A classification model works by analyzing historical data to identify patterns and trends. It uses various features such as open rates, click-through rates, past purchase behavior, and demographic information to make predictions about future behavior. The algorithm is trained on a labeled dataset, where the known outcomes are already categorized. Once trained, the model can then classify new, unseen data into the appropriate categories.