Churn prediction models utilize machine learning algorithms and data analytics to forecast which subscribers are at risk of churning. These models analyze various data points such as open rates, click-through rates, engagement history, and more to make informed predictions. Some common techniques include logistic regression, decision trees, and neural networks.