Research

Quantitative research from people
who've built careers on this

The research and white papers published here are the product of a team with decades of combined experience across data science, statistics, and performance marketing.

Featured

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.

June 1, 2025
Cody Greco Philip Hofmeister
Cody Greco, Philip Hofmeister

All research

Are You Underspending? Challenging Saturation Assumptions in Marketing Mix Modeling

Marketing Mix Modeling (MMM) is widely used to determine how much to spend on each marketing channel, yet nearly every major open-source framework, including Google's Meridian, Meta's Robyn, and PyMC-Marketing, defaults to a saturating response curve that assumes diminishing returns at every spend level. This paper tests that assumption empirically using 3,509 daily observations across 20 Facebook campaigns from an e-commerce brand. We compare two standard saturation forms (Hill and Weibull) against a simple linear model and find that the linear specification outperforms both on the majority of campaigns, suggesting that default saturation assumptions lead to materially lower spend recommendations and leave significant revenue uncaptured.

Katelyn Bischoff Judith Degen Cody Greco
Katelyn Bischoff, Judith Degen, Cody Greco
June 22, 2025

About our research

Deep expertise

The research and white papers published here are the product of a team with decades of combined experience across data science, statistics, and performance marketing. Our researchers hold advanced degrees in quantitative disciplines and have spent their careers studying the problems that make marketing measurement unreliable at scale.

Rigorous standards

That depth of expertise shapes everything from how we frame a research question to the standards we hold ourselves to before anything gets published. Every research paper and white paper goes through rigorous internal review to make sure the methodology is sound, the claims are defensible, and the findings are genuinely useful to the marketing community.

Research-first

At Prescient AI, we operate as a research organization first. Our work isn't produced to support a marketing agenda or validate a predetermined conclusion. It's designed to advance the field's understanding of marketing mix modeling, attribution, and media measurement in ways that hold up under scrutiny.

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