Bridging the Gap: An Explainable Methodology for Customer Churn Prediction in Supply Chain Management

Abstract

Customer churn prediction is a critical task for businesses aiming to retain their valuable customers. Nevertheless, the lack of transparency and interpretability in machine learning models hinders their implementation in real-world applications. In this paper, we introduce a novel methodology for customer churn prediction in supply chain management that addresses the need for explainability. Our approach take advantage of XGBoost as the underlying predictive model. We recognize the importance of not only accurately predicting churn but also providing actionable insights into the key factors driving customer attrition. To achieve this, we employ Local Interpretable Model-agnostic Explanations (LIME), a state-of-the-art technique for generating intuitive and understandable explanations. By utilizing LIME to the predictions made by XGBoost, we enable decision-makers to gain intuition into the decision process of the model and the reasons behind churn predictions. Through a comprehensive case study on customer churn data, we demonstrate the success of our explainable ML approach. Our methodology not only achieves high prediction accuracy but also offers interpretable explanations that highlight the underlying drivers of customer churn. These insights supply valuable management for decision-making processes within supply chain management.