Generative Marketing Mix Modeling: A Causal Inference Framework Linking GEO and GEM to Business Impact
Abstract
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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BibTeX
@misc{kato2026generative,
title = {{Generative Marketing Mix Modeling: A Causal Inference Framework Linking GEO and GEM to Business Impact}},
author = {Masahiro Kato and Daiki Honma and Taka Kato},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.11915},
eprint = {2609.11915},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.11915},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Generative Marketing Mix Modeling: A Causal Inference Framework Linking GEO and GEM to Business Impact
AU - Masahiro Kato
AU - Daiki Honma
AU - Taka Kato
PY - 2026
DA - 2026/09/10
PB - arXiv
DO - 10.48550/arxiv.2609.11915
UR - https://arxiv.org/abs/2609.11915
AB - 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.
ER -