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

 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 define a contact policy that suppresses an action for 20 days if it was rejected twice in any channel in the last 30 days, you need to specify the suppression rule as follows: "Suppress an action for 20 days if there are 2 rejects for any channel in the last 30 days." This rule ensures that the action is paused after it has been rejected a specified number of times across any channel within the given timeframe.

The best way to organize customer actions applicable to various business issues in Pega Customer Decision Hub is into a three-level hierarchy: Business issue > Group > Actions. This structure allows for clear categorization and management of actions based on their business purpose and relevance. The "Business issue" represents the high-level goal (e.g., Retention, Acquisition), "Group" categorizes the actions under each issue (e.g., Credit Cards, Mortgages), and "Actions" are the specific offers or recommendations.