision strategies and engagement policies.

Pega Customer Decision Hub Portal: This portal is used for overall customer decision management but not specifically for creating change requests.

Dev Studio Portal: This portal is more technical and used for developing and configuring applications.

Development Manager Portal: This is generally used for deployment and managing development pipelines.

Steps to Create a Change Request:

Step 1: Log into the 1:1 Operations Manager Portal.

Step 2: Navigate to the section where you can create a new change request.

Step 3: Specify the details of the change request, including the engagement policy and the decision strategy that needs to be updated.

Step 4: Submit the change request for approval and implementation.

Benefits:

Using the 1:1 Operations Manager Portal ensures that changes are tracked, reviewed, and approved through a formal process, maintaining governance and control over business operations.

Example:

U+ Bank wants to update the decision strategy for credit card offers. A change request is created in the 1:1 Operations Manager Portal, detailing the required updates to the engagement policy conditions.

References:

Pega-Customer-Decision-Hub-User-Guide-85.pdf: "Managing business-as-usual changes with Pega 1:1 Operations Manager" section.

Pega documentation on "Revision management and change request process".

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.

In response to the real-time container request by Customer Decision Hub, the U+ Bank website receives only relevant action details and its treatment details. This information includes the specifics of the action to be displayed (such as the offer) and the associated treatment (such as the banner or message), enabling the website to present personalized and contextually relevant offers to customers.

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.

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.

 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.

Contact Policy Conditions - U+ Bank has added all existing contact policy conditions, ensuring that offers comply with set business rules.

Unit Tests with Persona Testing - Before going live, it is essential to validate the implementation through persona-based unit tests. This helps verify that the strategy gives the expected results for different customer personas.

Process:

Design test personas that represent various customer segments.

Run tests to ensure that each persona receives appropriate offers.

Validate the expected outcomes, ensuring no regressions or policy breaches.

 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.

 Understand the strategy objective: The decision strategy is designed to output the most profitable shoe. The profit is calculated as Profit = Price - Cost.

 Evaluate the provided data:

Nike: Profit = $69 - $59 = $10

Adidas: Profit = $65 - $45 = $20

Puma: Profit = $85 - $65 = $20

Reebok: Profit = $75 - $50 = $25

 Determine the most profitable shoe: According to the calculations, Reebok has the highest profit ($25).

 Fill in the blank: Therefore, the output of the component in the blank space highlighted in red should be Reebok, which has the highest profit.

 Reference: The strategy design and output decision rules are outlined in the sections on "Understanding Next-Best-Action Designer arbitration" and "Next-Best-Action strategy framework components" in the Pega Customer Decision Hub User Guide​​.

To prevent overexposure and avoid sending the same promotional offer email for the next 60 days if the email was rejected twice by the customer in the last 30 days, you should define the suppression rule as "Suppress an action for 60 days if there are 2 rejects in the email channel in the last 30 days." This rule ensures that specific actions are paused based on the customer's past interaction history, preventing repetitive and potentially annoying communications.

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.

 Arbitration Factors: To prioritize customer needs in the decision strategy, adjusting the context weighting factor is essential. This factor takes into account the situational context of each action, thereby prioritizing actions that align with customer needs.

 Implementation: By boosting context weighting, you ensure that the decision strategy considers the specific context of customer interactions, leading to more relevant and satisfactory offer presentations.

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.

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.

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".

When implementing the Next-Best-Action paradigm to offer the new data plan, the mobile company should focus on "Customer relevancy and business profitability." This means that the company needs to ensure that the offers are relevant to individual customers based on their needs, preferences, and behavior, while also aligning these offers with the company's business objectives to ensure profitability. This customer-centric approach leverages Pega's predictive and adaptive analytics to personalize offers and maximize both customer satisfaction and business outcomes.

To manage the communication between Pega Customer Decision Hub and external channels for promoting new offers to each customer on their website, you need to configure a Real-time Container. Real-time Containers are used to deliver personalized content and actions to customers in real-time, based on their interactions with the website and other channels. They ensure that the most relevant offers are presented to each customer in a timely and contextually appropriate manner.

 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 .

 Propensity Increase: In Pega Customer Decision Hub, propensity indicates the likelihood of a customer responding positively to an offer. A propensity value of 0.83 signifies a high likelihood of customers showing interest in the Gold credit card offer.

 Customer Behavior Analysis: The increase to 0.83 suggests that similar customers have demonstrated interest, such as clicking on the offer or engaging with it in some way.