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

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​​.

 Ensuring the Email Channel is Enabled:

Navigate to the Channels tab in the Next-Best-Action Designer.

Ensure that the toggle for the Email channel is set to ON.

This action enables the email channel, making it available for use in Next-Best-Action strategies.

Scorecard Rule - A scorecard in Pega is used to calculate a score based on various customer attributes. To include a new attribute (customer income), the scorecard rule must be updated.

Steps to Update Scorecard:

Navigate to the scorecard rule in Dev Studio.

Add a new input for customer income.

Define the scoring logic for the new input.

Save the updated scorecard.

Decision Strategy - The decision strategy that uses this scorecard must also be updated to ensure it correctly utilizes the new score calculations.

Steps to Update Decision Strategy:

Locate the decision strategy in the Next-Best-Action Designer.

Ensure the updated scorecard is referenced correctly.

Adjust any logic or thresholds that depend on the scorecard output.

Test the strategy to ensure it works as expected with the updated scorecard.

 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.

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 .

 Placement Type in Treatment Design: To present the Rewards Card offer on the top right of the customer’s account page, you should use the "Tile" placement type. This type is specifically designed to position content in a compact, visually distinct block that can be placed in specific areas of a webpage, such as the top right corner.

 Implementation: In the treatment configuration within Pega, you can specify the placement type as "Tile" to ensure the offer is displayed correctly on the account page.

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.

 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 .

 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​​.

 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.

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".

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.

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.

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

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

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

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

 Arbitration Factors: In Pega, arbitration is the process of prioritizing actions based on multiple factors.

 Action Value: This is a specific arbitration factor that allows assigning financial values to actions.

 Purpose: Action value helps in determining the prioritization of actions based on their financial impact or benefit to the business.

 Reference: According to Pega Customer Decision Hub documentation, the action value is indeed the term used for this specific arbitration factor​​.

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.

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.

 Context: A bank wants to use the Next-Best-Action capability of Pega Customer Decision Hub to promote offers on their website.

 Real-time Container Request: When a real-time container request is made, Pega Customer Decision Hub processes this request.

 Response Information: The response sent back to the website includes comprehensive details about the actions.

 Details Provided: According to Pega documentation, the response to a real-time container request includes treatment details (like images and descriptions) and relevant action attributes that personalize the offer for the customer​​.

In Pega Customer Decision Hub, the characteristics of an action are defined by using properties. Properties are used to capture specific details about an action, such as the type of offer, the conditions under which it should be presented, and other relevant data that influence decision-making. These properties ensure that the actions are personalized and relevant to the customer.

 Volume Constraint Application: Given the volume constraints of a maximum of 100 daily for the StandardCard action, and 75 daily for both Email and SMS channels, the total actions delivered would be limited to the smallest constraint applicable to each channel.

 Calculation:

The StandardCard constraint allows 100 actions per day.

The Email constraint allows 75 actions per day.

The SMS constraint allows 75 actions per day.

Therefore, the total actions delivered = 100 (since the StandardCard action is constrained to 100 but both email and SMS actions can only reach 75 each).

 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​​​​.

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

Understanding the Portals:

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

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

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

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

Steps to Create a Change Request:

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

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

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

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

Benefits:

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

Example:

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

References:

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

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

 Understanding the Context: Myco, a telco, wants to introduce new plans without handsets. Currently, their business structure under the sales issue/plans group contains actions that are handsets.

 Business Structure: In Pega Customer Decision Hub, the business structure is organized into issues and groups.

 Action Naming: To reflect the new situation where plans do not include handsets, the term "handsets" needs to be changed.

 Level of Change: The appropriate level for renaming the plans to reflect this new structure is at the Issue level. At this level, the business context for actions is defined, and changes here will apply to all actions under that issue.

 Reference: As per the documentation, changes to business issues and groups should be done at the issue level to ensure that the new context is accurately represented across all relevant actions​​.

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.

Set-Property Component: This component performs the calculation of profit for each of the four brands (Nike, Adidas, Puma, Reebok). Since the Set-Property component is configured to calculate profit for each brand individually, it processes four inputs, resulting in four outputs.

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.

Aggregation components in Pega Customer Decision Hub provide the ability to make calculations based upon a list of actions. These components are used to perform operations such as summing values, averaging scores, or other statistical calculations that aggregate data from multiple actions. This is crucial for generating insights and making informed decisions based on the collective data of several actions.

 Configuring Ethical Bias Policy:

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

 Selecting Bias Fields:

Bias fields such as age, gender, and ethnicity are typically customer properties.

These properties are analyzed to detect and measure bias in decision strategies.

 Detailed Explanation:

By selecting the Customer context, you can add and configure relevant customer properties to the bias policy.

This allows for comprehensive bias testing and ensures fairness in action distribution.

 Verification from Pega Documentation:

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

To 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.

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.

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.

To use a customer property in an expression within a decision strategy in Pega, follow these steps:

Identify the Customer Property:

Determine the specific customer property you want to use in your strategy.

Prefix with 'Customer':

In Pega, customer properties are accessed by prefixing them with the keyword Customer. This ensures that the property is correctly referenced from the customer data.

Use the Property in Expression:

When defining an expression in a decision strategy, include the Customer prefix. For example, if the property is Age, use Customer.Age.

Verify Property Definition:

Ensure that the property is correctly defined in the data model and accessible within the strategy context.

References:

Pega Customer Decision Hub User Guide 8.6: Understanding the Context Dictionary, Configuring the Context Dictionary​​.

The best way to organize customer actions applicable to various business issues in Pega Customer Decision Hub is into a three-level hierarchy: Business issue > Group > Actions. This structure allows for clear categorization and management of actions based on their business purpose and relevance. The "Business issue" represents the high-level goal (e.g., Retention, Acquisition), "Group" categorizes the actions under each issue (e.g., Credit Cards, Mortgages), and "Actions" are the specific offers or recommendations.

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.

Designing Reusable Action Flows:

When creating an action flow intended for reuse, it's important to configure it with specific treatments.

The Specify Treatment option allows the flow to dynamically apply the appropriate treatment based on the action context.

This approach ensures consistency and flexibility, making it easier to manage and update the flows.

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).

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

To enable access to customer properties such as age, income, etc., in a decision strategy, the Data Import component is required. This component imports the relevant customer data into the strategy so it can be utilized for decision-making processes.

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".

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.

Correct Engagement Policy Configuration:

The customer must not be flagged as a defaulted borrower

Engagement policy: Eligibility

Customer already owns a higher-value offer

Engagement policy: Applicability

Must be a resident of New York City

Engagement policy: Eligibility

The savings product offers are relevant only if the customer has not explicitly opted out of direct marketing

Engagement policy: Applicability

Inappropriate for customers with a credit score < 300 as they are likely to default

Engagement policy: Suitability

The customer must not be flagged as a defaulted borrower

Eligibility: Eligibility conditions determine whether a customer qualifies for an offer at a basic level. Being flagged as a defaulted borrower is a fundamental criterion to disqualify a customer from receiving certain offers.

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

Updating Scorecard in Decision Strategy: When a scorecard rule is updated to include a new property such as customer income, it is essential to reflect this change in the decision strategy. The scorecard properties must be correctly mapped to ensure the new calculations are utilized.

To ensure that customers do not receive more than four promotional emails per quarter, you can use customer contact limits. This setting allows you to define the maximum number of contacts a customer can receive over a specific period, regardless of their responses to previous actions. By setting this limit, you can prevent overcommunication and enhance customer experience.

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

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