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

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.

Business Requirement: The need is to implement risk segmentation rules based on age and credit score.

Decision Table Integration: The decision table can be added to the decision strategy where it will evaluate the credit score and age, and then assign the appropriate risk segment.

Parameter Passing: The decision table within the strategy will use the credit score passed to it as a parameter to determine the segmentation.

References: Pega's methodology for incorporating decision tables into strategies for evaluating and segmenting customers based on specific criteria is well documented in the user guides .

Action Flow Template: To implement the requirement of using the same action flow template for all credit card actions, configure the email treatment. This allows you to define the desired flow pattern and reuse it across different credit card actions.

Configuration: Create an email treatment template that includes the specific flow pattern, and then apply this template to all the relevant credit card actions.

ï‚· Understand the strategy objective: The decision strategy is designed to output the most profitable shoe. The profit is calculated as Profit = Price - Cost.

ï‚· Evaluate the provided data:

Nike: Profit = $69 - $59 = $10

Adidas: Profit = $65 - $45 = $20

Puma: Profit = $85 - $65 = $20

Reebok: Profit = $75 - $50 = $25

ï‚· Determine the most profitable shoe: According to the calculations, Reebok has the highest profit ($25).

ï‚· Fill in the blank: Therefore, the output of the component in the blank space highlighted in red should be Reebok, which has the highest profit.

 Reference: The strategy design and output decision rules are outlined in the sections on "Understanding Next-Best-Action Designer arbitration" and "Next-Best-Action strategy framework components" in the Pega Customer Decision Hub User Guide​​.

ï‚· Addressing Test Failures: When persona tests fail after adding new engagement policy conditions, the appropriate step is to analyze the test case failures, update the tests as necessary, and re-execute them until all tests pass. This ensures that the changes made are correctly reflected in the tests and the system functions as expected.

ï‚· Implementation: Review the failing tests to understand why they failed, adjust the conditions or the expected results, and run the tests again to validate the changes.

ï‚· Understanding the Requirement:

The bank wants to suppress a group of mortgage offers if a customer ignores (does not click on) three offers within 15 days.

The suppression rule needs to be defined for the web channel.

ï‚· Configuring Suppression Rule:

The suppression rule must track the number of impressions (views) and check if there are no clicks.

It needs to be configured to act after three such impressions over a specific time period (15 days).

ï‚· Steps to Configure:

Define the group of mortgage offers as a single entity.

Set up the suppression criteria to trigger after three impressions without clicks for the web channel.

Configure the suppression period to be 15 days.

ï‚· Verification from Pega Documentation:

According to Pega's guidelines on adding tracking periods and suppression rules, this configuration accurately reflects the bank's requirements.

Aggregation components in Pega Customer Decision Hub provide the ability to make calculations based upon a list of actions. These components are used to perform operations such as summing values, averaging scores, or other statistical calculations that aggregate data from multiple actions. This is crucial for generating insights and making informed decisions based on the collective data of several actions.

Correct Engagement Policy Level Configuration:

Criterion 1: Owns a smart mobile

Engagement policy level: Group level

Criterion 2: Under graduation student / Post graduation student

Engagement policy level: Issue level

Criterion 3: New customer / Existing customer

Engagement policy level: Action level

To configure engagement policies correctly in Pega, it is important to choose the appropriate level of engagement policy based on the criteria. The levels are:

Issue level: Applies to all actions within a business issue.

Group level: Applies to all actions within a specific group.

Action level: Applies to individual actions.

1. Criterion 1: Owns a smart mobile

This criterion is relevant for all data plans under a specific group of data plans.

Therefore, it is best suited at the Group level, ensuring that all plans within the group check for this criterion.

2. Criterion 2: Under graduation student / Post graduation student

These educational statuses may be relevant across various groups and actions under a broader business issue like "Education" or "Student Plans".

Therefore, this should be configured at the Issue level to apply across all relevant actions within the issue.

3. Criterion 3: New customer / Existing customer

This criterion is specific to individual actions and can vary greatly between different offers.

Hence, it should be configured at the Action level to ensure precise control over which actions are offered to new versus existing customers.

To enable access to customer properties such as age, income, etc., in a decision strategy, the Data Import component is required. This component imports the relevant customer data into the strategy so it can be utilized for decision-making processes.

To present offers based on the responses of a test group that already received the offer, you should configure the propensity factor in Pega Customer Decision Hub. Propensity models predict the likelihood of a customer accepting an offer based on historical data and responses from similar customers. By leveraging propensity, you can ensure that offers are presented to customers who are most likely to respond positively, based on the behavior of the test group.