Marketing Mix Modeling (MMM) open-source solutions: A comparative analysis of Robyn, Meridian, and PyMC Marketing libraries.

M. Franco Pérez, X. Puig Oriol

Assessing advertising profitability is critical for data-driven marketing. Marketing Mix Modeling (MMM) quantifies the impact of advertising expenditure on KPIs—typically sales—by accounting for adstock (lag-stocked effect) and saturation (non-linear transformation). These models incorporate temporal components, such as trend and seasonality, alongside external covariates as controls.

With the rise of open-source frameworks, three libraries have gained prominence among practitioners: Robyn (Meta®) [Machine Learning], Meridian (Google®), and PyMC Marketing (PyMC Labs®) [both Bayesian approach]. This work studies the modelling alternatives available within each solutions and details their respective estimation procedures. Finally, we present a comparative performance analysis across various simulated scenarios, offering insights into their behavior, relative strengths, and the persistent statistical challenges for each solution individually and for the MMM domain as a whole.

Palabras clave: Marketing Mix Modelling (MMM), Sales Attribution, Lag-Stock Models, Non-linear Saturation effect.

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