{
  "id": 6702081,
  "title": "Generative Marketing Mix Modeling: A Causal Inference Framework Linking GEO and GEM to Business Impact",
  "url": "https://urgent.news/2026/09/10/generative-marketing-mix-modeling-a-causal-inference-framework",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-10T17:57:28.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.11915v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "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…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}