## 1 Introduction
Although all the mentioned research focused on improving the performance of machine learning, the studies are quite limited by the variety of datasets that are used--datasets that are widely used as benchmarks (see Table 1) and have quite limited properties, which will be discussed later in this article. However, even robust ensembles may underperform when data distributions shift over time, underscoring the need for adaptive techniques. In recent studies, the frequency of the use of ensemble and deep learning for telecommunications has increased. For example, [PERSON] et al. [16] introduce a bagging-based selective ensemble model that addresses class imbalances in the telecommunications sector, emphasizing both accuracy and profit-oriented measures. [PERSON] et al. [17] combine CatBoost and a Deep Neural Network (DNN) in a hybrid solution for card-fraud detection, attaining high AUC scores under near real-time conditions. The authors obtained AUC scores of 0.97 (CatBoost) and 0.84 (Deep Neural Network). Other works, such as that of [PERSON] et al. 's [18] comparative study and the work of [PERSON] with [PERSON] [19], who presented a comprehensive overview of CatBoost, highlight how gradient boosting (e.g., CatBoost, XGBoost, and LightGBM) and deep neural architectures (e.g., RNNs, CNNs, and transformers) are often fused into unified ensembles to improve both predictive performance and business metrics (e.g., profit-based churn modeling or fraud detection). Notably, CatBoost's popularity has increased recently, as reflected by the rapidly increasing number of articles (see Figure 1), confirming its growing acceptance in both academic research and industry applications. Adaptive learning techniques have been proposed to handle concept drift by updating models incrementally to adapt to changing data distributions [3; 20]. In reference [3], an adaptive churn prediction model for concept-sensitive imbalanced data streams was proposed. [PERSON] et al. [20] focused on adaptive learning using Naive Bayes combined with a genetic algorithm for feature weighting. Despite these advancements, many studies do not fully address the practical challenges posed by concept drift and data delays between model training and deployment [7]. [PERSON]. [PERSON] et al. [21] explored offline and online multi-target regression algorithms to predict customer behavior, concluding that online regression outperforms its offline counterparts due to its ability to handle streaming data and temporal dynamics. As mentioned previously, we focus on an analysis of the nature of concept drift rather than addressing it as a problem. With our data, we found that models do not necessarily \"expire\" with time; however, their performance varies with when they are tested. This can still be considered concept drift, although there is no need to address it to improve prediction performance. In parallel with the churn prediction challenges in telecommunications, recent research has increasingly focused on measuring the freshness of data via the concept of Age of Information (AoI). AoI quantifies how recently received information reflects the current state of a system or user, and has been applied in settings where latency or energy constraints make timely data updates critical [22]. For example, AoI-minimal clustering, transmission, and trajectory co-design have been studied in UAV-assisted wireless-powered communication networks to ensure the data remain up-to-date and are collected in an energy-efficient way. While our work addresses concept drift in churn prediction--a different aspect of how data and models evolve over time--there is a conceptual overlap in recognizing the importance of when data are obtained and used. AoI typically targets immediate or real-time data freshness (where seconds or even milliseconds matter), while concept drift focuses on longer-term shifts in the underlying data or user behavior (hours, days, or even months). They operate on different timescales yet both relate to when data are relevant and how models adapt to change.