Abstract

Marketing Mix Modeling (MMM) traditionally employs statistical metrics such as R-squared, Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and Akaike Information Criterion (AIC) for model calibration and evaluation. These metrics, while insightful, often fall short in addressing the complexities of real-world scenarios. This report explores advanced analytical techniques, focusing on the Probability Integral Transform (PIT) residuals and Kullback-Leibler (KL) divergence, to enhance the calibration of MMM. Our findings indicate significant deviations from uniformity in the PIT residuals for both optimal and suboptimal models, with the best model demonstrating lower KL divergence, suggesting a closer fit to the expected uniform distribution. This study underscores the value of incorporating advanced metrics for a more nuanced understanding of MMM calibration, beyond conventional evaluation methods.