Generative Marketing Mix Modeling: A Causal Inference Framework Linking GEO and GEM to Business Impact
Paper proposes Generative Marketing Mix Modeling to causally estimate Generative Engine Optimization and Marketing effects on business outcomes.
The authors develop GMMM, a causal inference framework for measuring how often users see and notice a firm's name in generated answers, which standard marketing data ignore. For GEO it combines repeated generated answers with question counts, shares of generative-system usage and notice probabilities; for GEM it uses sponsored placement records with notice probabilities. The framework compares expected business responses under alternative treatment sequences, establishes identification conditions, and is evaluated on simulated product-recommendation answers in English and Japanese.
- GMMM estimates causal effects of GEO and GEM on business outcomes.
- Combines generated-answer exposure, question counts, usage shares and notice probabilities.
- Validated only on simulated English and Japanese product recommendation scenarios.
- Products
- GMMM
From the original article
1 min read at arxiv.orgGenerative 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).
By Masahiro Kato · arxiv.org
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