CMSEO2025 Steve Toth.pdf
本地来源:seo-llm/raw/seo知识库/TXT整理/PPT/John喜欢和引用的那些海外演讲嘉宾PPTs/CMSEO2025-Steve-Toth.pdf.txt
v SEO’s Next Chapter: Why Truth Beats Traffic in the Age of AI Search Steve Toth “LLM optimization is not an opportunity. It's THE opportunity. AI, and LLMs, rip the fabric of marketing open and sew it back together in new ways. Learning how to influence LLMs as they influence buyers is mission critical.” A Quote from my colleague Kevin Indig … v About Me ● Creator of AI Notebook & SEO Notebook — 22,000+ subscribers combined. ● Left FreshBooks in March 2020, turning my former employer into a client and grew my solo consultancy past $1 million in its first year. ● CEO of Notebook Agency - 30 person team. ● Built award-winning strategies that rank #1 for multiple, high-intent keywords with 300K+ monthly searches. ● Dedicated thousands of hours to mastering AI search strategy. 1/3 v About Me ● Drove 50 million+ clicks for FreshBooks.com, including a page worth $50K in weekly traffic. ● Founded SEO IRL — Canada’s largest SEO conference. ● Clients: Purple Mattress, Spellbook, FreshBooks, Royal Bank of Canada, Mejuri, Cirque du Soleil, and Greg & David Baszucki of Roblox. ● Taught Answer Engine Optimization course for CXL ⭐4.7/5 ● My goal: help you stay two years ahead of your competitors. 2/3 Optimizing For AI Refinements v Deep Research ● Deep Research acts like an agent, taking time to perform research. ● It assembles a comprehensive report that can take upwards of 10 minutes depending on the model used and complexity. Scanning Our Client’s Website… v Hey Look, We Did Alright! In this presentation, I’ll share practical tips to optimize for Deep Research. How did we do it? v Advanced GEO: Refinement Synthesis ● ChatGPT Deep Research asks up follow-up questions after the initial query. ● Optimizing for these refinements ensures your content answers these likely subsequent questions, maintaining visibility in the conversation. 1/2 v Advanced GEO: Refinement Synthesis ● AI search is conversational. ● Users ask follow-up questions. ● Optimize for refinement synthesis. ● Ensures you make it to the finish line for refinements relevant to your ICPs. 2/2 v Cool! Where Can We Find These Refinements? ● ChatGPT Deep Research spells it out for us! v Refinement Questions v But Wait! There’s a Bot for it: Refinement Synthesizer ● Analyze model-specific refinements ● Highlight common threads ● Eliminate redundant prompts Here are some of the Questions v it generated for “Employer of Record Software” And here are the Top Refinements v in order of Importance Main Refinement Themes for Employer of Record Software: From refinements: Aggregated themes across models: What content can we improve on the website to better meet refinement criteria? E.g. Entered the site Deel.com v Before we wrap up Deep Research ● Refinements will vary each time you run deep research or any AI-based search on Google, Perplexity, Gemini etc. ● It’s much better to identify the common refinement themes and optimize pages towards that. ● Target content on the passage level vs page level. v Common Refinement Themes ● Comparisons: These help the LLMs reason more efficiently ● Who it’s for? Aka, the ICP: These help the LLM personalize the result ● Reviews: Adds human authenticity ● Price/budget: Goes without saying: important dealbreaker! ● Integrations: Dealbreakers for your ICP v Wrapping up Deep Research ● Let ChatGPT guide your content. ● Use the Deep Research Refinement Synthesizer to aggregate refinements. ● Optimize for refinement themes. Making it to the AI Journey Finish Line So How Do We Increase Our Chances of Attracting Our ICPs? We optimize for Dealbreakers! v Optimizing for Dealbreakers: ● What causes customers not to choose your product or service? Those are your "dealbreakers." ● For example, let’s say a user wants a CRM that absolutely must integrate with QuickBooks… 1/2 v Optimizing for Dealbreakers: ● Dealbreakers are a common part of the LLM buyer journey. ● LLMs ask users what their dealbreakers are to help find solutions that best meet their needs. 2/2 Introducing Dealbreaker Detector v What’s a Dealbreaker? Deep Research surfaces dealbreaker questions automatically… v What it Does: ● Dealbreaker Detector is designed to help you identify all the potential dealbreakers—those critical friction points—that might cause a customer to walk away from a product or service. ● It simulates the mindset of a skeptical buyer and analyzes your offering to flag issues across categories like: ○ Features (missing must-haves) ○ Integrations (compatibility gaps) 1/3 v What it Does: ○ Fit: Limitations for users with specific needs ○ Pricing: Confusing or restrictive pricing models ○ Usability: Steep learning curves or poor UX ○ Compliance: Legal, privacy, or security concerns ○ Customer Support: Lack of help when needed 2/3 v What it Does: ● DD provides clear, categorized lists of specific customer dealbreakers by thinking like a critical buyer, helping you proactively fix objections before they cost you sales. ● Identifies potential dealbreakers. ● It also generates FAQ-style rebuttals to help you preemptively address these objections in your marketing, sales, or onboarding material. 3/3 v Dealbreaker Questions v Optimizing for Dealbreakers is THE way to close deals inside the black box of LLMs! Creating LLM-Focused Comparison Pages to Dominate AI Search v Remember this? → v The Power of LLM-Focused Comparison Pages ● Comparison pages, such as [Your Brand] vs. [Competitor], are excellent assets for GEO. But to take it step further and truly leverage the personalization that AI offers its users, we can go even deeper with this strategy. ● Two-way and three-way comparisons for ICP (Clickup vs. Asana for Marketing Agencies) ● Alternatives pages: (Asana Alternatives for Marketing Agencies) 1/2 Comparison Page Example in Action (found in ChatGPT) Souce: https://www.zenpilot.com/blog/clickup-vs-monday.com It’s working! v One Final Note on Comparison and Alternatives Pages ● Put dates in your title tags to trigger recency bias of AI search prompts (use dynamic dates if your CMS supports it). ● Recency bias is confirmed in many of these leaked AI system prompts, e.g. Perplexity & ChatGPT Creating an Explicit AI Info Page notebook.agency footer v What’s an AI Info Page? ● An AI info page is a plain text page on your website you can point AI’s to that contains important company information. ● When visiting your homepage, the bot will follow the link and process the information on the page and likely use it in their response about your brand. What’s an AI Info Page? Source Info Pages in action… Info Pages in action… Info Pages in action… Link to chat v Create your own AI Info Page! Link to the AI Info Page Generator GPT Optimizing for Truth With Notebook Agency’s Truth Alignment Framework™ The web is full of fake news And so are LLMs What happens when the answers to your dealbreakers aren’t easily retrievable, under your control, dispersed, in accurate, incomplete, or even misleading? Fake news about your brand likely exists While “real news” aka the Truth isn’t easily retrievable and consolidated. Let’s try a search… Source What caused Deel, Rippling and Papaya Global to get recommended? And what about the qualified companies that weren’t recommended? Like Oyster HR… Source So then why didn’t it appear in the non-branded query’s answer? 8 Different sources from Oyster HR were needed to get the answer and… they were not easily retrievable… In fact, Google highlights the sources that it uses to generate its AI answer! Source No wonder it didn’t make it in the non-branded search! Now, Deel on the other hand… Source Highlights = relevant passages that support the query. No wonder Deel made it into the non-branded search! Steps to interrogate: ● Ask the non-branded question… ● And when you’re not found, reformulate and ask about your brand and dig into the sources! So if we’re not making key information retrievable, how can we use Truth AlignmentTM to boost visibility and accuracy? v Actions You Can Take Today ● Map your key dealbreakers on pages that the LLMs tend to cite. ● Optimize your help center documentation for LLM retrieval. ● But to go deeper, we can apply the Truth Alignment Framework™ v Why Truth Alignment™ in AI Conversations Matters ● In AI Search decision-making, accuracy = opportunity. ● AI is the new first impression. ● These models aren't just surfacing brands. They’re making recommendations. ● The risk? Getting silently disqualified in black box AI conversations. 1/2 v Why Truth Alignment™ in AI Conversations Matters ● When your product truths like compliance, integrations, or core capabilities aren’t consolidated on key pages and clearly reflected in answers, LLMs may: ○ ○ ○ Recommend less capable competitors with better-structured content Misrepresent or oversimplify what you do by generating an unfactual response based on inaccurate third-party content. Outright hallucinate when information is insufficient. 2/2 The Truth Alignment Framework™ is Notebook Agency’s effort to fix that… and measure truth. v From the Starting Line to the Podium ● Visibility Isn’t the Win. Recommendation Is. ● AI platforms are now the first and last touchpoints in many buying journeys. ● Being mentioned by LLMs isn’t enough, subsequent questions cause LLMs to influence decisions. ● Brands must be cited accurately, persuasively, and for the right reasons. v What the Truth Alignment Framework™ Does ● Makes LLMs as knowledge as your top salesperson. ● Turns LLMs into trained advocates, not just answer engines. ● Ensures your brand is recommended (not just mentioned) in late-stage queries. ● Bridges the gap between brand truths and generative model outputs. v Step 1: Codify Truth with the Truth Notebook ● Creating a single source of truth to measure against. ● Centralized, client-validated record of product capabilities, ICP fit, pricing, integrations, dealbreakers, and competitive positioning. ● Enables us to audit, score outputs against our ideal sales-grade answers, and create citation-ready content. v Codify Truth with the Truth Notebook™ ● We use ontology and taxonomy principles to create the Truth Notebook (a sophisticated RAG system designed by our CTO Max Geraci). ● V1 is generated based on help documentation and information like sales battlecards, internal documentation, sales call transcripts, and even transcribed text provided by the client. ● The Truth Notebook evolves as the product evolves. v Step 2: Interrogating LLMs to find Truth Gaps ● Use buyer-style prompts to test LLMs: “Does Recurly include advanced subscription management, churn analytics, and global payment support for scaling SaaS businesses?” ● Responses are captured, graded, and analyzed across ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews for how well they reflect what’s in the Truth Notebook. 1/2 v Step 2: Interrogating LLMs to find Truth Gaps ● What are the quality of the sources that influence AI’s answers? Are they easily retrievable or incomplete and scattered? ● Do they fully represent the truth, partially or not at all? ● Varied, natural language prompts test against different phrasing. 2/2 v Step 2: Interrogating LLMs to Understand What They Currently Recommend ● We can even interrogate to see which products the LLMs recommend one vs. the other. ● With questions like: ○ Who has better customer support, Zoura or Recurly? ○ How does Zoura handle multi-currency compared to Recurly? 1/2 v Step 2: Interrogating LLMs to Understand What They Currently Recommend ● Which billing platform is better for managing complex subscription upgrades and downgrades, Zuora or Recurly? ● This measures the effectiveness of the comparison page strategy. 2/2 v Step 3: Score Outputs with the Truth Score™ ● ● Each answer is scored on: ○ Accuracy (Is it fully correct, partially, or not at all? Are the sources consolidated and clear? If not, then you’re less likely to be included in nuanced non-branded prompts.) ○ Coverage (Are all key truths present?) ○ Sentiment (Are we being recommended?) Benchmarks which truths are missing, distorted, undervalued and not backed up with retrieval-friendly content. v Step 4: Detect and Fix Discrepancies ● ● Find Out Why AI Gets It Wrong ○ On-site issues (truths are not consolidated, content is vague, not optimized for retrieval) ○ Off-site distortions (third-party content preferred over first-party, unfavorable reviews, outdated articles, competitor-driven narratives, i.e. their comparison pages) Map LLM responses to the sources influencing them (possible because we use browser automation and not API). v Step 5: Optimize for Retrieval & Influence ● Make the Right Truths Easy to Surface. ● Rebuild on-site content to consolidate and support key truths ● Edit and saturate off-page content with correct truths (websites must be in the same sphere and adding recency language to these truths helps them get chosen for retrieval). 1/2 v Step 5: Optimize for Retrieval & Influence ● Launch LLM-ready comparison pages structured for citations and retrievable product truths. ● Ensure truths appear in the passages LLMs are most likely to quote. 2/2 v Step 6: Monitor, Adapt, Repeat to improve Truth Score ● LLMs Evolve. So Should Your Representation. ● Continuous re-interrogation with varied buyer prompts. ● Real-time Truth Score dashboard. ● Feedback loop informs content, PR, Social Media, & SEO. ● Reach out to third-party websites to correct mistruths. v So yeah, tracking mere mentions… v Without an end? v To understand your AI Share of Voice? Nope! Fact: AI answers change. Tracking mention frequency is not enough and not a true reflection of reality. Instead, we need to Propagate and Track Truth & on sources that LLMs cite! Truth. v The Outcome: Sales-Grade AI Representation ● Your brand is not just mentioned, but chosen! ● Greater influence in LLM buyer journeys. ● More accurate citations and recommendation frequency. ● Strategic defensibility against louder, less capable competitors. v Results Source v One more? Source v Ok, fine. Here’s another! Source Thank You! Let’s Connect How can we help? ainotebook.com (AEO Strategy Newsletter) seonotebook.com (SEO Strategy Newsletter) notebook.agency (my AEO/SEO Agency) seoirl.com (my conference) Linkedin: linkedin.com/in/stevetothjr
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