What 50K Query Fan-Outs Reveal About Brand Visibility in AI Era
New methods emerge to measure brand perception in the age of AI-powered search.
1Brand Analysis with Massive Dataset
Researchers created a massive dataset of 1,000 subtopics and 50,000 prompts to understand how AI models shape brand perception. The study identified 10 distinct fan-out types based on Google's Web Guide Beta results. This dataset reveals how brands align with search intent and position themselves in search results, offering unprecedented insights for digital marketers in the generative AI era.
2Brand Bias in LLM Responses
The research found nearly 100% brand mention rate in AI responses when prompts contained brand names. The 50,000-prompt dataset used one primary brand and two competitors per subtopic as contextual hints. Results demonstrate how subtle prompt engineering can create significant differences in LLM outputs, highlighting the importance of carefully structured prompts in brand visibility analysis.
3Fan-Out Types with Most Brand Mentions
Analysis showed 12.8% of generated prompts mentioned either the primary brand or competitors. Remarkably, 97% of brand references concentrated in just two fan-out types. While brand mentions in [Entity] fan-outs were expected, the strong brand component in [Comparison] fan-outs was surprising. Other fan-out types showed minimal brand references, revealing specific patterns in AI's brand association tendencies.
4Measuring Prompt Alignment
Researchers developed an 'Alignment' metric using cosine similarity to measure how well fan-out prompts matched their subtopics. Alignment scores across the dataset ranged from 0.22 to 1.00, with a mean score of 0.67. Scores above 0.90 indicated nearly perfect topical alignment, while the research team considered 0.67 as achieving the right balance between relevance and diversity in prompt generation.
5Optimal Prompt Length Insights
The dataset revealed an average prompt length of 8.2 words, with Gemini naturally converging to this average under its 12-word limit. Researchers emphasize that ideal prompts should be specific enough yet broad enough to cover variations. While humans might write paragraphs in ChatGPT, marketers need trackable prompt lengths that balance specificity with coverage for effective AI-powered search strategies.
6Dataset Structure and Methodology
The study generated 5 instances of each of 10 fan-out types per subtopic (1000x5x10=50,000 prompts). Gemini suggested one primary brand and two competitors for each subtopic. Most data was generated using Gemini 3.1 Flash Lite API, while more complex grounding queries utilized the advanced Gemini 3.5 Flash model, demonstrating a tiered approach to AI-powered marketing research.
7Long-Tail Search Strategy Shifts
The research illustrates how the transition from organic to question-based searches is changing SEO professionals' long-tail strategies. AI-powered prompt research is replacing traditional keyword research methods. Findings reveal how fan-out prompts are transforming search engines' content evaluation logic, requiring marketers to adapt their visibility measurement approaches in the generative AI landscape.
8Implications for Marketing Strategies
These findings offer new ways to measure brand visibility in the AI era, establishing prompt engineering as a critical skill for digital marketers. The research highlights the growing importance of new metrics like AI Visibility where traditional SEO measurements fall short. For brands, AI-powered search strategies are becoming increasingly vital, requiring updated approaches to maintain and measure online presence effectively.
Original article: Moz Blog — 50 Bin Sorgu Analiziyle Marka Görünürlüğüne Dair Çarpıcı Veriler