Churn is a metric widely used by companies to quantify customer attrition, defined as the event in which a customer terminates a contractual relationship or discontinues purchasing a company’s products or services. This work addresses the prediction of churn in B2B customers in the agribusiness sector, utilizing machine learning techniques applied to a real dataset with a three-year transaction history. The study evaluates three algorithms that are widely used in the field: Logistic Regression, Random Forests, and XGBoost. The main objective is to analyze the predictive stability of these models over time (Out-of-Time analysis), without re-training, verifying their ability to maintain consistent performance on future data.

The predictive quality was gauged utilizing the AUC-ROC metric, supplemented by a 10-fold cross-validation technique to assess the robustness of the models. The results revealed that the Random Forest model exhibited enhanced stability and superior overall performance, with AUC-ROC varying between 0.849 and 0.877 in the four quarters analyzed, while Logistic Regreesion and XGBoost showed greater variability. The Student's t-test confirmed a statistically significant difference between RF and LR (\(p \leq 0.05\)).

The conclusions of this analysis serve to fortify the practical applicability of the proposed methodology. This applicability manifests in the form of a reduction in operating costs associated with frequent model maintenance and the facilitation of customer retention strategies.