PEGAPCDC87V1 Exam Dumps - Certified Pega Decisioning Consultant (PCDC) 87V1

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.

Next-Best-Action in Pega Customer Decision Hub is primarily applied to customer engagement and decisioning scenarios:

Purpose of Next-Best-Action:

Step 1: Next-Best-Action aims to provide personalized recommendations and decisions that are most relevant to the customer at any given moment.

Step 2: It uses predictive and adaptive analytics to determine the most appropriate actions based on customer data and business rules.

Application in Loan Decisions:

Step 1: In the context of determining if a borrower gets a loan, Next-Best-Action evaluates various factors such as creditworthiness, loan history, and current financial status.

Step 2: It leverages AI models to assess the risk and likelihood of repayment, ensuring that loan offers are extended to eligible and suitable customers.

Implementation Steps:

Step 1: Set up decision strategies in Pega Customer Decision Hub to include criteria for loan eligibility, such as credit scores and income levels.

Step 2: Configure predictive models to analyze customer data and predict loan approval probabilities.

Step 3: Use engagement policies to ensure that the loan offers are aligned with business objectives and regulatory requirements.

Benefits:

Applying Next-Best-Action to loan decisions helps in making data-driven and objective decisions, reducing the risk of defaults and improving customer satisfaction by offering relevant financial products.

References:

Pega-Customer-Decision-Hub-User-Guide-85.pdf: "Understanding Next-Best-Action Designer basics" section.

Pega documentation on "Decision management and AI in financial services".

ï‚· Marketing and Sales Actions: Creating actions like "No annual fee credit card" and "1% cash back credit card" address different customer needs and stages in the customer lifecycle, including acquisition and engagement.

ï‚· Full Customer Lifecycle: These actions involve marketing and sales efforts aimed at attracting and retaining customers by offering attractive benefits.

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.

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.

ï‚· Requirement:

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

ï‚· 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.

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.

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.