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

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

Conditions Evaluation:

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

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

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

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

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

 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.

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

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

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

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

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

To 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 implement the required changes for the mortgage offers, you should create two separate change requests in the 1:1 Operations Manager portal. This ensures each change is tracked and managed individually.

Update Suitability Condition for Fifteen-Year Fixed-Rate Mortgage Offer :

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

Step 2 : Create a change request for updating the suitability condition of the Fifteen-Year Fixed-Rate Mortgage offer.

Step 3 : Specify the details of the change, including the new suitability condition (Credit Score > 450).

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

Introduce a New Offer - Twenty-Year Fixed-Rate Mortgage :

Step 1 : In the 1:1 Operations Manager Portal, create a new change request for introducing the Twenty-Year Fixed-Rate Mortgage offer.

Step 2 : Define the new offer, including its eligibility, applicability, and suitability conditions (Credit Score > 600).

Step 3 : Configure the necessary treatments and engagement policies for the new offer.

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

Best Practices :

Creating separate change requests ensures that each modification is properly documented and approved, reducing the risk of errors and making it easier to track changes.

This approach also allows for independent testing and validation of each change before it goes live.

References :

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

Pega documentation on " Creating change requests in 1:1 Operations Manager " .

By following these steps, U+ Bank can effectively manage the changes to their mortgage offers, ensuring that both the updated suitability condition and the new offer are correctly implemented.

 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.

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.

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.

 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.

 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​​.

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.

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.

 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.

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 " .

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.

Action Flow Template: To implement the requirement of using the same action flow template for all credit card actions, configure the email treatment. This allows you to define the desired flow pattern and reuse it across different credit card actions.

Configuration: Create an email treatment template that includes the specific flow pattern, and then apply this template to all the relevant credit card actions.

 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.

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 " .

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

To 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.

 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.

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

 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.

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.

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.

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

 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​​.

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

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

Modify the Strategy Framework :

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

Steps to Modify :

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

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

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

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

Testing and Validation :

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

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

To address the issue where customers have ignored a particular mortgage offer and the bank wants to offer the action 30% more frequently, we need to configure the appropriate arbitration factor in Pega Customer Decision Hub.

Arbitration Overview : Arbitration in Pega CDH determines how actions are prioritized and selected for presentation to the customer. It involves factors like business value, propensity, and action weighting.

Action Weighting : Action weighting is used to adjust the frequency and priority of specific actions. By increasing the action weighting, you effectively make the action more likely to be selected during the arbitration process.

Implementation :

Navigate to the Next-Best-Action Designer in Pega CDH.

Locate the action for the mortgage offer that needs increased frequency.

Adjust the action weighting to increase by 30%. This adjustment modifies the priority calculation, making the action more likely to be selected.

 Introduction to Prioritize Component :

The Prioritize component in Pega Decisioning is used to order actions based on specific criteria.

This component evaluates the importance or priority of different actions to determine which should be executed first.

 Understanding Propensity :

Propensity is a prediction of the likelihood that a customer will respond positively to a particular action.

It is a key factor in decision-making processes as it reflects the customer ' s predicted behavior based on past interactions and other data.

 Role of Propensity in Prioritize Component :

The primary function of the Prioritize component is to rank actions.

This ranking is typically based on the propensity value, which is calculated using predictive models.

The action with the highest propensity is considered the top action because it has the highest predicted likelihood of a positive customer response.

 Verification from Pega Documentation :

According to Pega ' s guidelines on creating and understanding decision strategies, propensity is a standard and recommended metric for prioritizing actions within the Prioritize component.

Pega Customer Decision Hub can be applied to various types of decisions, including determining how to retain a customer. It leverages decision management and AI technologies to analyze customer data, predict behaviors, and recommend personalized actions that can help improve customer retention and satisfaction.

 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.

To present offers from both groups and arbitrate across the two groups to select the best offer based on customer behavior, you need to map a real-time container to the Top-level or Issue-level in the Next-Best-Action Designer. This mapping allows the system to evaluate and arbitrate offers from different groups based on the defined criteria and customer behavior, ensuring the most relevant offers are presented.

Decision Table Results: In Pega, the standard property that holds the result of a decision table evaluation is pxOutcome .

Property Usage: This property can be used to reference the result of the decision table within a decision strategy.

References: This usage of pxOutcome is consistent with Pega ' s approach to handling results from decision tables and mapping these results into strategies .

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​

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.

 Volume Constraint Reset Interval: To meet the requirement of giving away phone accessories in batches of 50 and promoting a new batch once the stock in a batch is completed, you should select the " Manual " reset interval. This setting allows manual control over when the constraint is reset, enabling the business to start a new batch promotion as needed.

 Implementation: Configure the volume constraint for the promotional emails with the manual reset interval to ensure controlled batch processing.

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

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

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

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

To 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.

To present customer-centric mobile internet offers on Facebook, MyCo should place a paid ad. This action ensures that the offers are targeted and displayed to the relevant audience on the social media platform, leveraging Pega Customer Decision Hub’s integration with paid media channels like Facebook Ads.

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.

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​​.

 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.

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

Engagement Policy Condition :

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

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

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

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

To limit the number of emails sent to customers irrespective of past outcomes with a particular offer and customer, you can implement customer contact limits. Customer contact limits are used to control the number of interactions a customer can receive over a specific period on a particular channel, ensuring that customers are not overwhelmed with too many messages.

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 " .