Customer churn prediction for a personal care product retail chain operating in Turkey

Understanding the reasons for customer loss and the customer behaviors leading to it, as well as being able to predict customer's loyalty to an industry or a company provides enormous advantages in retaining existing customers and avoiding revenue loss due to the marketing and advertising costs associated with attracting new customers. In this study, the 29-month data from a personal care product retail chain operating in Turkey was used, and because of the imbalanced values and non-customer entries of the dataset, the oversampling method and synthetic sampling was applied. During the model development phase, Logistic Regression, Decision Tree, K-Nearest Neighbors, Random Forest, Extra Trees Classifier, and MLP (Multi-Layer Perceptron) Classifier were applied, and their performances were evaluated using metrics such as accuracy, recall, F1-score, precision, and confusion matrix. Based on these comparisons, it was observed that the Random Forest and MLP Classifier models demonstrated the best performances for this dataset, while other tree-based algorithms, such as the Extra Trees Classifier and Decision Tree, achieved slightly lower but comparable performance.