PEGAPCDC87V1 Questions and Answers

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.

If the volume constraint shown in the image (Maximum 75 Daily with Channel: Action: StandardCard, Maximum 50 Daily with Channel: Email, Maximum 50 Daily with Channel: SMS) is applied, the outbound run will deliver 75 actions in total. This includes actions across different channels, adhering to the specified limits.

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 .

Correct Action Hierarchy Levels:

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Action Hierarchy Level: Action

Retention

Action Hierarchy Level: Business Issue

Tablets

Action Hierarchy Level: Group

Service

Action Hierarchy Level: Business Issue

Change address

Action Hierarchy Level: Action

Proactive retention

Action Hierarchy Level: Group

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Action: This is a specific offering or initiative that can be presented to customers. It is a specific action within the broader strategy.

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.

A solid arrow from a "Set Property" component to a "Filter" component in a Pega decision strategy indicates that a property set by the "Set Property" component is being referenced or used by the "Filter" component. This relationship ensures that the data set in the "Set Property" component influences the filtering criteria in the "Filter" component, allowing for more dynamic and contextually relevant decisions.

To automatically pause actions that are shown too often for a specific time period, you need to define suppression rules in Pega Customer Decision Hub. Suppression rules allow you to put an action on hold after a specific number of outcomes, such as views or clicks, have been recorded over a defined period. This helps prevent overexposure and ensures a better customer experience by limiting how frequently the same action is shown to a customer.

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.

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.

To ensure that Silver credit cards are not offered to customers under 27 years of age and that Platinum cards are offered only to customers who had a positive balance in the last year, you need to configure "Engagement policies" in the Next-Best-Action Designer. Engagement policies allow you to set criteria such as eligibility, applicability, and suitability for actions. For this scenario, you would set the eligibility criteria to ensure the age and balance conditions are met before the offers are presented.

To present offers based on the responses of a test group that already received the offer, you should configure the propensity factor in Pega Customer Decision Hub. Propensity models predict the likelihood of a customer accepting an offer based on historical data and responses from similar customers. By leveraging propensity, you can ensure that offers are presented to customers who are most likely to respond positively, based on the behavior of the test group.

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.

 Translating Traditional Segments: When moving away from traditional marketing to always-on outbound capabilities in Pega Customer Decision Hub, translating traditional segments is essential to identify the target audience correctly.

 Segmentation Configuration: In Pega, segmentation rules are configured to define and group customers based on various attributes. This allows the system to identify the right target audience for outbound campaigns.

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 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.

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.

 Requirement:

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

 Model Types:

Entity Model: Used for recognizing entities within data.

Text Analytics Model: Used for analyzing text data.

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

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

 Suitable Model:

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

 Verification from Pega Documentation:

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

Customer Lifetime Value (CLV) - The bank wants to offer a discount based on the customer's CLV.

Filter Component - The Filter component is used in decision strategies to include or exclude actions based on specific criteria, such as CLV.

Implementation:

Add a Filter component in the decision strategy.

Configure the filter to check if the CLV value is greater than 400.

If true, proceed with the 10% discount offer.

 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.

Correct Order of Implementation:

Create a change request

Define action details and engagement policies

Rank the change request

Perform persona testing

Approve the change request

Complete and deploy the revision

Step by Step Comprehensive Detailed Explanation with References:

Create a change request:

The first step is to create a change request within the Business Operations Environment. This involves initiating a request for the new offer and specifying what needs to be changed or added.

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.

 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​​.

 Understanding the Requirement:

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

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

 Pega Scenario Planner:

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

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

 Running the Simulation:

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

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

 Steps to Use Scenario Planner:

Log in to the Pega Customer Decision Hub portal.

Navigate to Discovery -> Scenario Planner.

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

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

 Verification from Pega Documentation:

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

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.

 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.

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.

 Context:

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

 Available Outbound Channels:

Mobile Application: Typically considered an inbound channel.

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

SMS: Commonly used outbound channel for direct customer communication.

Email: Widely used outbound channel for direct customer communication.

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

 Selection:

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

 Verification from Pega Documentation:

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

Interaction History Summary - This rule aggregates data like customer responses, action outcomes, and interactions over a set time period.

Steps to Create the Rule:

In Dev Studio, go to Configure -> Decisioning -> Decisions -> Data Sources -> Interaction History Summaries.

Create a new summary rule.

Set the time period to 20 days.

Define the interaction data to be tracked (e.g., accepts, rejects).

Benefits - Enables precise tracking of customer engagement and responses, facilitating better decision strategies based on historical data.

 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​​.

 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.

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.

 Pega allows for the personalization of email subjects using customer data and expressions.

 This feature enhances the relevance and engagement of the email content.

 For example, using "Dear " in the subject line can increase open rates and customer engagement.

 Reference: Pega-Customer-Decision-Hub-User-Guide-87.pdf, section on configuring email treatments and personalizing content .

 Introduction to Group By and Filter Components:

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

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

 Dotted Line Relationship:

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

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

 Functionality Explanation:

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

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

 Verification from Pega Documentation:

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

 Ethical Bias Policy Configuration:

To allow minimal bias on age while ensuring eligibility (Age >= 18), an appropriate Gini coefficient threshold needs to be set.

The Gini coefficient measures statistical inequality, with 0 representing perfect equality and higher values indicating more bias.

 Choosing the Gini Threshold:

A 0.1 Gini coefficient is a low threshold that allows minimal bias.

It ensures that the actions are distributed fairly among customers while still respecting the eligibility condition.

 Detailed Explanation:

Setting a 0.1 Gini coefficient allows for slight variations in distribution, which is sufficient to accommodate the Age >= 18 requirement without introducing significant bias.

 Verification from Pega Documentation:

The Pega Customer Decision Hub User Guide explains the use of Gini coefficients for measuring and setting thresholds to control bias in decisioning strategies​​.

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

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

To limit the number of emails sent to customers irrespective of past outcomes with a particular offer and customer, customer contact limits should be implemented. Customer contact limits help control the frequency of communications with customers across different channels, ensuring that they do not receive an excessive number of messages.

In designing the next-best-action prioritization expression that balances customer needs with business objectives, you need to consider "Predicted customer behavior." This factor involves using predictive models to forecast how customers are likely to respond to different actions, ensuring that the recommended actions are both relevant to the customer and aligned with business goals.

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

Correct Engagement Policy Configuration:

Age

Engagement policy: Group-level eligibility

Opt-out

Engagement policy: Group-level applicability

Debt-to-income ratio

Engagement policy: Action-level suitability

Age

Group-level eligibility: This criterion determines whether the customer is eligible to receive any credit card offers. Eligibility conditions ensure that only customers meeting the basic criteria (e.g., being above the age of 18) are considered for the offers. This condition applies to all credit card offers within the group.

To temporarily suspend sending outbound messages and instead write the selected customers and action details to a database table for later offline processing, the most efficient way is to update the Send shape with Finalization in all the action flows. This approach allows the system to complete the processing of actions without sending the messages, and instead, store the necessary details in a database for later use.

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.

Step by Step Comprehensive Detailed Explanation with References:

Defining Property Constraint:

A property constraint can be used to limit the number of VISA card offers sent in an outbound run.

This constraint is based on the card_type action property, ensuring that only a specified number of VISA card offers are included.

This method allows for precise control over the types of actions being sent, adhering to business requirements.