arxiv.orgpaper

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

This paper introduces Generative Marketing Mix Modeling (GMMM), a causal inference framework for estimating the business impact of Generative Engine Optimization (GEO) and Generative Engine Marketing (GEM) — i.e., how often a firm's name appears in AI-generated answers and sponsored placements within generative systems. GMMM combines repeated generated answers, question volumes, usage shares across generative systems, and notice probabilities to identify treatment effects, comparing expected business responses under alternative treatment sequences. The authors test the method on simulated product-recommendation answers in English and Japanese.