 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.

 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.

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.

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.

 Context: The bank wants to suppress mortgage offers if a customer ignores three offers within a group for one month.

 Suppression Rule: In Pega, suppression rules are defined to manage overexposure to customers.

 Implementation: The suppression rule should be set at the group level to ensure the entire group of mortgage actions is suppressed for 30 days after three rejections.

 Pega Configuration: The configuration for suppression rules supports this requirement by allowing the setting of a threshold for rejections across any channel, thus making option A the correct choice​​.

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.

 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.

 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.

 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​​​​.

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.

 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.

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.

Identifying the Requirement:

The bank uses Pega Customer Service in its contact center and wants to use CDH to determine the Next-Best-Action when a call is accepted by a service representative.

The task is to determine what pieces of information are used by Pega CDH to determine the Next-Best-Action recommendations.

Evaluating Pega CDH Capabilities:

Pega CDH integrates seamlessly with Pega Customer Service to leverage real-time customer data and contextual information.

The system uses a combination of call context (information specific to the current call) and customer profile (historical and demographic data) to make informed decisions.

Process Overview:

Call Context:

Includes details such as the reason for the call, current issues or inquiries, and any immediate needs or concerns expressed by the customer during the call.

Customer Profile:

Comprises historical interaction data, customer preferences, past purchases, and any predictive analytics that provide insights into customer behavior and needs.

Next-Best-Action Determination:

Pega CDH processes this combined data to generate the most relevant and timely action recommendations for the service representative to offer to the customer.

References and Supporting Documentation:

The Pega Customer Decision Hub User Guide outlines that the system uses both the call context and the customer profile to determine the Next-Best-Action in a contact center environment, ensuring personalized and contextually relevant interactions.

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 Scenario:

There are two segments: Segment A and Segment B.

Segment A references Segment B.

The goal is to ensure that Segment B is automatically refreshed when Segment A is refreshed.

Segment Refresh Settings:

Refreshable Segment: This setting determines if a segment can be refreshed automatically.

Refresh Child Segments: This setting ensures that when a parent segment is refreshed, all its child segments are also refreshed.

Configuration Requirement:

To achieve the automatic refresh of Segment B when Segment A is refreshed, you need to enable both the Refreshable option for Segment B and the Refresh Child Segments option for Segment A.

Implementation:

Segment A: Enable the Refresh Child Segments option.

Segment B: Enable the Refreshable option.

Correct Configuration:

The correct configuration is to set Segment A with Refresh Child Segments enabled and Segment B with Refreshable enabled.

 Requirement:

U+ Bank has added five new actions to the Credit Cards group and wants to enable these actions in the email channel.

 Configuring Each Action with an Email Treatment:

Each action must be configured with an appropriate treatment for the email channel.

Treatments define how the actions are presented in the channel, including the content and format of the email.

This step ensures that the actions are prepared to be sent via email with the correct content.

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

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

To 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.

To prevent the same offer from being shown repeatedly to a customer, the correct suppression rule is to suppress an action for 10 days if there are five impressions for the web channel in the last 14 days. This ensures that after a customer has been presented with the same offer five times within a 14-day period, they will not see that offer for the next 10 days, thereby preventing offer fatigue.

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.

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).

 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.

Proposition Data Components - These components in a decision strategy are used to import and reference actions or propositions.

Requirement - The strategy designer wants to import all actions from one group and a subset from another.

Minimum Number Calculation:

One component for importing all 10 actions from the Sales/Credit Cards group.

Another component for importing the 2 specific actions from the Sales/Mortgages 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.

 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.

 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.

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.

Goal of Next-Best-Action: The primary goal of Next-Best-Action is to balance customer needs with business objectives. This approach ensures that actions taken are beneficial for both the customer and the business, leading to better engagement and outcomes.

Enrichment decision components in Pega Customer Decision Hub provide the ability to set customer properties. These components are used to enhance the customer data within a decision strategy by adding or updating information about the customer. This allows for more personalized and contextually relevant decisions.

Definition and Purpose:

Enrichment components are used within decision strategies to add or update customer data, ensuring that the decisioning process has the most relevant and up-to-date information.

This can include setting properties like customer preferences, behaviors, or other attributes that might influence the decision outcome.

Types of Enrichment Components:

Set Property: This is the primary enrichment component used to set a value to a property within the strategy.

Data Import: Importing data from external sources to enrich the customer profile.

Data Transform: Applying transformations to data to format or manipulate it as needed within the strategy.

Usage in Decision Strategies:

When designing a decision strategy, enrichment components are typically used in the initial stages to ensure that all necessary customer data is available and accurate before proceeding to decision logic components.

For example, before evaluating eligibility or applicability, you might use an enrichment component to update the customer's credit score or recent transaction history.

Implementation Steps:

Step 1: Drag the enrichment component into the decision strategy canvas.

Step 2: Configure the component by selecting the property to set and defining the value or expression to be used.

Step 3: Connect the enrichment component to other components within the strategy to ensure the enriched data is utilized in subsequent decision-making steps.

Example:

Consider a scenario where a bank wants to offer personalized loan options based on the customer's recent financial activities. An enrichment component can be used to update the customer's profile with their latest transaction data, ensuring that the loan offer is tailored to their current financial status.

References:

Pega-Customer-Decision-Hub-User-Guide-85.pdf: "Understanding the Next-Best-Action strategy framework" section, which describes the use of enrichment components in decision strategies.

Pega documentation on "Creating and understanding decision strategies".

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

Task: Create a new action

Portal: Pega Customer Decision Hub

Task: Modify a decision table

Portal: Operations Manager

Task: Create a decision strategy

Portal: Pega Customer Decision Hub

Task: Edit an engagement policy for an action

Portal: Pega Customer Decision Hub

Task: Edit a group level engagement policy

Portal: Pega Customer Decision Hub

Task: Edit an email treatment for an action

Portal: Pega Customer Decision Hub

Create a new action:

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

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​​.

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

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

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

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​

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​

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.

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

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

Requirement Analysis: U+ Bank wants to display a specific credit card offer (Standard Card) to every customer who logs in to the bank website using Pega Customer Decision Hub.

Identify Mandatory Artifacts: To implement this requirement in Pega Customer Decision Hub, certain artifacts are necessary:

Business Structure: This defines the business hierarchy in Pega CDH, such as issues and groups. For this scenario, a relevant business issue and group must be defined (e.g., Credit Cards -> Standard Card).

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

To analyze the distribution of offers to well-engaged, under-engaged, or not-engaged customers, you would run the Pega Value Finder simulation. The Value Finder tool is designed to help identify and understand the engagement levels of different customer segments.

Pega Value Finder:

Pega Value Finder is used to identify under-served or over-served customers by analyzing historical interaction data.

It provides insights into how well different customer segments are being served and helps in understanding engagement levels.

This tool can categorize customers into well-engaged, under-engaged, or not-engaged based on their interactions and responses to offers.

Traditionally, segments were used to identify the target audience for a campaign. In the always-on approach, segments translate into engagement policies and AI. Engagement policies define when specific actions or groups of actions are appropriate for customers, while AI-driven decisions help determine the most relevant next best actions for individual customers based on predictive and adaptive analytics. This approach ensures that each customer interaction is highly personalized and contextually relevant.

 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​​.

One-to-One Marketing Channels: As the company transitions to a one-to-one marketing approach, leveraging various channels is essential to effectively present new offers like a data plan.

Retail Store: Offers can be presented directly to customers through in-store interactions, allowing personalized recommendations based on their profile and behavior.

Chatbot: Automated chatbots can engage customers in real-time on digital platforms, offering personalized data plans based on customer interactions and preferences.

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.

 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.

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.

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.

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".

To ensure that a customer does not see offers on their account page if they have already received three home loan offers in the last two weeks, you need to define customer contact limits. These limits allow you to control the number of times a customer can be contacted with specific types of offers within a given timeframe, thus preventing overexposure and enhancing customer satisfaction.

 Understanding the Requirement:

The bank wants to suppress a group of mortgage offers if a customer ignores (does not click on) three offers within 15 days.

The suppression rule needs to be defined for the web channel.

 Configuring Suppression Rule:

The suppression rule must track the number of impressions (views) and check if there are no clicks.

It needs to be configured to act after three such impressions over a specific time period (15 days).

 Steps to Configure:

Define the group of mortgage offers as a single entity.

Set up the suppression criteria to trigger after three impressions without clicks for the web channel.

Configure the suppression period to be 15 days.

 Verification from Pega Documentation:

According to Pega's guidelines on adding tracking periods and suppression rules, this configuration accurately reflects the bank's requirements.