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.
References:
* 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.

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.

Designing Reusable Action Flows:
* When creating an action flow intended for reuse, it's important to configure it with specific treatments.
* The Specify Treatment option allows the flow to dynamically apply the appropriate treatment based on the action context.
* This approach ensures consistency and flexibility, making it easier to manage and update the flows.

Triggering Segment Runs with Next-Best-Action Outbound Schedule:
* Segments can be configured to run in alignment with the Next-Best-Action outbound schedule.
* This setup ensures that the segment is refreshed and populated with the latest customer data before each
* outbound run.

One-to-One Marketing Channels: As the company transitions to a one-to-one marketing approach, leveraging various channels is essential to effectively present new offers like a data plan.
* Retail Store: Offers can be presented directly to customers through in-store interactions, allowing personalized recommendations based on their profile and behavior.
* Chatbot: Automated chatbots can engage customers in real-time on digital platforms, offering personalized data plans based on customer interactions and preferences.