Which models dominate churn prediction? Insights from 240 ML/DL studies (2020–2024)

An interesting comprehensive review of 240 studies shows how ML & DL are reshaping churn prediction, highlighting trends, gaps, and a roadmap for future research.

🔹 ML models trends → Random Forest and Boosting lead with steady growth, while Logistic Regression and SVM remain staples. KNN and Naïve Bayes lag behind.

🔹 DL models trends → Deep Neural Networks dominate. CNNs, RNNs, LSTMs, and even Transformers appear, but at smaller scales.

👉 Together, the field still leans heavily on tree-based ML, while DL is emerging for richer and sequential data.

Full open-access review: https://www.mdpi.com/3508932

💬 What’s your experience — do RF/XGBoost still win in production churn tasks, or are DL approaches catching up?