Generative Marketing Mix Modeling: A Causal Inference Framework Linking GEO and GEM to Business Impact
Paper recorded by Signals 4 on 2026-09-10 in cs.AI. Abstract reproduced from arXiv; link to the original below.
Published 2026-09-10 on arXiv · recorded by Signals 4 on 2026-09-11
Category: cs.AI · 人工智能 · first seen 2026-09-11
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 ques
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Cite this page: Generative Marketing Mix Modeling: A Causal Inference Framework Linking GEO and GEM to Business Impact: the #104 most recent of 300 cs.AI papers we have recorded (as of 2026-09-10). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/generative-marketing-mix-modeling-a-causal-inference-framework-linking-geo-and-g.html
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