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

 Understanding the Requirement:

Myco wants to find new opportunities to improve marketing during the ideation stage of a project for presenting post-paid offers.

This involves identifying gaps and underserved customer segments that can be targeted with new or improved offers.

 Pega Value Finder:

Pega Value Finder is designed to analyze the distribution of actions across different customer segments.

It helps identify customers who are not receiving any actions or are receiving only low-propensity actions, thus highlighting opportunities to improve the strategy.

 Running the Simulation:

In Pega Value Finder, you can run simulations to discover underserved customers.

The tool provides insights into customer groups that are not well-engaged, allowing businesses to adjust their strategies and create more relevant actions or treatments.

 Steps to Run Value Finder:

Log in to the Pega Customer Decision Hub portal.

Navigate to Discovery -> Value Finder.

Create a new simulation and select the relevant issue and group.

Run the simulation to analyze customer distribution and identify underserved segments.

 Verification from Pega Documentation:

The Pega Customer Decision Hub User Guide details the process and benefits of using Value Finder to discover gaps and opportunities in customer engagement strategies​​​​.

To address the issue where customers have ignored a particular mortgage offer and the bank wants to offer the action 30% more frequently, we need to configure the appropriate arbitration factor in Pega Customer Decision Hub.

Arbitration Overview: Arbitration in Pega CDH determines how actions are prioritized and selected for presentation to the customer. It involves factors like business value, propensity, and action weighting.

Action Weighting: Action weighting is used to adjust the frequency and priority of specific actions. By increasing the action weighting, you effectively make the action more likely to be selected during the arbitration process.

Implementation:

Navigate to the Next-Best-Action Designer in Pega CDH.

Locate the action for the mortgage offer that needs increased frequency.

Adjust the action weighting to increase by 30%. This adjustment modifies the priority calculation, making the action more likely to be selected.

 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.