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

To ensure that a customer does not see offers on their account page if they have already received three home loan offers in the last two weeks, you need to define customer contact limits. These limits allow you to control the number of times a customer can be contacted with specific types of offers within a given timeframe, thus preventing overexposure and enhancing customer satisfaction.

 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.

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".

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.

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.

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.

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.

 Introduction to Prioritize Component:

The Prioritize component in Pega Decisioning is used to order actions based on specific criteria.

This component evaluates the importance or priority of different actions to determine which should be executed first.

 Understanding Propensity:

Propensity is a prediction of the likelihood that a customer will respond positively to a particular action.

It is a key factor in decision-making processes as it reflects the customer's predicted behavior based on past interactions and other data.

 Role of Propensity in Prioritize Component:

The primary function of the Prioritize component is to rank actions.

This ranking is typically based on the propensity value, which is calculated using predictive models.

The action with the highest propensity is considered the top action because it has the highest predicted likelihood of a positive customer response.

 Verification from Pega Documentation:

According to Pega's guidelines on creating and understanding decision strategies, propensity is a standard and recommended metric for prioritizing actions within the Prioritize component.