Propensity models are monitored using confidence-based performance estimation (CBPE). The idea behind CBPE is that the ground truth for classification models is not always available post-deployment, and when it is it may be delayed. You generally want to know if your model is drifting right away, not months from now.

It turns out we can estimate the live performance of the production model by looking at both binary predictions and the associated probability (score). Using both gives us a sense of the confidence in the answer, meaning we can reliably estimate the current confusion matrix of the production model and therefore its current F1 score.

By comparing the model's estimated F1 over time with its expected value computed during the training phase you can get a sense of whether your model is experiencing any data drift or not. The G2M platform model monitoring page will also provide upper and lower bounds for your model's estimated F1 to indicate whether model performance is within its expected range or not.

Note that the CBPE approach will detect data drift and covariate shift but not concept drift. Concept drift is hard to detect methodically, so you should always be on the lookout for domain-specific changes which might invalidate your model, like know business or market transitions.

More details on the CBPE approach can be found here.