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

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.

To determine which of the new credit card offers (Diamond, Platinum, and Gold) will be the best performing action, the Action Distribution Report is used. This report helps in running a distribution test to analyze how the different offers are being distributed among the customer base, providing insights into which offer might perform the best based on initial distribution and eligibility criteria.

With the volume constraints applied (75 maximum daily actions for StandardCard, 50 maximum daily actions for Email, and 50 maximum daily actions for SMS), the outbound run will deliver a total of 75 actions. This includes actions across different channels while adhering to the specified constraints.

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.

 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.

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.

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.

 Requirement:

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

 Configuring Each Action with an Email Treatment:

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

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

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

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

 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.

 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.

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.

In a call-center application that receives Next-Best-Actions from Pega Customer Decision Hub, the customer service representative (CSR) is guided on the next important conversation to have with the customer. The Next-Best-Action recommendations help CSRs make informed decisions and provide relevant, personalized service to customers by suggesting the most appropriate actions based on the customer's profile, interaction history, and current context.

 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.

 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.

 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.

 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.

 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.

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.

To estimate the business value created by the subsequent next-best-action run using the selected configuration, you should run a simulation using Pega Scenario Planner. This tool allows you to project the potential business impact and value of different next-best-action scenarios, providing insights into the expected outcomes based on the configured strategies.

 Understanding Refresh the Audience Option:

This option ensures the decision process uses the most current data available.

 Functionality:

Before each run of the schedule, the audience data is refreshed to reflect any changes or updates in customer information.

 Purpose:

This ensures decisions are made based on the latest customer profiles, improving the relevance and accuracy of recommendations.

 Verification from Pega Documentation:

Pega documentation on scheduling and audience refresh options for ensuring up-to-date customer data is used.

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

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".

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.

Referencing Customer Properties: In Pega decision strategies, customer properties are accessed by prefixing the property name with "Customer." This convention helps in clearly identifying the context of the properties being used.

To implement the change in the threshold value for the USD 2000 credit limit from “<175” to “<200”, as a Strategy Designer, you should change the cutoff value in the results tab of the scorecard decision component. This adjustment ensures that the decision logic used to automatically approve credit card limits reflects the new threshold, thereby updating the conditions under which customers qualify for the specified credit limit.

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​​.

 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.

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

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

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

Implementation:

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

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

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

 Volume Constraints Implementation: To implement a policy that limits the sending of more than 500 emails per day, you need to configure volume constraints. Volume constraints allow you to set limits on the number of actions delivered within a specified period.

 Configuration: Create a volume constraint rule and set the maximum volume to 500 emails per day.

 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​​.

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.

Task: Create a new action

Portal: Pega Customer Decision Hub

Task: Modify a decision table

Portal: Operations Manager

Task: Create a decision strategy

Portal: Pega Customer Decision Hub

Task: Edit an engagement policy for an action

Portal: Pega Customer Decision Hub

Task: Edit a group level engagement policy

Portal: Pega Customer Decision Hub

Task: Edit an email treatment for an action

Portal: Pega Customer Decision Hub

Create a new action:

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

Understand the Scenario: A Customer Service Representative (CSR) presents an offer to a customer, and the customer rejects the offer.

Next Step in Pega CDH:

In Pega Customer Decision Hub, when an offer is rejected, the system typically reevaluates the Next-Best-Action. This means it reassesses the available actions to determine the most appropriate next step for the customer based on current context and customer data.

Contact Policy Conditions - U+ Bank has added all existing contact policy conditions, ensuring that offers comply with set business rules.

Unit Tests with Persona Testing - Before going live, it is essential to validate the implementation through persona-based unit tests. This helps verify that the strategy gives the expected results for different customer personas.

Process:

Design test personas that represent various customer segments.

Run tests to ensure that each persona receives appropriate offers.

Validate the expected outcomes, ensuring no regressions or policy breaches.

To ensure that the Platinum Plus offer is prioritized over other offers, you can increase the "business weight" of the Platinum Plus card. The business weight is a lever used in the arbitration process to influence the prioritization of actions. By increasing the business weight, you make the Platinum Plus offer more likely to be selected over other competing offers.

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).

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​​.

Email treatments in Pega Customer Decision Hub allow for personalization, including customizing the email subject to address the customer by name or other personalized data.

Definition and Purpose:

Email treatments are used to define the content and presentation of email communications sent to customers as part of outbound campaigns.

Personalization enhances the relevance and engagement of these emails by including customer-specific information.

Capabilities of Email Treatments:

Personalization Tags: Pega supports the inclusion of dynamic tags in the email subject and body, allowing for personalized greetings, offers, and other content. For example, "Dear " where "" is dynamically replaced with the actual name of the customer.

HTML Content: Design rich email content using HTML, which can include images, links, and other multimedia elements.

Steps to Personalize Email Subjects:

Step 1: Create or edit an email treatment in Pega.

Step 2: In the subject line field, use the syntax for personalization tags. For example, "Dear , check out our new offers!".

Step 3: Map the personalization tags to the appropriate customer data fields to ensure the correct information is inserted when the email is sent.

Testing Personalization:

Use seed lists to test personalized emails. Seed lists are groups of test email addresses used to preview how the email will look when sent to actual customers.

Ensure that personalization attributes appear correctly in the received test emails.

Best Practices:

Always test personalized emails thoroughly before launching a campaign to ensure that the tags are working correctly and the content renders as expected.

Use personalization to enhance customer engagement by making emails feel more relevant and tailored to individual recipients.

References:

Pega-Customer-Decision-Hub-User-Guide-85.pdf: "Configuring email treatments" section.

Pega documentation on "Defining an action for outbound".

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

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

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

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

To 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.

 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​​.

 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​​.

Pega Scenario Planner is the best tool for estimating the business value of a next-best-action run with the selected configuration:

Purpose of Scenario Planner:

Step 1: Pega Scenario Planner is designed to simulate and analyze the potential impact of business changes before they are implemented.

Step 2: It provides insights into how changes in decision strategies, engagement policies, or other configurations affect business outcomes.

Simulation Setup:

Step 1: Define the scenarios you want to test, including changes to the decision strategies or engagement policies.

Step 2: Configure the simulation to reflect the real-world conditions and customer data relevant to the business case.

Analyzing Results:

Step 1: Run the simulation and review the results to understand the estimated business value generated by the next-best-action run.

Step 2: Analyze key performance indicators (KPIs) such as acceptance rates, response rates, and financial metrics to evaluate the impact of the changes.

Example:

U+ Bank can use Pega Scenario Planner to simulate the presentation of Credit Card offers on their self-service portals, allowing them to estimate the business value generated by these offers based on the selected configuration.

References:

Pega-Customer-Decision-Hub-User-Guide-85.pdf: "Increasing your understanding of the effect of your business changes with the Scenario Planner" section.

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

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

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

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

 Business Structure: Setting up the business structure to Sales/CreditCards ensures that the actions and offers are correctly categorized and managed within the appropriate business context.

 Real-Time Container: Creating and configuring a real-time container is necessary for presenting the credit card offer to customers in real-time as they log in to the website.

 Action and Web Treatment: Defining the action and configuring its web treatment ensures that the offer is ready to be displayed to the customers, complete with the necessary details and presentation format.