Generative Engine Optimization at Scale: Measuring Brand Visibility Across AI Search Engines
A large-scale measurement of how 102 brands surface across ChatGPT, Gemini, Perplexity, Claude, and Grok — and what AI engines actually cite.
Abstract
People increasingly get answers straight from AI assistants — ChatGPT, Claude, Perplexity, Gemini, Grok — instead of scrolling ten blue links. For a brand, the question that matters has changed: not whether you rank for a keyword, but whether an AI model names you when someone asks about your category. This is the work of Generative Engine Optimization (GEO), which subsumes Answer Engine Optimization (AEO) and AI Search Visibility.
We measure brand visibility and analyze responses across the five major AI search engines, looking at what they appear to value when they cite brands, what sources they rely on, and what content LLMs are more likely to surface. The already-authoritative brands get cited naturally; the harder, more important problem belongs to everyone else — SMEs, D2C brands, creators, and early-stage startups without much of an online footprint.
We demonstrate our findings on 100K+ prompt responses across 100+ brands tracked on Ranqo between March and May 2026. The first visibility runs form a clean three-tier brand-stature ladder (73% / 44% / 11%). When the engines cite sources, about 78% go to corporate websites — mostly third-party brand pages, not the brand's own. The highest-leverage citation surface is the ranked listicle. Sentiment is the unstable part, flipping about 6.7× more often than whether a brand is mentioned at all. We close by proposing seven v1.1 protocols to test whether specific recommendations can causally improve AI visibility.
Key findings
Global brands appear in 73% of unbranded AI answers, mid-market 44%, niche 11% — about 30 points per step down (Cohen's d up to 2.34).
Only 2.9% of 149,912 AI citations point to a brand's own site; 75.2% point to corporate and competitor pages.
The ranked best-of list is the single highest-leverage page — one list surfaces a brand across many AI answers.
Whether AI frames a brand positively flips 6.7× more often than whether it mentions the brand at all.
AI visibility is a brand-stature ladder
The headline result is also the most robust one. On a brand's very first tracking run, unbranded category visibility falls into three clean tiers: global household names like Stripe and Nike appear in 72.9% of relevant AI answers, established mid-market brands like Olipop and Klaviyo in 43.6%, and small or niche brands in just 11.4%. Each step down the ladder costs roughly 30 percentage points of visibility.
A Kruskal–Wallis test rejects equality of the tier distributions (H = 38.32, p = 4.8 × 10⁻⁹), with large effect sizes throughout (Cohen's d up to 2.34). Because stature is observed rather than randomized, we report this as a quantification of an expected effect, not a causal claim — but it is the first multi-tenant measurement of the gap we found in the GEO literature we surveyed.
The brand-stature visibility ladder
Day-1 unbranded category visibility by brand tier (first tracking run, 95% CI)
Brands surface on day one — when they're named
Practitioner folklore says AI visibility takes six months. The data doesn't support that, with one qualification: it depends on whether the prompt names the brand. When a prompt names the brand, every engine recognizes it immediately — 94–100% on the first run. When it doesn't, recognition drops sharply and tracks the stature ladder above.
Day-1 recognition: named vs unnamed
First-run mention rate per engine — brands surface immediately when named, far less when not
Your own website is only 2.9% of citations
Across 149,912 citations, only 2.9% point at the brand's own domain. The dominant class — 75.2% — is corporate pages owned by other companies in the same space: competitors, peers, and vendors. AI engines preferentially build “alternatives” answers, and the sources behind those answers are peer-brand pages, not your site. Among non-corporate sources, video leads: YouTube is cited more often than editorial media, Reddit, or Wikipedia.
Where AI citations point
Share of 149,912 source citations by class — your own domain is the smallest slice
The listicle is the highest-leverage page in AI search
When an engine cites a page, about 59% of the time it is content rather than a homepage or product page. Within that content, one format dominates: the ranked “best-of” listicle is 35.7% of content citations — about 21% of all citations. Once a ranked list includes a brand, that single page becomes a source the engines reuse across many different prompts — which makes it the single highest-leverage surface a brand can target.
The listicle leads every content format
Share of content-level citations by page format — the ranked best-of list dominates
Mention is near-binary; you still can't measure it once
For every (brand, prompt, engine) cell tracked across at least three runs, mention behavior is near-deterministic: 77.5% of cells are strictly always- or never-mentioned, and only 6.8% flip run-to-run. Visibility is closer to a fixed property of the cell than a coin flip — but the 22.5% in the middle is exactly where measurement noise concentrates, so single-run readings mislead for cells near the boundary. This echoes independent work on AI-search measurement (Schulte et al., “Don't Measure Once”): visibility is a distribution, not a single-point outcome.
Sentiment is 6.7× noisier than mention
Whether an engine frames a brand positively or negatively flips 45.5% of the time, against 6.8% for mention — 6.7× noisier. Sentiment-weighted scores need a much larger sample before they stabilize. One sharper note: not a single cell is consistently negative. When negativity surfaces, it is transient, never systematic.
What this does — and doesn't — establish
This is a measurement study, and a vendor-produced one: Ranqo built and runs the platform analyzed here. It establishes baselines, trajectories, and source and sentiment composition. It does not claim that acting on Ranqo's recommendations causally lifts visibility — that is the randomized closed-loop trial we lay out as the v1.1 protocol slate. The paper names its own limits, in detail, in §8. For a practitioner translation of the measurement choices behind share of voice, see our share-of-voice guide.
Methodology & dataset
Ranqo issues controlled, unbranded category prompts to five AI engines via their official APIs and records, for each (prompt, platform, run) tuple, whether a brand is mentioned, its position, the sentiment of the mention, and every source the engine cited. Unbranded prompts are the GEO-relevant measure: they test whether an engine surfaces a brand when nobody has named it.
Uncertainty on means and slopes uses a nonparametric bootstrap (10,000 resamples). The three-tier visibility comparison uses a Kruskal–Wallis omnibus test, pairwise Mann–Whitney U tests with Bonferroni correction, and Cohen's d for effect size, with a leave-one-out sensitivity check on the small Tier 1 cell. This is a vendor-produced measurement study; the paper states its limits and non-causal scope explicitly.
Observation window: March–May 2026Engines: ChatGPT, Gemini, Perplexity, Claude, Grok
Cite this paper
@article{ranqo2026geo,
title = {Generative Engine Optimization at Scale: Measuring Brand
Visibility Across AI Search Engines},
author = {{Ranqo}},
year = {2026},
eprint = {2606.20065},
archivePrefix = {arXiv},
primaryClass = {cs.IR},
doi = {10.48550/arXiv.2606.20065},
url = {https://arxiv.org/abs/2606.20065}
}References & further reading
From the Ranqo research library
- GEO vs AEO vs SEO: Three Measurement Views of the Same Work
- What AI Platforms Really Recommend When You Ask About CRM Software
- What is Generative Engine Optimization (GEO)? The Complete 2026 Guide
- The 5 Factors That Determine Whether AI Cites Your Brand
- How to Get Cited by Perplexity: The Citation-Engine Playbook
- AI Visibility for SaaS: The Complete B2B Playbook
- AI Visibility for E-commerce & DTC Brands: It's Research-and-Handoff, Not Search
- The E-E-A-T Playbook for AI Citations: Visible Authority Beats Markup Theatre
- Schema Markup for AI Citations: A Complete Guide
- How to Measure AI Share of Voice: The Three Decisions That Change the Number
Prior academic work
- Aggarwal et al. (2024) — GEO: Generative Engine OptimizationarXiv:2311.09735 · KDD 2024
- Puerto et al. (2025) — C-SEO Bench: Does Conversational SEO Work?arXiv:2506.11097 · NeurIPS Datasets & Benchmarks 2025
- Yang (2025) — News Source Citing Patterns in AI Search SystemsarXiv:2507.05301
- Kirsten et al. (2025) — Characterizing Web Search in the Age of Generative AIarXiv:2510.11560
- Algaba et al. (2025) — How Deep Do LLMs Internalize Scientific Literature and Citation Practices?arXiv:2504.02767
- Schulte et al. (2026) — Don't Measure Once: Measuring Visibility in AI Search (GEO)arXiv:2604.07585
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