U+ Bank, a retail bank, has recently implemented a project in which qualified customers see mortgage offers when they log in to the web self-service portal.
Currently, only the customers who satisfy the following engagement policy conditions receive the Fifteen-year fixed-rate mortgage offer:
The bank decides to make two changes:
1. Update the suitability condition for the Fifteen-year fixed-rate mortgage offer.
2. Introduce a new offer, Twenty year fixed-rate mortgage.
The following table shows the new engagement policy conditions for both mortgage offers:
What is the best practice to fulfill this change management requirement in the Business Operations Environment?
To implement the required changes for the mortgage offers, you should create two separate change requests in the 1:1 Operations Manager portal. This ensures each change is tracked and managed individually.
Update Suitability Condition for Fifteen-Year Fixed-Rate Mortgage Offer:
Step 1: Log into the 1:1 Operations Manager Portal.
Step 2: Create a change request for updating the suitability condition of the Fifteen-Year Fixed-Rate Mortgage offer.
Step 3: Specify the details of the change, including the new suitability condition (Credit Score > 450).
Step 4: Submit the change request for approval and implementation.
Introduce a New Offer - Twenty-Year Fixed-Rate Mortgage:
Step 1: In the 1:1 Operations Manager Portal, create a new change request for introducing the Twenty-Year Fixed-Rate Mortgage offer.
Step 2: Define the new offer, including its eligibility, applicability, and suitability conditions (Credit Score > 600).
Step 3: Configure the necessary treatments and engagement policies for the new offer.
Step 4: Submit this change request for approval and implementation.
Best Practices:
Creating separate change requests ensures that each modification is properly documented and approved, reducing the risk of errors and making it easier to track changes.
This approach also allows for independent testing and validation of each change before it goes live.
Pega-Customer-Decision-Hub-User-Guide-85.pdf: 'Managing business-as-usual changes with Pega 1:1 Operations Manager' section.
Pega documentation on 'Creating change requests in 1:1 Operations Manager'.
By following these steps, U+ Bank can effectively manage the changes to their mortgage offers, ensuring that both the updated suitability condition and the new offer are correctly implemented.
U+ Bank has recently implemented Pega Customer Decision Hub"M. As a first step, the bank went live with the contact center to improve customer engagement. Now, U+ Bank wants to extend its customer engagement through the web channel. As a decisioning consultant, you have created the new set of actions, the corresponding treatments, and defined a new trigger in the Next-Best-Action Designer for the new web channel.
What else do you configure for the new treatments to be present in the next-best-action recommendations?
Initial Configuration: U+ Bank has implemented Pega Customer Decision Hub for the contact center and now wants to extend it to the web channel.
Understand Channel Configuration: Pega CDH requires the configuration of channels in the Next-Best-Action Designer to ensure that treatments are correctly recommended.
Modify the Strategy Framework:
Next-Best-Action Framework: This strategy framework is used to determine the best actions for customers across various channels. It needs to be modified to include configurations specific to the web channel.
Steps to Modify:
Access Next-Best-Action Designer: Navigate to the Next-Best-Action Designer.
Channels Tab: Configure the new web channel in the Channels tab.
Modify the Strategy: Update the Next-Best-Action Framework strategy to incorporate the new web channel. This involves ensuring the new actions and treatments are included and prioritized correctly for the web channel.
According to the Pega Customer Decision Hub User Guide, modifying the framework strategy to cater to new channels is necessary for extending customer engagement (Reference: Pega-Customer-Decision-Hub-User-Guide-85.pdf, Chapter on 'Understanding Next-Best-Action Designer channels').
Testing and Validation:
Test the new configurations to ensure treatments for the web channel are correctly recommended in the next-best-action suggestions.
Conclusion: To configure the new treatments for the web channel in the next-best-action recommendations, the Next-Best-Action Framework strategy must be modified to cater to the new web channel.
In a decision strategy, you can use aggregation components to_______________.
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.
Reference module: Essentials of always-on outbound
A bank has been running traditional marketing campaigns for many years. One such campaign sends an offer email to qualified customers on day 1. On day 3, it sends a reminder email to customers who haven't responded to the first email. On day 7, it sends a second reminder to customers who haven't responded to the first two emails. If you were to re-implement this requirement using the always-on outbound customer engagement paradigm, how would you approach this scenario?
To re-implement the bank's traditional marketing campaign using the always-on outbound customer engagement paradigm in Pega, follow these steps:
Understand the Traditional Campaign Requirements:
Day 1: Send an offer email to qualified customers.
Day 3: Send a reminder email to customers who haven't responded to the first email.
Day 7: Send a second reminder to customers who haven't responded to the first two emails.
Set Up Next-Best-Action Designer:
Use the Next-Best-Action Designer to define engagement policies, arbitration, and channels to manage customer interactions.
The always-on approach continuously evaluates customer interactions and selects the best action based on real-time data and AI-driven insights.
Configure the Primary Schedule:
Set up a daily run schedule for the Next-Best-Action strategy. This allows the AI to evaluate customer interactions daily and decide the best action to take.
Leverage AI for Decisioning:
Configure the AI models to determine the best action for each customer based on their interaction history and engagement policies. The AI will automatically handle:
Sending the initial offer email on Day 1.
Sending a reminder email on Day 3 to those who haven't responded.
Sending a second reminder on Day 7 to those who still haven't responded.
Define Engagement Policies:
Eligibility: Define which customers qualify for the offer.
Applicability: Ensure the offer is relevant to the customer.
Suitability: Verify that sending the offer is in the best interest of the customer.
Contact Policies: Set rules to manage the frequency and timing of emails to avoid over-communication.
Implement AI-Driven Actions:
Use the Next-Best-Action strategy framework to select actions (e.g., sending emails) based on the AI model's recommendations.
Configure treatments (email content) and channels (email) within the Next-Best-Action Designer.
Monitor and Adjust:
Continuously monitor the performance of the campaign using Pega's analytics and adjust the strategy as needed to optimize engagement and response rates.
Pega Customer Decision Hub User Guide 8.5: Understanding Next-Best-Action Designer basics, Configuring the Next-Best-Action Designer for Pega Customer Service, Setting constraints contact policy limits and controls.
As a Decisioning Consultant, you are tasked with configuring the ethical bias policy. Which context do you need to select to add bias fields?
Configuring Ethical Bias Policy:
To configure bias fields, the context of the bias must be selected, which usually pertains to customer-related properties.
Selecting Bias Fields:
Bias fields such as age, gender, and ethnicity are typically customer properties.
These properties are analyzed to detect and measure bias in decision strategies.
Detailed Explanation:
By selecting the Customer context, you can add and configure relevant customer properties to the bias policy.
This allows for comprehensive bias testing and ensures fairness in action distribution.
Verification from Pega Documentation:
Pega documentation confirms that customer-related properties are the primary context for configuring bias fields in ethical bias policies.
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