Preventing customer churn, i.e., termination of business commitments, is essential for companies operating in saturated markets, especially for subscription-based models such as telecommunication. Knowing when customers decide to terminate services is instrumental to effective churn prevention. In this study, we investigate how churn prediction performs in practice when training models on different time intervals of historic data (1-4 weeks back) and predicting churn at different numbers of weeks ahead (1-4 weeks). We use a real-world, time-series dataset of mobile subscription usage to examine churn prediction for business-to-business (B2B) customers. We utilize the timeseries data at a higher temporal resolution than prior studies and investigate different forecasting horizons. Leveraging popular machine learning algorithms such as Random Forests, Gradient Boosting, Neural Networks, and Gated Recurrent Unit, we show that the best model achieves an average F1-score of 79.3% for one-week ahead predictions. However, the average F1-score decreases to 63.3% and 61.8% for two and four weeks ahead, respectively. A model interpretation framework (SHAP) evaluates the feature impact on the models' internal decision logic. We also discuss the challenges in applying churn prediction for the B2B segment.