###### Abstract  
Customer retention is a key priority for mobile telecommunications companies, as acquiring new customers is significantly more costly than retaining existing ones. A major challenge in this field is predicting customer churn--users discontinuing services. Traditional predictive models such as rule-based systems often struggle with the complex, non-linear nature of customer behavior. To address this, we propose the use of deep learning techniques, specifically multi-layer perceptron (MLP) and radial basis function (RBF) networks, to improve the accuracy of churn predictions. However, while neural networks excel in predictive performance, they are often criticized for being \"black-box\" models, lacking interpretability. A real-world data set is considered, which originally contained information about 15,000 randomly selected clients. Various network structures and configurations are analyzed. The obtained results are compared with results generated using fuzzy rule-based and rough-set rule-based systems. The MLP model achieved an almost perfect accuracy of 0.999 with an F-measure of 0.989, outperforming traditional methods such as fuzzy rule-based and rough-set systems. Although the RBF model slightly lagged in accuracy, it demonstrated a superior recall of 0.993, indicating better identification of potential churnes. These results demonstrate that neural network models significantly enhance predictive performance in churn modeling. The interpretability of the model is also discussed since it bears significance in real applications. Our contribution lies in showing that deep learning methods significantly enhance churn prediction accuracy, though the challenge of model interpretability remains a critical area for future work. churn modeling; telecommunication; deep learning; automation of customer analysis; big data +
Footnote †: journal: Medical  
Customer churn prediction is a critical task for businesses across various industries, as retaining customers is often more cost-effective than acquiring new ones. The use of mobile telecommunications and smartphones is one of the key factors in digital transformation. In this paper, we delve into the realm of customer churn prediction and the automation of customer analysis, employing a deep learning approach to tackle this complex problem. Specifically, we explore the efficacy of MLP and RBF models in predicting churn using real-world data from a telecommunications company in Poland. Such an analysis is an important part of business/customer [PERSON] systems. Our study focused on a data set comprising information on 15,000 randomly selected clients, encompassing diverse demographics and usage patterns. Through meticulous data preprocessing, the data set was prepped for analysis, ensuring its readiness for training and evaluation. In the context of predictive modeling, particularly in the analysis of customer churn, the concepts of entropy and information theory offer powerful tools to measure the uncertainty and informativeness of features used in prediction models. Entropy, a fundamental measure in information theory introduced by [PERSON] [1], quantifies the uncertainty in a data set. High entropy indicates a high degree of unpredictability or disorder, whereas low entropy suggests more predictability [2]. In this study, we analyze the entropy of various features within our telecommunications data set to understand which variables contribute most to the uncertainty in predicting customer churn. By calculating the entropy and correlation for different attributes, we identify which features provide the most informative signals that can reduce uncertainty in churn predictions [3; 4]. For instance, attributes like customer tenure or service usage patterns may have lower entropy, indicating they are more reliable predictors of churn. Furthermore, information gain, which is derived from entropy, is utilized to evaluate the importance of each feature in churn prediction models.