Once a marketing mix model is trained the following error metrics are generated and shown in two sets, one for the training dataset, one for the testing dataset (usually the last 13 periods of the full dataset):
r2: the Pearson r2, defined as the square of the Pearson correlation, is a measure of whether there is any linear relationship between predicted and actual outcomes. It ranges from 0 to 1. In cases with strong model bias you may have low R2 and high r2: the predictions may be not good due to their bias but there is a straightforward relationship between the biased predictions and the actual outcomes.
RMSE: the root mean square error (RMSE) measure the average difference between predicted outcomes and actual outcomes. It is defined mathematically as the standard deviation of the residuals. It is used by many regression algorithms as the objective function to be minimized.
MAPE: the mean absolute percentage error (MAPE) is very similar to MAE and is defined as the average absolute percentage value of the difference between predictions and actuals. The value is expressed as a number, not a percentage, meaning that 100 means 1e2 and not 100%. In cases where actual outcomes are small MAPE can be quickly biased to be very large as the averaging is not weighted.