Query Fan-Out: What 60,000+ Searches from ChatGPT & Google Show | AirOps and Chris Long

Описание: Chris Long, Co-Founder of Nectiv, joined us to break down how LLMs like ChatGPT and Google Gemini actually retrieve, evaluate, and cite content, revealing why AI search triggers layered query fan-out and what content and SEO teams must do to stay visible.

AI Search Relies on Query Fan-Out, Not Single Queries. AI systems like ChatGPT and Google Gemini break down prompts into multiple, layered searches, making search much more complex.

AI Fan-Out Queries Are Longer and More Varied Than Human Searches. LLMs often generate long-tail, multi-word queries—many with zero traditional search volume—requiring new strategies for SEO tracking.

Structured Content Formats Are Critical for AI Extraction and Citation. Tables, lists, and Q&A blocks help AI systems extract and cite content accurately, increasing the chance of brand mentions.

Reviews, Freshness, and Comparisons Dominate AI Search Patterns. Content optimized for reviews, recent years, and comparisons is most likely to be retrieved and cited by AI.

Optimizing for Google and ChatGPT Fan-Out Is Largely Overlapping. Both platforms use similar extraction patterns, so optimizing for one typically benefits the other, streamlining AI-first SEO efforts.

Get all of the takeaways & more from Chris Long: https://www.airops.com/blog/webinar-r...

02:45 Definition and explanation of what query Fan-ut is
07:12 ChatGPT Fan-Out analysis and search behavior patterns
14:31 Industry-specific fanout patterns and frequency analysis
22:35 Introduction to Google/Gemini fanout analysis methodology
24:58 Google Fan-Out comparison showing 9x more queries than ChatGPT
30:14 Common patterns in Google Fan-Outs, including reviews and freshness
38:27 Key takeaways and content optimization strategies
39:09 Tools and automation for Fan-Out content mapping
45:02 Q&A session on implementing Fan-Out strategies