Marketing mix modeling
How Bayesian regression and repeated simulation help estimate channel contribution, uncertainty, saturation, and better budget ranges.
A model of the business, not a prettier attribution report
A marketing mix model relates outcomes such as revenue or qualified leads to media spend while accounting for seasonality, pricing, promotions, distribution, economic conditions, and other drivers. It is especially useful when click-level attribution is incomplete or misleading.
Why Bayesian
Bayesian regression does not return one falsely precise answer. It estimates a distribution of plausible effects. Prior knowledge can be included transparently, sparse channels can be regularized, and the output carries credible ranges that decision-makers can inspect.
Where Monte Carlo fits
Sampling methods run the model many times across plausible parameter combinations. Those repeated draws reveal peaks, valleys, diminishing returns, and uncertainty. Budget scenarios can then be simulated against the full range of likely responses instead of a single average coefficient.
What optimization actually means
The goal is not to hand every dollar to the channel with the highest historic ROAS. A useful optimizer respects saturation, minimum commitments, channel interactions, testing budgets, operational capacity, and the cost of being wrong.