Pegasystems PEGAPCDC87V1 - Certified Pega Decisioning Consultant (PCDC) 87V1

To determine which of the new credit card offers (Diamond, Platinum, and Gold) will be the best performing action, the Action Distribution Report is used. This report helps in running a distribution test to analyze how the different offers are being distributed among the customer base, providing insights into which offer might perform the best based on initial distribution and eligibility criteria.

ï‚· Requirement:

The bank wants to avoid sending promotional emails related to credit card offers to a customer if they have already received one within a limited time period.

ï‚· Configuring Suppression Policy:

A suppression policy needs to be defined to prevent over-contacting customers.

Suppression policies automatically put an action on hold after specific criteria are met, such as a customer having already received a promotional email.

This ensures that customers are not bombarded with repeated offers, improving customer experience and adhering to communication limits.

To update the cashback offer to be presented through a new channel (SMS) and to update the suitability condition, you need to perform two main tasks: adding a new treatment and editing the engagement policy.

Add a New Treatment:

Step 1: Navigate to the "Actions" section in the Pega Customer Decision Hub.

Step 2: Locate the existing cashback offer action.

Step 3: Add a new treatment specifically for the SMS channel. This involves creating a new SMS treatment that defines the message content, format, and any other relevant details.

Step 4: Ensure the new treatment is linked to the cashback offer action.

Edit the Engagement Policy:

Step 1: In the Pega Customer Decision Hub, go to the "Engagement Policies" section.

Step 2: Locate the engagement policy associated with the cashback offer.

Step 3: Update the suitability condition by lowering the debt-to-income ratio threshold from 48 to 45. This ensures that the new condition is applied when evaluating customer eligibility for the cashback offer.

Step 4: Save and publish the updated engagement policy to ensure it takes effect.

Verify the Changes:

Step 1: Test the new SMS treatment by sending test messages to a seed list to verify the content and formatting.

Step 2: Ensure that the updated engagement policy correctly filters customers based on the new debt-to-income ratio threshold.

References:

Pega-Customer-Decision-Hub-User-Guide-85.pdf: "Defining treatments for actions" and "Configuring engagement policies" sections.

Pega documentation on "Creating and managing actions" and "Editing engagement policies".

To configure an ethical bias policy that allows no bias based on gender, use the 0.9-1.11 Rate Ratio:

Understanding Rate Ratios:

Step 1: Rate ratios are used to detect and measure bias in decision-making processes.

Step 2: A rate ratio of 1 indicates perfect parity, meaning there is no bias between groups.

Configuring Bias Detection:

Step 1: In Pega Customer Decision Hub, you can configure bias detection by setting an acceptable rate ratio range.

Step 2: To ensure no bias based on gender, set the rate ratio to a narrow range close to 1, such as 0.9-1.11. This range ensures that the decisioning process treats different genders fairly, with minimal deviation.

Implementation:

Step 1: Navigate to the Bias Detection settings within the Decision Strategy framework.

Step 2: Set the rate ratio to 0.9-1.11 for gender to monitor and enforce non-discriminatory practices in presenting offers.

Example:

MyCo can use this configuration to ensure that their mobile data package offers for students do not exhibit gender bias, providing equal opportunities for both male and female students.

References:

Pega-Customer-Decision-Hub-User-Guide-85.pdf: "Complying with policies or regulations by detecting unwanted bias" section.

Proposition Data Components - These components in a decision strategy are used to import and reference actions or propositions.

Requirement - The strategy designer wants to import all actions from one group and a subset from another.

Minimum Number Calculation:

One component for importing all 10 actions from the Sales/Credit Cards group.

Another component for importing the 2 specific actions from the Sales/Mortgages group.

ï‚· Arbitration Factors: To prioritize customer needs in the decision strategy, adjusting the context weighting factor is essential. This factor takes into account the situational context of each action, thereby prioritizing actions that align with customer needs.

ï‚· Implementation: By boosting context weighting, you ensure that the decision strategy considers the specific context of customer interactions, leading to more relevant and satisfactory offer presentations.

Correct Engagement Policy Configuration:

The customer must not be flagged as a defaulted borrower

Engagement policy: Eligibility

Customer already owns a higher-value offer

Engagement policy: Applicability

Must be a resident of New York City

Engagement policy: Eligibility

The savings product offers are relevant only if the customer has not explicitly opted out of direct marketing

Engagement policy: Applicability

Inappropriate for customers with a credit score < 300 as they are likely to default

Engagement policy: Suitability

The customer must not be flagged as a defaulted borrower

Eligibility: Eligibility conditions determine whether a customer qualifies for an offer at a basic level. Being flagged as a defaulted borrower is a fundamental criterion to disqualify a customer from receiving certain offers.

ï‚· 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.

ï‚· 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.

ï‚· 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.

ï‚· 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.

ï‚· 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​​​​.

Understanding Aggregation Components: Aggregation components in Pega decision strategies are used to perform calculations based on a list of actions. This can include summing values, calculating averages, or other statistical measures.

Use Case of Aggregation Components:

Set a Text Value: This is typically handled by Set Property or Data Transform components.

Filter Actions: Filters are used for filtering actions based on priority and relevance.

Choose Between Actions: This is typically handled by a Decision or Filter component.

Aggregation is specifically used to perform calculations based on a group of actions. It can be used to sum the total value of actions, calculate the average propensity, etc.

Implementation in Decision Strategy:

Add Aggregation Component: In the decision strategy, add an Aggregation component.

Define Calculation: Specify the type of calculation (sum, average, count, etc.) and the actions to be included in the calculation.

The Pega Customer Decision Hub User Guide mentions the use of aggregation components to perform calculations on action lists, confirming their primary function is for making calculations based on a list of actions (Reference: Pega-Customer-Decision-Hub-User-Guide-85.pdf, Chapter on "Using aggregation components in decision strategies").

Conclusion: In a decision strategy, aggregation components are used to make calculations based upon a list of actions, enabling the strategy to derive meaningful metrics from groups of actions.

Customer Lifetime Value (CLV) - The bank wants to offer a discount based on the customer's CLV.

Filter Component - The Filter component is used in decision strategies to include or exclude actions based on specific criteria, such as CLV.

Implementation:

Add a Filter component in the decision strategy.

Configure the filter to check if the CLV value is greater than 400.

If true, proceed with the 10% discount offer.