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Est. 2023

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One treats it as a standard classification task — predicting whether a customer will churn within a specified time frame (binary outcome). The other uses survival analysis to forecast when a customer is likely to churn. A variety of machine learning techniques are employed, including logistic regression, decision trees, gradient boosting, and random forests, with newer approaches using deep learning models such as RNNs and transformers for sequential data. When the timing of churn matters, survival models like the Cox proportional hazards model, random survival forests, and deep survival networks are particularly valuable. Recent research consistently shows that ensemble and advanced machine learning methods generally outperform simpler individual models, while survival-based approaches provide richer insights into the temporal dimension of churn.