The math behind
better marketing decisions

OMEN: Identifiability, Inductive Bias, and Mechanistic Modeling in Marketing Mix Models

Marketing Mix Modeling (MMM) is widely used to guide large-scale marketing investment decisions, yet practitioners routinely observe instability, implausible elasticities, and contradictory budget recommendations across model implementations. We argue that the primary explanation is structural: the separable, additive factorization used by most MMMs conflicts with actual marketing system dynamics, leading to non-identifiability under realistic conditions. We compare OMEN, a mechanistic and nonlinear MMM, against two widely used open-source baselines using a synthetic, agent-based marketing environment with known causal ground truth. Across ten independently generated datasets, OMEN exhibits substantially lower error in recovering true incremental contributions, sharply reduced baseline leakage into media attribution, higher peak-lift fidelity during high-demand periods, and markedly lower optimization regret under counterfactual budget shifts.

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