# A new framework for Marketing Mix Modeling: Addressing Channel Influence Bias and Cross-Channel Effects

arXiv: https://arxiv.org/abs/2311.05587 · HTML: https://mlsanity.com/paper/2311.05587/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 48 references that are in the map.
- First posted on arXiv 9 Nov 2023.

## Builds on (most cited first, 1 of 1)

- [Hierarchical Marketing Mix Models with Sign Constraints](https://mlsanity.com/paper/2008.12802/) (2020)

## Abstract (arXiv)

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.