Masahiro Kato, Daiki Honma, Taka Kato
arXiv 10 Sep 2026 · Statistics — Machine Learning
arXiv:2609.11915 · PDF · Extracted main text
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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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Pierre E. Jacob, Lawrence M. Murray, Chris C. Holmes, and Christian… Better together? statistical learning in models made of modules, 2017 | 0.644 | 2 | 2 | 100% |
| 2 | Yuxue Jin, Yueqing Wang, Yunting Sun, David Chan, and Jim Koehler (2017) Bayesian methods for media mix modeling with carryover and shape effects | 0.644 | 2 | 2 | 100% |
| 3 | Martyn Plummer (2015) Cuts in bayesian graphical models | 0.644 | 2 | 2 | 100% |
| 4 | James Robins (1987) A graphical approach to the identification and estimation of causal parameters in mortality studies with sustained exposure peri… | 0.511 | 2 | 1 | 100% |
| 5 | Adivit Acharya, Matthew Blackwell, and Maya Sen (2016) Explaining causal findings without bias: Detecting and assessing direct effects | 0.405 | 1 | 1 | 100% |
| 6 | Pranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan… (2024) Geo: Generative engine optimization | 0.405 | 1 | 1 | 100% |
| 7 | Puneet S. Bagga, Vivek F. Farias, Tamar Korkotashvili, Tianyi Peng,… (2026) E-geo: A testbed for generative engine optimization in e-commerce, 2026 | 0.405 | 1 | 1 | 100% |
| 8 | Christian Carmona and Geoff Nicholls (2020) Semi-modular inference: enhanced learning in multi-modular models by tempering the influence of components | 0.405 | 1 | 1 | 100% |
| 9 | David Chan and Mike Perry (2017) Challenges and opportunities in media mix modeling | 0.405 | 1 | 1 | 100% |
| 10 | Aiyou Chen and Timothy C. Au (2022) Robust causal inference for incremental return on ad spend with randomized paired geo experiments | 0.405 | 1 | 1 | 100% |
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