ChurnNet: A Optimized Modern AI for Churn Prediction
For practitioners in customer retention, this paper shows that simpler models can be more effective than complex deep learning approaches for churn prediction.
The study compares traditional ML methods (Random Forests, XGBoost, SVM) with a modern time-series model for churn prediction, finding that conventional methods outperform the modern model in predictive performance, data efficiency, and computational requirements across multiple datasets.
Increased competition and the growing similarity of products and services offered by retailers have lowered the barriers for customers to switch to competitors. Accurate churn prediction can be a valuable tool for driving effective personalized marketing campaigns and helping to reduce customer attrition. This study evaluates the performance of traditional machine learning techniques, namely, Random Forests, XGBoost, and Support Vector Machines, and compares them with the Unified Multi-Task Time Series Model for churn prediction, a binary time-series classification task. Despite the strong capacity of the latter to model complex temporal dynamics and inter-variable relationships, our results indicate that for churn prediction, conventional methods can still outperform it in terms of predictive performance, data efficiency, and computational resource requirements for training and deployment. These findings are consistent across multiple datasets and various churn labeling techniques.