## 1 Introduction
### Datasets Background  
Most articles either do not provide direct references to the sources they used to derive their data or provide links that no longer work. This is why, considering this constraint, the number of openly available datasets that other researchers in the field can use to conduct churn prediction studies is still quite small. Such constraints notwithstanding, a few well-known datasets have become standards when carrying out repeatable experiments for comparison. In the following paragraphs, we provide an overview of these datasets--including synthetic data from IBM, various telco churn datasets hosted on BigML and Kaggle, and more complex, real-world sets like cell2 cell and Orange Telecom--and discuss their characteristics. The IBM dataset [28] is a widely cited, synthetic dataset simulating a fictional telecommunications operator that offers home phone and Internet services. It consists of 7043 customer records and 33 attributes covering demographic information (e.g., gender and dependents), billing details (monthly charges and total charges), and a variety of service subscription indicators (e.g., phone services and streaming TV). The dataset includes a binary churn indicator and a \"churn reason\" feature, which is uncommon in real-world data. Although synthetic, this dataset provides a clear, well-structured context for initial exploratory studies and benchmarking churn models. The BigML dataset [29] is a frequently used telecommunications churn dataset available on the BigML platform. It is often cited by different authors due to its accessibility and the clarity of its metadata. While the exact size and attributes can vary, as different versions or subsets exist on the platform, the dataset typically includes customer tenure, service features, and billing information, alongside the churn flag. BigML's platform integration offers researchers a ready-to-use format, making it suitable for quick experimentation and comparisons of model performance. The cell2 cell dataset [30] comes from a large U.S. wireless telecommunications provider. It includes monthly subscriber-level data designed explicitly for churn modeling. Typically containing thousands of records, this dataset often features attributes such as usage patterns (call minutes and international calls), billing cycles, contract durations, and demographics. The richness and real-world nature of the cell2 cell dataset make it well-regarded for explorations of complex churn patterns and evaluations of advanced modeling techniques. Multiple churn-related datasets sourced from Kaggle [31; 32; 33; 34; 35] have been referenced in research. Kaggle hosts a variety of telco churn datasets, ranging from small, curated samples to larger sets exceeding 100,000 records. These often include service usage metrics (e.g., voice minutes and internet usage), contractual information (month-to-month or long-term contracts), payment details (electronic checks and credit card details), and customer support interactions. Each Kaggle dataset may focus on slightly different aspects, and some of them are augmented with synthetic features or specialized attributes in order to offer a diversity of testbeds for algorithm development and performance benchmarking. Researchers cite these datasets due to their public availability, well-defined formats, and community-provided benchmarks. The Orange Telecom dataset [36] is a well-known dataset provided during a KDD Cup challenge. It contains detailed customer usage information, demographic data, and billing details, accompanied by churn labels. With thousands of entries and a rich set of attributes, it has historically been used to evaluate advanced data mining, feature engineering, and machine learning techniques. The dataset's complexity and real-world authenticity make it an enduring reference for testing model robustness and transferability. Our dataset is based on daily aggregated user metrics over 720 days for over 12,000 users.