Pass the Pegasystems Pega Decisioning Consultant PEGAPCDC87V1 Questions and answers with ExamsMirror

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.

 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.

 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.

 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.

 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 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.

 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.

To promote various auto loan offers to qualified customers on its website using Pega Customer Decision Hub, you need to:

Create a decision strategy: This involves defining the logic that determines which auto loan offers are most relevant to each customer based on their profile, behavior, and eligibility criteria.

Set up the business structure: This involves organizing the offers into a hierarchical structure of business issues and groups, such as categorizing them under a "Loans" issue and an "Auto Loans" group. This structure helps in managing and prioritizing the offers effectively.