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https://arxiv.org/abs/2606.20065

[2606.20065] Generative Engine Optimization at Scale: Measuring Brand Visibility Across AI Search Engines Skip to main content Search arXiv Press Enter to search · Advanced search Computer Science > Information Retrieval arXiv:2606.20065 (cs) [Submitted on 18 Jun 2026] Title:Generative Engine Optimization at Scale: Measuring Brand Visibility Across AI Search Engines Authors:Pratyush Kumar (Ranqo) View a PDF of the paper titled Generative Engine Optimization at Scale: Measuring Brand Visibility Across AI Search Engines, by Pratyush Kumar (Ranqo) View PDF HTML (experimental) Abstract:People increasingly get answers straight from AI search engines like ChatGPT, Claude, Perplexity, and Gemini rather than scrolling search results. Brands that once focused on search engine optimization (SEO) must now optimize for how these engines represent, cite, and recommend them -- a shift variously called Generative Engine Optimization (GEO), Answer Engine Optimization (AEO), and AI Search Visibility. We treat AEO and AI Visibility as part of GEO, and study how to measure brand visibility across AI engines: what they value when they cite a brand, which sources they rely on, and what content large language models surface. The hard case is everyone outside the already-authoritative top brands -- SMEs, D2C brands, creators, and early-stage startups.

We analyze 100K+ prompt responses across 100+ brands tracked on Ranqo between March and May 2026. First visibility runs form a clear three-tier brand-stature ladder: global household names (e.g., Stripe, Nike) appear in 73% of relevant AI answers on their first run; established mid-market and regional brands (e.g., Olipop, Klaviyo) in 44%; niche and small brands in just 11% -- about 30 percentage points per step. When engines cite sources, about 78% go to corporate websites; among non-corporate sources YouTube leads, ahead of Reddit, editorial media, and Wikipedia. The highest-leverage page is the ranked "best-of" listicle, the most-cited content format at about 21% of all citations. Sentiment is the unstable signal: whether a brand is framed positively or negatively flips about 6.7 times more often than whether it is mentioned at all. These findings provide a first large-scale baseline for measuring GEO: AI brand visibility can be measured, differs by platform, and varies strongly by brand maturity.

We close by proposing seven v1.1 protocols to test whether specific recommendations can causally improve AI visibility. Comments: 14 pages, 4 tables; v1.0 preprint Subjects: Information Retrieval (cs.IR); Computation and Language (cs.CL); Computers and Society (cs.CY) ACM classes: H.3.3 Cite as: arXiv:2606.20065 [cs.IR] (or arXiv:2606.20065v1 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2606.20065 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Pratyush Kumar [view email] [v1] Thu, 18 Jun 2026 10:36:13 UTC (29 KB) Full-text links: Access Paper: View a PDF of the paper titled Generative Engine Optimization at Scale: Measuring Brand Visibility Across AI Search Engines, by Pratyush Kumar (Ranqo) View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.IR < prev | next > new | recent | 2026-06 Change to browse by: cs cs.CL cs.CY References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

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