AI Discoverability Brand Visibility Lessons from Real-World Studies

Noriko Yokoi discusses how brands can optimize their visibility and discoverability across major AI search engines like ChatGPT, Gemini, and Claude.

In this Marketecture AI Insiders session hosted by David Berkowitz of the AI Marketers Guild, Yokoi shares data-backed insights from real-world studies conducted by her startup, findable.app. The discussion focuses on generative engine optimization (GEO), citation behavior, and how search dynamics differ across large language models (LLMs).

The session also covers the impact of specific landing pages versus homepages on citations, strategies for small and medium-sized businesses to target niche local search prompts, and why crawlable video content like YouTube is increasingly critical for AI discoverability compared to closed social platforms.

Key moments

[05:49] Do Different AI Search Engines Provide Consistent Brand Recommendations?

Noriko Yokoi explains that AI search engines are highly non-deterministic, meaning they rarely provide the same brand recommendations across multiple queries or different engines.

In a study analyzing ChatGPT, Perplexity, Gemini, and Claude, Yokoi found that only 27% of buyer-intent questions returned the same top brand across all four engines. For three out of four questions, at least one AI engine selected a completely different brand. Even when asking the exact same question repeatedly to the same model, the consistency of brand mentions peaked between 78% and 87% before dropping significantly, proving that generative AI search behaves very differently from traditional deterministic search engines.

This volatility means marketers cannot rely on tracking a single platform. Because each LLM possesses unique model weights and real-time search protocols, a brand's visibility profile will look radically different on ChatGPT compared to Gemini or Claude.

[09:13] Where is the Biggest Opportunity for Brands to Influence AI Search Answers?

Noriko Yokoi advises that the best opportunity for brand visibility lies in search categories where AI engines exhibit "moderate consensus" or fragmentation rather than an established winner.

When an AI engine has already settled on a dominant category leader (such as Nike for athletic sneakers), it is extremely difficult to displace that recommendation. However, in categories with highly fragmented results—such as banking, where engines recommend multiple competitors like Chase, Citibank, and others—the landscape remains open. Marketers can capitalize on these gaps by identifying where the AI lacks a single definitive answer and positioning their brand to fill that specific informational void.

To find these opportunities, brands must audit categories to see where AI recommendations diverge. Where there is high fragmentation or disagreement among the top engines, targeted content optimization can help a brand secure a foothold as a newly cited authority.

[12:30] How Do Earned Media and Owned Content Compare in AI Citation Volume?

Noriko Yokoi reveals that owned content is the least cited source in AI search results, with 95% of citations pointing to unrelated third-party sources, 5% to earned media coverage, and only 1% to owned brand websites.

While brands naturally prioritize optimizing their own sites, AI engines heavily favor third-party editorial authority and independent sources over brand-owned collateral. This is because search algorithms prioritize neutral, authoritative information to answer user queries objectively.

However, this does not mean brands should abandon owned content. Yokoi emphasizes that owned content must be highly authoritative, while marketing strategies must simultaneously focus on securing external PR, media mentions, and third-party reviews to feed the authoritative databases that LLMs rely on.

[13:28] Does a Direct Link to a Page Carry More Weight in AI Search Than a Brand Mention?

Noriko Yokoi states that a specific, linked page-level citation carries 2.5 times more weight and visibility in AI search results than a simple unlinked brand mention.

AI search engines prefer to direct users to specific landing pages that answer their questions directly rather than mentioning a brand name in passing. Creating tightly targeted, comprehensive landing pages optimized to answer highly specific buyer queries dramatically increases the likelihood of being cited.

In terms of format, Yokoi's research shows that authoritative homepages are still heavily cited, but specific resource, comparison, and product pages see significant boosts when they address explicit search intents. Designing clear, structured layouts that search engine scrapers can easily parse is critical for earning these high-value links.

[14:34] Why Should Marketers Optimize Across Multiple AI Engines Instead of a Single Platform?

Noriko Yokoi explains that optimizing content for visibility across all four major AI engines—ChatGPT, Gemini, Claude, and Perplexity—yields an 11 times higher return in brand visibility compared to optimizing for just a single platform.

Because each LLM uses different training data, search algorithms, and citation mechanisms, a brand's visibility on one platform does not guarantee presence on another. Relying on a single-engine optimization strategy leaves major gaps in reach.

Analyzing and tracking brand footprint across all major search agents ensures that a brand captures the diverse pathways through which modern users search. Multi-engine optimization serves as a hedge against algorithmic updates and shifts in consumer platform preferences.

[21:58] How Can SMBs and Local Brands Compete Against National Competitors in AI Search?

Noriko Yokoi demonstrates that local and small-to-medium businesses (SMBs) can outcompete national brands by targeting highly localized, niche search prompts rather than broad category terms.

A national brand claiming dominant market share across the country may completely lose visibility when a search query is localized down to a specific state, city, or neighborhood. For example, while a national chain might dominate general search terms, a local provider can win localized queries (e.g., "best Indian restaurant in Curry Hill, NYC") by tailoring their digital footprint to local contexts.

AI engines generate highly customized, geography-specific answers. This gives localized businesses a strong structural advantage if they optimize their sites and third-party profiles with precise local signals.

[25:42] How Does Prompt "Fan Out" and Query Variation Affect Brand Discoverability?

Noriko Yokoi explains that slight variations in how a search prompt is phrased can produce vastly different brand recommendations and citation scores.

Because generative AI relies on semantic understanding rather than exact keyword matching, brands cannot rely on a single optimized phrase. A strategy called "fan out"—which involves deconstructing a main search query into dozens of conversational and grammatical variations—is crucial for mapping a buyer's diverse ways of asking questions.

Tracking how a brand ranks across these granular prompt variations helps identify specific phrasing gaps where competitors might be stealing visibility. It allows marketers to build a comprehensive map of how various user personas search at different stages of the buying funnel.

[35:26] Why is YouTube Content More Valuable for AI Search Visibility Than TikTok or Instagram?

Noriko Yokoi points out that YouTube content is highly visible to AI search engines because it is openly crawlable and automatically transcribed, whereas platforms like Instagram and TikTok remain closed gardens.

Search engine crawlers can easily access and parse text-based transcripts from YouTube videos to synthesize answers and generate citations. In contrast, social media platforms like TikTok and Instagram restrict search engine crawlers from indexing their video content.

For brands producing video media, syndicating content to YouTube is essential to ensure that AI models can retrieve, summarize, and cite their insights in response to user queries. The presence of transcribed text makes audio-visual content a powerful asset for modern AI discoverability.