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Digital firms increasingly operate in environments where customer interactions generate continuous streams of behavioral data. These data include browsing patterns, transactions, social media interactions, app usage, recommendation responses, and digitally mediated service encounters. At the same time, firms invest heavily in personalization systems intended to transform such data into more relevant customer experiences. Despite this progress, many organizations do not fully convert behavioral data and personalization investments into sustained engagement and business performance. The problem is not simply a lack of data or technology. Rather, firms often manage data collection, personalization, engagement, and performance as separate activities instead of treating them as dynamically connected elements of a continuous learning system. This article proposes the Digital Customer Intelligence Loop as an original conceptual model. The model links behavioral data, personalization capability, customer engagement, and business performance in a self-reinforcing cycle. It explains how engagement outcomes generate new behavioral signals that improve subsequent personalization and strengthen future performance. The proposed model identifies behavioral data as the input layer, personalization capability as the transformation layer, customer engagement as the behavioral response layer, and business performance as the value outcome layer. Five tables clarify the model’s main components, including behavioral data types, personalization dimensions, engagement mechanisms, performance outcomes, and feedback dynamics. The framework offers researchers a basis for testing dynamic customer intelligence processes and gives managers a strategic tool for building continuous customer learning systems.