Model performance
Key findings
. The dominant source of MMM failure is not noise, insufficient data, or imperfect regularization, it's misalignment between the model's hypothesis class and the causal structure of real marketing systems.
. When spend is correlated with demand (as it always is in practice), separable MMMs become under-identified. No amount of data, regularization, or hyperparameter tuning can make a misspecified MMM reliable.
. We identify seven constraints that marketing systems exhibit—funnel directionality, nonstationary efficiency, memory and latency, flexible response shapes, cross-channel interaction, exogenous modulation, and non-universal saturation—that separable models cannot represent.
. Rather than decomposing outcomes into baseline and additive channel effects, OMEN models marketing as a system evolving over time. Baseline demand and marketing effects emerge jointly from system dynamics rather than being specified independently.
For practitioners, unstable or implausible attribution outputs should not be interpreted as tuning failures; they are often indicators that the model lacks the capacity to capture the system being measured. Progress in MMM will depend less on incremental algorithmic refinement and more on rethinking the structural assumptions that define the problem.