arXiv paper proposes causal framework for measuring generative AI marketing impact

A new arXiv preprint introduces Generative Marketing Mix Modeling, a causal inference approach for estimating how exposure to a brand's name inside AI-generated answers affects business outcomes. The method links generative engine optimization and generative engine marketing metrics to sales impact, since conventional marketing datasets do not capture how often users see or notice a company's name in generated responses.