Concept drift detection is a critical component of most online learning algorithms. While the primary goal of concept drift detection techniques is to trigger the retraining of machine learning models, in high-risk domains such as industry and healthcare “where models serve as decision-support systems, with final decisions made by humans” it is essential to understand the nature of the drift, its potential causes, and its impact on model performance. In this paper, we propose a 3P (three-perspective) approach for efficiently detecting concept drifts and analyzing their nature from three perspectives: data characteristics, model performance, and explainability. Unlike traditional approaches that merely identify the point in time where drift occurs, our approach not only detects drift but also provides deeper context by integrating insights from data, model behaviour, and XAI (explainable AI). This enriched perspective enables a more comprehensive understanding of drift dynamics, facilitating informed decision-making. Additionally, by incorporating explainability-driven analysis, our approach allows for detecting drifts that may not be identifiable through model performance alone, as with most conventional detectors. We integrate this three-perspective analysis with visualization techniques and demonstrate its effectiveness on benchmark datasets and two real-world use cases from healthcare and industry.