Machine Learning Strategies for Customer Churn Prediction in Competitive Enterprises

Customer churn predication is regarded as one of the most vital issues in organizations; especially in the organizations which belong to the competitive industry, where maintaining the existing customers in the organization is far more beneficial as compared to attracting new customers. In this paper, the existing approaches and methods of customer churn prediction is presented with the focus on the machine learning approaches. In the classification algorithms we examine logistic regression, decision trees, random forests, support vector machines, ensemble methods and neural networks. Furthermore, the most common approaches in the context of data preprocessing are discussed, such as data cleaning, feature construction, and dealing with imbalanced data. This encompasses accuracy, precision, recall, F1 score, and AUC-ROC are presented to compare the performance of the models. It also presents real-life examples from the telecommunication, banking, e-commerce, subscription, and online gaming industries. The key issues in churn prediction analysis such as data quality, imbalanced dataset, behavioral change of customers and interpretability of models are discussed. Lastly, we indicate potential avenues for further research, like: integration into CRM systems, real-time analytics, individual retention strategies, and the use of XAI. The goal of this survey is to present a state of art in customer churn prediction discussing useful strategies in the field and pointing out the directions for further investigation.