Generative Marketing Mix Modeling: A Causal Framework Linking GEO and GEM to Business Impact

As generative AI engines increasingly mediate product discovery, conventional marketing analytics struggle to quantify brand visibility embedded in synthesized textual answers. To bridge this gap, the authors introduce Generative Marketing Mix Modeling (GMMM), a causal inference framework designed to isolate the business impact of Generative Engine Optimization (GEO) and Generative Engine Marketing (GEM). By modeling repeated prompt sampling, platform market shares, and notice probabilities, the method establishes formal identification conditions across simulated English and Japanese product recommendations.