How to Rank on ChatGPT (AI SEO With Steve Toth)

Key Concepts:

• GEO (Generative Engine Optimization): Optimizing for ranking in Large Language Models (LLMs) like ChatGPT, Gemini, and Perplexity, as opposed to traditional SEO for Google.
• LLMs (Large Language Models): AI models like ChatGPT, Gemini, and Perplexity that generate human-like text.
• Fan Out Queries: The process where LLMs generate multiple related queries to gather information for a single user query. Different platforms (Google, OpenAI, Perplexity) implement this differently.
• Agentic Search: A search method where the AI uses reasoning between queries to refine its search strategy (used by OpenAI).
• Information Retrieval: The process of efficiently finding relevant information within a large dataset.
• Deal Breakers: Key criteria that customers use to make purchasing decisions.
• Truth Alignment Framework: A method for scoring how well LLMs accurately represent a brand's information.
• Ontology and Taxonomy: A structured representation of a company's products, services, and key attributes.
• RAG (Retrieval-Augmented Generation): A technique for improving the accuracy and reliability of LLMs by grounding them in external knowledge sources.
• Prompt Injection: A technique for manipulating the behavior of an LLM by crafting specific prompts.
• Token: A unit of text used by LLMs, typically a word or part of a word.

1. Main Topics and Key Points:

• The Difference Between SEO and GEO: Ranking in LLMs requires a different approach than traditional SEO. Establishing a strong brand presence through off-page mentions, G2 profiles, and listicles is crucial for LLMs.
• Fan Out Queries and Recency Bias: LLMs use fan out queries to gather information. Perplexity and Gemini are transparent about the queries they use. LLMs have a recency bias, preferring more recent results.
• Comparison Pages: Comparison pages are a key strategy for lesser-known players to get into LLM results. Well-structured comparison pages with clear information retrieval elements are essential.
• Addressing Deal Breakers: Understanding the buyer journey and addressing deal breaker questions in content is crucial. LLMs actively prompt for these deal breakers.
• Testing and Experimentation: Successful SEOs and GEOs need to be willing to test and experiment.
• Niching Down: Niching down for specific products and ICPs is a great way to become relevant to AI search platforms.
• Trust and Conversion Rates: LLMs are seen as personal recommendations, leading to higher conversion rates and session times.
• The Importance of Attribution: Accurately tracking how customers are finding a business is crucial for understanding the ROI of GEO.
• The Winner Takes All Challenge: There's a risk that LLMs will over-represent the most popular solutions, making it harder for smaller players to gain exposure.
• Brand Awareness: Raising brand awareness through other channels before focusing on GEO is important.
• The GEO Process: The GEO process involves understanding the buyer journey, identifying key deal breakers, and creating content that addresses those deal breakers in an information retrieval-friendly way.
• Information Retrieval Friendly Content: Content should be structured in 100 to 300 token chunks, disambiguated, and supported with links.
• Off-Page GEO: Influencing what people are saying on Reddit and other platforms is an important element of GEO.
• Truth Alignment Framework: The truth alignment framework involves tracking what a company's sales-grade answer would be to a deal breaker versus what the LLM's answer is.
• AI-Generated Content: AI can be used to create comparison pages and other content, but it's important to have a source of truth that is kept up to date.
• The Future of GEO: The future of GEO will likely involve increased use of agents and a more fractured landscape of LLMs.

2. Important Examples, Case Studies, or Real-World Applications Discussed:

• Authority Hacker: The example of Authority Hacker's outdated information in ChatGPT highlights the need to monitor and influence what LLMs are saying about a brand.
• Hrefs: Hrefs reported that 12% of their signups are coming from 0.5% of their traffic, indicating the potential of LLMs for high-quality traffic.
• Indoor Map Software Client: The example of an indoor map software client ranking number one in a deep research journey demonstrates the effectiveness of targeting specific use cases and integrations.
• Employer Record Software: The example of employer record software is used to illustrate how to identify key buying decision criteria and create content that addresses those criteria.
• Resume Generator: A resume generator company found that their highest converting channels were ChatGPT and YouTube.
• Notebook Agency: Notebook Agency's LLM info page is used as an example of how to directly influence what LLMs know about a company.
• Rippling: Rippling is used as an example of an Employer of Record (EOR) software company.

3. Step-by-Step Processes, Methodologies, or Frameworks Explained:

• GEO Process:
  1. Understand the buyer journey.
  2. Identify key deal breakers.
  3. Create content that addresses those deal breakers in an information retrieval-friendly way.
• Creating Information Retrieval Friendly Content:
  1. Structure content in 100 to 300 token chunks.
  2. Disambiguate content.
  3. Support content with links.
  4. Use declarative sentences.
• Truth Alignment Framework:
  1. Track what a company's sales-grade answer would be to a deal breaker.
  2. Compare that to what the LLM's answer is.
  3. Identify the content online that is influencing the LLM's answer.
  4. Create a campaign to propagate more positive information and correct misinformation.
• Using Deep Research to Guide Content Creation:
  1. Use deep research to identify the key themes and questions that LLMs are asking.
  2. Create content that addresses those themes and questions.
• Creating an LLM Info Page:
  1. Create a page on your website with key information about your company.
  2. Link to that page from your homepage.
  3. Use a tool to generate a V1 of the page.
  4. Update the page with your own information.

4. Key Arguments or Perspectives Presented, with Their Supporting Evidence:

• GEO is different from SEO: GEO requires a different approach than traditional SEO, focusing on brand presence, deal breakers, and information retrieval.
• LLMs are becoming more important for purchasing decisions: LLMs are seen as personal recommendations, leading to higher conversion rates and session times.
• Attribution is crucial for understanding the ROI of GEO: Accurately tracking how customers are finding a business is essential for justifying GEO investments.
• The winner takes all challenge is a concern: There's a risk that LLMs will over-represent the most popular solutions, making it harder for smaller players to gain exposure.
• Off-page GEO is important: Influencing what people are saying on Reddit and other platforms is an important element of GEO.
• Agencies need to define their GEO service offerings: Agencies need to experiment and test different approaches to GEO in order to develop compelling service offerings.

5. Notable Quotes or Significant Statements with Proper Attribution:

• "AI traffic has increased 9.7x in the past year... Search traffic is 210 times higher than that." - Tim Solo (via Gail)
• "...across his clients he's seeing the conversion rate of this traffic is very high because people are going to chat GBT and other LLMs to when they're just ready to buy..." - Steve (referring to his client data)
• "It's not just about sort of ranking for keywords but about what stat or any knows or thinks about your brand." - Unattributed
• "We're seeing way higher conversion rates, way higher session times... Google traffic was already the highest quality traffic the web had to offer because of intent." - Steve
• "Whenever we ship a new feature, getting it reflected accurately in the LLMs is almost like task number one." - Steve
• "Everybody knows that they want GEO, but nobody knows what they want." - Steve

6. Technical Terms, Concepts, or Specialized Vocabulary with Brief Explanations:

• GEO (Generative Engine Optimization): Optimizing for ranking in Large Language Models (LLMs).
• LLMs (Large Language Models): AI models like ChatGPT, Gemini, and Perplexity that generate human-like text.
• Fan Out Queries: The process where LLMs generate multiple related queries to gather information for a single user query.
• Agentic Search: A search method where the AI uses reasoning between queries to refine its search strategy.
• Information Retrieval: The process of efficiently finding relevant information within a large dataset.
• Deal Breakers: Key criteria that customers use to make purchasing decisions.
• Truth Alignment Framework: A method for scoring how well LLMs accurately represent a brand's information.
• Ontology and Taxonomy: A structured representation of a company's products, services, and key attributes.
• RAG (Retrieval-Augmented Generation): A technique for improving the accuracy and reliability of LLMs by grounding them in external knowledge sources.
• Prompt Injection: A technique for manipulating the behavior of an LLM by crafting specific prompts.
• Token: A unit of text used by LLMs, typically a word or part of a word.
• SERP: Search Engine Results Page.
• ICP: Ideal Customer Profile.
• G2: A peer-to-peer review site for software and services.
• ATS: Applicant Tracking System.
• API: Application Programming Interface.
• JSON: JavaScript Object Notation, a lightweight data-interchange format.

7. Logical Connections Between Different Sections and Ideas:

The discussion flows logically from the initial debate about the hype surrounding LLMs to a detailed exploration of GEO strategies. The importance of brand presence and accurate information is emphasized throughout. The conversation connects the technical aspects of LLMs (fan out queries, tokenization) to practical applications (creating comparison pages, addressing deal breakers). The discussion also addresses the challenges and opportunities for agencies in the emerging GEO landscape.

8. Any Data, Research Findings, or Statistics Mentioned:

• AI traffic has increased 9.7x in the past year but only represents 0.25% of site traffic on average.
• Search traffic is 210 times higher than AI traffic.
• Hrefs reported that 12% of their signups are coming from 0.5% of their traffic.
• One client saw 186 conversions from ChatGPT in the last 30 days.

9. Clear Section Headings for Different Topics:

(Headings are incorporated into the "Main Topics and Key Points" section above.)

10. A Brief Synthesis/Conclusion of the Main Takeaways:

GEO is an evolving field that requires a different approach than traditional SEO. While the hype around LLMs may be overblown, they are becoming increasingly important for purchasing decisions. Establishing a strong brand presence, addressing deal breakers, and creating information retrieval-friendly content are crucial for success in GEO. Agencies need to experiment and define their GEO service offerings in order to meet the growing demand for this type of expertise. The future of GEO will likely involve increased use of agents and a more fractured landscape of LLMs.