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

Paul cannot see the Reward card offer due to the contact policies defined by U+ Bank:

Contact Policies Defined:

Policy 1: Suppress a group of credit card offers for 30 days if any credit card offer is rejected three times in any channel in the past 15 days.

Policy 2: Suppress the Reward card offer for 7 days if it is rejected twice in any channel in the last 7 days.

Paul’s Rejection History:

Paul has rejected other credit card offers twice on the web channel and once in the contact center within the last 15 days.

Reason for Suppression:

Due to Policy 1, since Paul has rejected any credit card offer (not specifically the Reward card) three times in any channel in the past 15 days, all credit card offers, including the Reward card offer, are suppressed for 30 days.

Understanding the Suppression Logic:

Step 1: The decision strategy evaluates the rejection history of offers.

Step 2: It applies the contact policies to determine which offers should be suppressed.

Step 3: In this case, the policy conditions are met due to multiple rejections of credit card offers, leading to the suppression of the Reward card offer.

References:

Pega-Customer-Decision-Hub-User-Guide-85.pdf: "Setting constraints, contact policy limits, and controls" section.

Pega documentation on "Managing contact policies".

To target customers living in a specific geographic area for advertising the new fiber optic service, you need to configure a "Customer segment" in the Next-Best-Action Designer. This involves defining the segment of customers based on their geographic location, ensuring that only those customers in the northern region are included in this segment. By doing so, you can ensure that the banner advertisement for the fiber optic service is displayed only to those customers.

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.