A new framework for Marketing Mix Modeling: Addressing Channel Influence Bias and Cross-Channel Effects
This addresses challenges in marketing analytics for businesses, providing more accurate attribution and resource allocation, but it is incremental as it builds on existing physics-inspired methods.
This research tackled the problems of channel influence bias and cross-channel effects in Marketing Mix Modeling by integrating the Michaelis-Menten equation and Maxwell-Boltzmann kinetic theory into hierarchical Bayesian models, resulting in maintained predictive accuracy with superior analytical insights into channel effectiveness and interactions.
This research addresses two fundamental challenges in Marketing Mix Modeling: the tendency of models to over-attribute influence to high-investment channels and the difficulty in quantifying cross-channel effects. We propose integrating the Michaelis-Menten equation and Maxwell-Boltzmann kinetic theory into hierarchical Bayesian models to overcome these limitations. Our approach uses the Michaelis-Menten model to characterize shape effects with spending-independent parameters and Boltzmann-type equations to systematically quantify cross-channel dynamics. Experimental results show that this physics-inspired approach maintains predictive accuracy while providing superior analytical insights into channel effectiveness and interactions. The normalized Michaelis-Menten constant offers an investment-independent measure of channel efficacy, while the N-particle system simulation reveals previously ignored channel interdependencies, enabling more accurate attribution and informed resource allocation decisions.