The Next-Best-Action (NBA) strategy is optimized by balancing customer needs with business objectives. Predictive analytics (B) addresses the customer's perspective by calculating propensity—the likelihood a customer will respond positively to an action. This ensures the action is relevant and personalized. Business priorities (A) represent the organization's goals, assigning a value or weight to each action based on its importance to the business (e.g., revenue, retention). The Pega Customer Decision Hub arbitration framework combines these two components to select the action that provides the most value to both the customer and the business.

C. Mass advertising: This is a one-to-many communication strategy, which is the opposite of the personalized, one-to-one engagement principle that Next-Best-Action is designed to deliver.

D. Customer complaints: While customer feedback is valuable data that can be used to refine strategies or models over time, it is not a direct, core component used in the real-time arbitration formula to optimize the action selection.

1. Pega Academy, Pega Customer Decision Hub Foundation (24) course: In the "One-to-one customer engagement" module, the lesson "Arbitrating between actions" explains that the core arbitration formula balances customer relevance (Propensity, determined by predictive models) with business value (Action Value, determined by business priorities).

2. Pega Documentation, Customer Decision Hub (24.1) User Guide: The section "Next-Best-Action Designer basics" under "Arbitration" details how the system weighs various factors to select the top actions. It explicitly states, "The formula for ranking actions is as follows: Priority = Propensity Context Weight Action Value." This formula directly incorporates predictive analytics (Propensity) and business priorities (Action Value).