Model performance monitoring is a way to ensure deployed models are sustainable and performing optimally over time. Often models are built and deployed and  the system performance, like model  failures and records processed, are monitored to ensure the model is running. While this type of monitoring is important, system monitoring cannot tell if the model is performing as it was intended or is beginning to decay. Model performance monitoring goes deeper, evaluating the model inputs and outputs to detect more subtle signals that a model may not be delivering its intended value before the model starts to impact key business decisions. 

External events can have a dramatic effect on consumer behavior.  Covid 19 is a great example of that.  Businesses and offices closed, shopping behavior changed, and people stopped traveling. The repercussions for predictive models was often very dramatic.  There are ways to mitigate the damage to your models. Read more in this articled, entitled “The Covid 19 Butterfly Effect”.