This paper delves into the intricate process of exporting data from Pega's adaptive models, pivotal in the realm of Pega's next-best-action decision strategies for personalized customer engagement. Focusing on the Adaptive Decision Manager (ADM) within Pega Decision Management, the paper elucidates how ADM utilizes customer data, including profiles, interaction contexts, and past behaviors, to predict customer actions like web banner clicks. The adaptive models, leveraging a Bayesian algorithm, continuously learn from customer responses, adapting to changing preferences and needs. The process of exporting this dynamic, self-learning data for external analysis is critically examined. This includes the recording of historical data, managing data sets for relevant information, and the utilization of tools like Pega Data Scientist Tools for deeper insights. The paper aims to offer a comprehensive guide on harnessing these advanced predictive models for external analytical purposes, thereby enhancing decision-making and strategic planning in business environments.