Two MMM Methodologies
Linear regression MMM (OLS or ridge regression on transformed variables) and Bayesian MMM are two methodologies for the same problem. Linear regression is the historical standard; Bayesian is the modern default since the early 2020s. Both produce channel contribution estimates and response curves; they differ in how they handle uncertainty, priors, and sparse data.
What Linear Regression MMM Does
Apply adstock and saturation transforms to media variables using fixed hyperparameters (often selected via grid search). Fit a regression (OLS, ridge, or LASSO) of the outcome on the transformed variables. Report channel coefficients with frequentist confidence intervals derived from sampling theory. Simpler, faster, and easier to explain to non-statisticians than Bayesian.
What Bayesian MMM Does
Specify prior distributions on every parameter including adstock, saturation, and channel coefficients. Use MCMC or variational inference to compute posterior distributions for all parameters jointly. Report contribution decomposition with credible intervals derived from the posterior. More sophisticated, handles sparse data and multicollinearity better, but slower and harder to explain.
Where Each Is Appropriate
Linear regression: brands with very long history (5+ years of weekly data), few channels (under 10), no informative priors to bring, and a need for fast iteration. Bayesian: brands with shorter history, many channels, informative priors from category benchmarks, and the need for full posterior uncertainty in decision-making. Most modern MMMs fit the Bayesian profile, which is why Bayesian has become the default.
How They Differ in Practice
For AI search variables specifically, the difference matters a lot. AI visibility data is typically short (one to two years); linear regression methods struggle to identify a clean coefficient on the AI variable with that short history. Bayesian methods compensate via informative priors derived from category benchmarks, producing usable AI coefficients with the limited data available.
Feature Comparison
| Dimension | Linear Regression MMM | Bayesian MMM |
|---|---|---|
| Methodology | OLS, ridge, or LASSO | MCMC or variational inference |
| Prior incorporation | Limited (regularization) | Full priors on all parameters |
| Handles sparse data | Poorly | Well (with informative priors) |
| Handles multicollinearity | Poorly | Well |
| Uncertainty reporting | Frequentist CIs | Full posterior credible intervals |
| Computational cost | Low (minutes) | Higher (hours) |
| AI variable identification | Limited with short history | Strong with informative priors |
| Modern frameworks | Custom Python/R, some legacy vendors | Robyn, LightweightMMM, PyMC-Marketing, modern vendors |
The Practical Choice
For new MMM builds in 2026: Bayesian. The methodology better matches the data realities (sparse, multicollinear, short-history channels including AI search) and the modern frameworks have made Bayesian inference accessible to teams without dedicated econometrics staff. Linear regression MMMs are still common in legacy implementations but should not be the choice for new builds.
How Presenc AI Helps
Presenc AI publishes prior guidance designed for Bayesian MMM workflows. The category-level adstock and saturation priors for the AI variable are the operational difference between strong and weak AI coefficient identification in Bayesian models with short AI visibility history.