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

ï‚· 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.

ï‚· 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.

ï‚· 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.

ï‚· 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.

ï‚· 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​​​​.

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.

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".

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.

ï‚· 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.

ï‚· 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.

ï‚· 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.

ï‚· Verification from Pega Documentation:

Pega's documentation confirms that updates to scorecard rules are automatically integrated into decision strategies using those rules.

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

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

ï‚· 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​​.

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.

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.

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.

ï‚· 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.

ï‚· 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.

ï‚· Understanding Refreshable Segment Option:

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

ï‚· Functionality:

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

ï‚· Purpose:

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

ï‚· Verification from Pega Documentation:

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

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.

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

Enable the Email Channel:

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

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

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

Define the Primary Schedule:

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

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

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

Additional Considerations:

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

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

Example Implementation:

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

References:

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

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

To 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​​.

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.

ï‚· Requirement:

The bank wants to avoid sending promotional emails related to credit card offers to a customer if they have already received one within a limited time period.

ï‚· Configuring Suppression Policy:

A suppression policy needs to be defined to prevent over-contacting customers.

Suppression policies automatically put an action on hold after specific criteria are met, such as a customer having already received a promotional email.

This ensures that customers are not bombarded with repeated offers, improving customer experience and adhering to communication limits.

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.

ï‚· 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.

ï‚· Configuring Ethical Bias Policy:

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

ï‚· 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.

ï‚· 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.

ï‚· Verification from Pega Documentation:

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

When MyCo updates their scorecard to include a new property, such as customer annual income, in the calculation of the postpaid credit limit, the decision strategy itself does not need to be changed to reflect the updated scorecard. This is because the scorecard rule already encapsulates the logic for calculating the credit limit, including any new properties added.

Scorecard Rule Update:

The scorecard rule in Pega is responsible for computing scores based on various properties.

If the scorecard rule is updated to include a new property, the decision strategy referencing this scorecard automatically uses the updated scorecard without requiring any additional changes to the strategy itself.

Decision Strategy:

The decision strategy references the scorecard rule, and as long as the reference remains unchanged, any updates to the scorecard are automatically applied.

There is no need to remap properties, add proposition filters, or set new properties within the strategy.

Implementation:

Ensure that the scorecard rule is correctly updated and tested to include the new property.

The decision strategy continues to use the updated scorecard without any modifications.

Identify the Requirement: U+ Bank wants to offer credit cards only to customers with specific risk segments and also ensure that the age of the customer is greater than 25 years. This involves setting eligibility criteria for credit card offers.

Understand Engagement Policies: Engagement policies in Pega CDH are used to define rules that govern when specific actions are relevant for a customer. This includes eligibility, applicability, and suitability conditions.

Access Next-Best-Action Designer: To implement these criteria, navigate to the Next-Best-Action Designer within Pega Customer Decision Hub.

Engagement Policy Tab:

Eligibility Criteria: Set the criteria to include customers with an age greater than 25 years.

Define the other conditions based on Credit Score and Average Balance to segment customers into Good, Fair, Poor, and Very Poor risk categories.

According to the Pega Customer Decision Hub User Guide, the engagement policy tab allows users to define when specific actions or groups of actions are appropriate for customers (Reference: Pega-Customer-Decision-Hub-User-Guide-85.pdf, Chapter on "Understanding Next-Best-Action Designer engagement policies").

Implementing the Criteria:

Navigate to the Engagement Policy tab: This is within the Next-Best-Action Designer.

Add a New Policy: Create a new engagement policy where you define the eligibility criteria for the credit card offer.

Set Age Condition: Ensure the age condition (Age > 25) is part of the eligibility criteria.

Validation:

Ensure the conditions are tested thoroughly to verify that the credit card offers are only presented to customers meeting the set criteria.

Additional Resources:

For detailed steps on configuring engagement policies, refer to the Pega documentation on "Configuring engagement policies for actions" (Reference: Pega-Customer-Decision-Hub-User-Guide-85.pdf).

Conclusion: The best practice to ensure that credit cards are offered only to customers with age greater than 25 is to use the Engagement Policy tab in the Next-Best-Action Designer, where you can set the eligibility conditions appropriately.

ï‚· 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.

ï‚· 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.

ï‚· Conclusion:

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

ï‚· Context:

U+ Bank wants to use outbound communication channels for Next-Best-Action recommendations.

ï‚· Available Outbound Channels:

Mobile Application: Typically considered an inbound channel.

Google: Used for online advertising but not a direct outbound channel.

SMS: Commonly used outbound channel for direct customer communication.

Email: Widely used outbound channel for direct customer communication.

Contact Center: Usually an inbound channel where customers call in.

ï‚· Selection:

SMS and Email are the most suitable outbound channels for sending Next-Best-Action recommendations directly to customers.

ï‚· Verification from Pega Documentation:

Pega documentation on configuring and using SMS and Email as outbound channels for Next-Best-Action delivery.

ï‚· 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.

ï‚· 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.

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

ï‚· Understanding the Scenario Planner Output:

The image shows the projected reach and projected responses for several credit card offers, including the Standard card.

The "Projected responses" column indicates the number of customers expected to respond positively to each offer.

ï‚· Interpreting the Data for the Standard Card:

For the Standard card, the "Projected responses" is 21, with a change indicator showing a decrease of 7.

This decrease is factored into the projected response, meaning the expected number of positive responses after considering the applied business weight.

ï‚· Step-by-Step Calculation:

The base projected responses for the Standard card is 21.

The decrease of 7 has already been factored into this projection.

Therefore, the number of customers likely to accept the Standard card remains 21, as it represents the final projected response after adjustments.

ï‚· Verification from Pega Documentation:

The Scenario Planner in Pega Customer Decision Hub provides these projections to help visualize the impact of business weight adjustments and other lever changes on customer actions.

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.

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.

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

Definition of Propensity:

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

Role in Prioritize Component:

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

Configuration Steps:

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

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

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

Benefits:

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

References:

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

Pega documentation on "Creating and understanding decision strategies".

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).

Business Requirement: The need is to implement risk segmentation rules based on age and credit score.

Decision Table Integration: The decision table can be added to the decision strategy where it will evaluate the credit score and age, and then assign the appropriate risk segment.

Parameter Passing: The decision table within the strategy will use the credit score passed to it as a parameter to determine the segmentation.

References: Pega's methodology for incorporating decision tables into strategies for evaluating and segmenting customers based on specific criteria is well documented in the user guides .

ï‚· 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.

ï‚· 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.

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.

ï‚· 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.

ï‚· 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.

ï‚· 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.

ï‚· 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.

ï‚· 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​​​​.

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).

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

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 .

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.

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.

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.

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.

Value Finder in Pega Customer Decision Hub identifies under-served customer segments and provides recommendations to optimize engagement policies. It helps in understanding how different customer groups are being served and suggests adjustments to engagement conditions to ensure a more inclusive and effective customer outreach.

Correct Engagement Policy Level Configuration:

Criterion 1: Owns a smart mobile

Engagement policy level: Group level

Criterion 2: Under graduation student / Post graduation student

Engagement policy level: Issue level

Criterion 3: New customer / Existing customer

Engagement policy level: Action level

To configure engagement policies correctly in Pega, it is important to choose the appropriate level of engagement policy based on the criteria. The levels are:

Issue level: Applies to all actions within a business issue.

Group level: Applies to all actions within a specific group.

Action level: Applies to individual actions.

1. Criterion 1: Owns a smart mobile

This criterion is relevant for all data plans under a specific group of data plans.

Therefore, it is best suited at the Group level, ensuring that all plans within the group check for this criterion.

2. Criterion 2: Under graduation student / Post graduation student

These educational statuses may be relevant across various groups and actions under a broader business issue like "Education" or "Student Plans".

Therefore, this should be configured at the Issue level to apply across all relevant actions within the issue.

3. Criterion 3: New customer / Existing customer

This criterion is specific to individual actions and can vary greatly between different offers.

Hence, it should be configured at the Action level to ensure precise control over which actions are offered to new versus existing customers.

Understanding Aggregation Components: Aggregation components in Pega decision strategies are used to perform calculations based on a list of actions. This can include summing values, calculating averages, or other statistical measures.

Use Case of Aggregation Components:

Set a Text Value: This is typically handled by Set Property or Data Transform components.

Filter Actions: Filters are used for filtering actions based on priority and relevance.

Choose Between Actions: This is typically handled by a Decision or Filter component.

Aggregation is specifically used to perform calculations based on a group of actions. It can be used to sum the total value of actions, calculate the average propensity, etc.

Implementation in Decision Strategy:

Add Aggregation Component: In the decision strategy, add an Aggregation component.

Define Calculation: Specify the type of calculation (sum, average, count, etc.) and the actions to be included in the calculation.

The Pega Customer Decision Hub User Guide mentions the use of aggregation components to perform calculations on action lists, confirming their primary function is for making calculations based on a list of actions (Reference: Pega-Customer-Decision-Hub-User-Guide-85.pdf, Chapter on "Using aggregation components in decision strategies").

Conclusion: In a decision strategy, aggregation components are used to make calculations based upon a list of actions, enabling the strategy to derive meaningful metrics from groups of actions.