- The paper establishes GEO as a novel framework evaluating brand visibility in AI search results using controlled, unbranded queries.
- It introduces a multi-dimensional scoring method that combines boolean mentions, ordinal positions, and sentiment metrics to stratify brands into three tiers.
- Findings reveal rapid, engine-specific visibility changes driven by content quality and citation patterns, informing tailored optimization strategies.
Generative Engine Optimization at Scale: Evaluating Brand Visibility via AI Search Engines
Contextual Framework: GEO and the Evolving Role of AI Search
Generative Engine Optimization (GEO) emerges as a robust analytical paradigm addressing the shift from classical search engines to generative AI-driven platforms. The paper establishes GEO as an umbrella term encompassing practices like Answer Engine Optimization (AEO) and AI Search Visibility, all aimed at quantifying and enhancing the representation and citation of brands in AI-generated answers. Unlike traditional SEO, where ranking for specific keywords drives discoverability, GEO interrogates whether AI models mention, cite, and recommend brands in response to unbranded, category-level prompts. This reflects a fundamental transformation in information retrieval patterns, driven by LLMs capable of synthesizing diverse web content.
The scope is focused on quantifying the determinants of brand visibility, especially for entities without pre-existing online authority (e.g., SMEs, D2C brands). The approach operationalizes competitive brand recognition through controlled queries across ChatGPT, Claude, Gemini, Perplexity, and Grok, representing both search-native and parameter-based LLMs.
Methodological Architecture
Query and Prompt Engineering
The Ranqo platform orchestrates a systematic measurement pipeline using six prompt categories: discovery, problem/solution, use case, comparison, expert-level, and brand-research queries. Emphasis is placed on unbranded queries to isolate genuine competitive visibility. Prompts are auto-generated via Claude Sonnet, seeded with dynamic market-research context, ensuring topical relevance.
Signal Extraction and Scoring
Each (prompt, platform, run) tuple extracts multi-dimensional signals: boolean brand mention, ordinal position, sentiment (positive/neutral/negative, with continuous score), and full citation provenance (URL, domain type, relationship to brand). Aggregate metrics include visibility rate, share of voice, average ordinal position, and sentiment-weighted mention scores. Statistical conventions are nonparametric (bootstrap, Mann-Whitney U), reflecting the right-skewed, bounded distribution of visibility.
Page-Level Audit
Ranqo's page audit protocol operationalizes six dimensions—crawlability, content quality, page speed, AI readiness, citation potential, and authority/trust content. The latter three are weighted most heavily, paralleling findings from GEO benchmarks showing that machine-extractable provenance signals (quotations, statistics, citations) outweigh shallow schema markup or prompt engineering in driving citation rates [aggarwal2024].
Closed-Loop Recommendation
A recommendation engine leverages production telemetry to generate structured improvement actions (with impact and effort estimates), tied to quantifiable baseline and lift scores. This closed-loop design underpins forthcoming RCT-grade causal evaluation protocols.
Empirical Findings
Three-Tier Visibility Ladder: Quantification of Brand Stature Effects
The dominant empirical result is the stratification of AI search visibility into three tiers based on brand stature, operationalized via deterministic rubric (Wikipedia presence, press coverage, funding/public status):
- Tier 1: Global household brands (e.g., Stripe, Nike) achieve 73% average visibility in unbranded category prompts on initial tracking.
- Tier 2: Established mid-market/regional brands (e.g., Olipop, Klaviyo) manifest 44% visibility—marking an absolute step-down of ~30pp vs. Tier 1.
- Tier 3: Niche/small brands are visible in just 11% (another ~32pp decline).
These differences are statistically robust (Kruskal–Wallis, Mann-Whitney, Cohen's d>1.3). No prior GEO benchmark has provided comparable quantification. Visibility is essentially deterministic for most (brand, prompt, platform) cells, with 77.5% classified as always or never mentioned.
Engine-Specific Divergence and Temporal Persistence
The empirical baseline reveals flat-to-mildly declining visibility trajectories on most engines (notably, ChatGPT and Perplexity decline slowly over time), suggesting that without intervention, visibility does not improve organically. Visibility is highly engine-specific—cross-platform Jaccard overlap of citations is low (~0.12), demonstrating minimal agreement in cited sources and ranking across models.
Source Composition and Content Format Analysis
Citation provenance analysis shows:
- Corporate/third-party brand pages dominate (75.2%), with brand-owned domains constituting only 2.9%, indicating that peer-brand content (competitors, category actors) is the principal citation surface.
- YouTube is the most-cited non-corporate source (4.2%), surpassing editorial media, Reddit/community forums, and Wikipedia.
- Content Format: The ranked “best-of” listicle is the single most-cited format (21% of all citations, 36% of content citations), confirming its leverage in surfacing brands across multiple queries.
Sentiment Noise Characterization
Brand sentiment extraction is unstable: sentiment flips 6.7× more often than mention. No cells persistently exhibit negative sentiment; negativity is transient and episodic. This differential noise profile counsels against conflating mention and sentiment in impact measurement.
Visibility by Prompt Category
Discovery prompts (broad, top-of-funnel) yield the highest per-brand visibility (~23%), while specific problem/use-case prompts are consistently lower (~11%). Agency-tier LLMs (Claude, Grok) show elevated recognition rates, though the sample skews to higher-stature brands.
Protocol Design and Research Agenda
Seven protocols are designed for future population-scale causal measurement:
- Cross-platform citation overlap, position decay, RCT-grade closed-loop lift (Protocol P3), schema regression, entity-first sequencing, web-search on/off separation, white-hat C-SEO replication.
These are framed as falsifiable hypotheses rather than confirmed contributions; causal claims are reserved for future multi-brand RCTs.
Practical and Theoretical Implications
- Tier-Dependent Investment: GEO is only actionable for brands above a minimum online prominence threshold; early-stage brands must invest primarily in mass presence (Wikipedia, mainstream press, YouTube) before fine optimization.
- Engine-Specific Strategies: Each generative engine must be treated as an independent “market,” with specialized citations and ranking idiosyncrasies. Cross-engine results do not generalize.
- Quality-Driven Optimization: Machine-extractable content-quality signals are the principal drivers of visibility. Schema markup is operational hygiene, not a citation lever.
- Rapid Iteration: Visibility changes are measurable in weeks, not quarters, enabling fast closed-loop diagnosis and content repair.
Theoretically, the findings reinforce “Matthew effect” dynamics established by [algaba2025]: LLMs perpetuate concentration, favoring already cited and authoritative domains. This has implications for algorithmic bias, brand visibility, and the competitive dynamics of niche, emergent brands.
Limitations
- The current dataset is observational; no causal evidence is provided for the efficacy of Ranqo recommendations.
- Cohort brands are convenience-sampled (SaaS, retail, fintech, Indian DTC) and tier assignment is manually coded.
- Platform opacity and model configuration fixity limit inference into ingestion and retrieval mechanics.
- Sentiment extraction combines genuine variance and classifier noise.
Conclusion
GEO quantifies and operationalizes the determinants of brand visibility across major AI search engines, providing a first large-scale empirical baseline. Brand stature is the dominant determinant of AI visibility; engines diverge in their behaviors, and cited sources are concentrated and format-specific. The findings inform practical intervention strategies and establish the need for engine-specific measurement and rapid closed-loop iteration. Future work centers on multi-vendor replication, RCT-grade intervention measurement, and training-vs-retrieval separation. The academic trajectory calls for publication of falsifiable protocols and rigorous quantification—structuring the field for empirical validation and iterative improvement.
For reference: "Generative Engine Optimization at Scale: Measuring Brand Visibility Across AI Search Engines" (2606.20065).