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Life sciences · Preprint
arXiv · September 10, 2026
Raises a question worth testing. It does not answer one.
This is a theoretical causal inference framework (GMMM) designed to estimate business impact of generative engine optimization and marketing. Validation is limited to simulated data only, with no empirical demonstration on real marketing outcomes or actual user behavior.
Methodological framework with simulation validation. Intervention: Generative Engine Optimization (GEO) and Generative Engine Marketing (GEM) placements modeled via GMMM framework. Simulations in English and Japanese; no geographic setting specified..
Framework combines generated answer frequency, question counts, system share, and notice probabilities to estimate GEO causal effects Framework integrates sponsored placement records and notice probabilities for GEM causal effects Empirical performance investigated using simulated answers to product recommendations in English and Japanese
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A methodological framework with simulated data that raises questions about causal inference in generative AI marketing; not yet validated on real business outcomes or empirical data.
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Generative artificial intelligence changes how firms reach customers, but standard marketing data do not record how often users see and notice a firm's name in generated answers. We develop Generative Marketing Mix Modeling (GMMM) to estimate the causal effects of Generative Engine Optimization (GEO) and Generative Engine Marketing (GEM). For GEO, GMMM combines repeated generated answers with question counts, shares of use across generative systems, and notice probabilities. For GEM, it combines records of sponsored placements with notice probabilities. GMMM compares expected business responses under alternative treatment sequences and establishes sufficient conditions for identifying the resulting effects. We investigate the empirical performance of the proposed method using simulated answers to product recommendation in English and Japanese.
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