SuperMarketers.ai: Your Roadmap to AI-Driven Marketing

Rand Fishkin ran 2,961 prompts across ChatGPT, Claude, and Google AI. Fewer than 1 in 100 produced the same brand list. In this conversation, he breaks down why the entire "AI ranking" category is built on probability, what actually moves your brand into LLM answers, and how he's marketing his new product AlertMouse with zero AI search visibility on day one.Rand Fishkin is the cofounder and CEO of three companies: SparkToro, software that makes audience research accessible to everyone, indie game maker Snackbar Studio, and the the superior alternative to Google Alerts: Alertmouse.What you'll learn:→ Why AI answers are probabilistic, not deterministic, and what to track instead of rankings→ The two real mechanisms that influence LLM visibility (RAG and training data)→ How user prompts actually behave when 150 people are asked the same question→ Why PR and positioning are now 100x more important than tactical SEO→ The white hat playbook for showing up in ChatGPT, Gemini, and Google AI Overviews→ How to use AlertMouse and SparkToro together to find prompts your buyers actually use→ What "audience intelligence" vs. "audience research" teaches you about brand positioning lag→ The Seattle Ultrasonics knife story: how one founder turned mentions into product roadmap→ Where Rand says marketers should spend attention in 2026 (and what to ignore)Chapters:0:02 Intro0:38 The Datos prompt study and why AI answers are random4:13 The two mechanisms that move LLM visibility8:47 How Rand is marketing AlertMouse from zero visibility13:42 Why positioning is the biggest AEO lever17:08 AlertMouse use cases and the Seattle Ultrasonics story22:11 Where to actually invest attention in 2026Connect with Rand:SparkToro: https://sparktoro.comAlertMouse: https://alertmouse.comLinkedIn: https://www.linkedin.com/in/randfishkinWhat are other ideas that we can add for the YouTube thumbnail? I don't really love "AI rankings don't exist." What are some of the main takeaways from the episode?

Most outbound teams burn through their TAM with the same generic sequences and wonder why CAC keeps climbing. Parthi Loganathan, CEO of LetterDrop, runs a one-person BDR team that books seven-plus meetings a week using a different approach: identify the small slice of buyers actively evaluating competitors, then build campaigns that lead with value instead of pitch.In this conversation, Parthi breaks down how LetterDrop reverse-engineers competitor pipelines from public online signals - who is commenting on whose posts, who is connecting with which AEs, who is engaging with which content. He explains the highest-converting outbound campaign he has ever run (giving competitors' qualified leads away for free to VPs of Sales), why agentic systems will absorb the brittle Zapier-style workflows BDRs run today, and what part of the BDR job is permanent: face-to-face channels, creative campaign design, and intelligence work that requires actually understanding the buyer.He also shares why he believes SaaS is entering a compression cycle - where the software that survives exposes itself as APIs and MCPs for agents instead of dashboards for humans - and why his team just shipped 70% of LetterDrop as MCP endpoints for Claude Code users.Key TakeawaysOnly 5% of your TAM is in-market at any time. Reaching out to the other 95% is what is killing BDR economics. Parthi's whole thesis is that outbound stops working when you spray and starts working when you read public signals to find the active 5% before competitors do.You can reverse-engineer your competitor's CRM from public data. LetterDrop builds a graph of who is talking to whom online - comments, connections, engagement patterns - and uses that to guess which prospects are actively talking to your competitors. Without scraping anything proprietary.The "free lead" campaign is LetterDrop's highest-converting outbound play. Identify someone evaluating a competitor, send the lead directly to that competitor's CMO or VP of Sales as a free gift. Perceived value is hundreds to thousands per qualified lead, so it gets opened, replied to, and converts at rates a pitch sequence cannot touch.BDRs keep the face channels and the creativity. Agents take everything else. Account intelligence, sequencing, decisioning, and brittle Zapier-style workflows go to agents. BDRs own calls, video DMs, in-person, and the campaign creativity that requires understanding why your buyers actually buy.SaaS is compressing toward APIs and MCPs. The software that survives the agent era will not be dashboards. It will be hard-to-replicate data exposed as MCP endpoints. LetterDrop already shipped 70% of its product as MCPs for Claude Code users - a preview of where B2B SaaS pricing power is heading.LetterDrop - Signal-based outbound and competitive intelligence platformSuperMarketers: Build your AI visibility system at https://supermarketers.aiFollow Gen on LinkedIn: https://www.linkedin.com/in/genfurukawa

Andréa Jones has published 400 podcast episodes while raising two kids under five. Her system is not hustle - it is infrastructure built from necessity.The podcast is her content hub. Every idea starts as a voice note in Google Notes, gets structured in a ChatGPT project trained on her proprietary Mindful Marketing framework, audience personas, and email voice. She records in Riverside, then runs the audio through Cast Magic - a repurposing tool that generates 20 pieces of content per episode: show notes, social quotes, one-liners, newsletter drafts, and thread-style posts trained on her voice.She layers these outputs into two Airtable calendars - an editorial calendar for signature content and a social media calendar she populates months in advance during high-energy windows. Content from episode 399 might not hit social until two months later.Andréa also runs Uncommon Marketing Agency, where she builds AI-powered interactive web experiences for brands - including an 8-bit style game for Niagara Falls tourism that replaces the generic results you get from ChatGPT or Claude with a curated, closed-loop brand experience.Her strongest take: marketers need to get "elegant" with prompting. The inputs need to be as detailed as the outputs you expect - audience personas, objection handling, awareness levels. Most people skip this and get generic results.One podcast episode produces 20+ content pieces through Cast Magic - Andréa uploads each episode's audio and gets titles, timestamps, social quotes, one-liners, newsletter drafts, and thread-style posts - all trained on her voice. She edits but never starts from blank.Content batching on energy cycles beats daily consistency - She populates her Airtable social calendar months in advance during high-energy windows, then coasts during low-energy periods. Her calendar currently runs through June with repurposed podcast content.ChatGPT projects trained on your framework cut outline time from 2 hours to 20 minutes - She uploaded her Mindful Marketing framework transcripts, audience personas, offer positioning, and email voice into a single ChatGPT project she uses for every episode.Your AI inputs need to be as long as your expected outputs - Her client's sales page read like generic ChatGPT because the prompt didn't include audience personas, objection handling, or awareness levels. Context engineering is the differentiator.Closed-loop AI experiences beat open web for brand marketing - Uncommon Marketing Agency builds interactive web games that surface only the brand's curated content, avoiding the noise and dated information that LLMs pull from the open internet.OnlineDrea - Andréa's personal brand: courses, podcast, and the Do Less Market Better Kit (free course)Uncommon Marketing Agency - Gamified and interactive AI-powered marketing experiencesMindful Marketing Podcast - 400 episodes on anti-burnout marketing strategiesCast Magic - AI podcast repurposing tool (generates 20+ content pieces per episode)Riverside - Podcast recording and transcript-based editingKey TakeawaysLearn More

Steve Toth has spent 15 years in SEO and now runs one of the sharpest AEO/GEO consultancies in B2B. His core argument: stop tracking where your brand ranks in LLM responses. Start measuring whether LLMs represent you accurately.His Trust Alignment Framework scores how well ChatGPT, Perplexity, and Gemini answer questions about your product across six pillars - vertical, company size, comparisons, pricing, integrations, and features. The gap between your "sales-grade answer" and the LLM's answer is your visibility problem.Steve walks through live demos showing how ChatGPT Deep Research and Perplexity surface follow-up refinements - and how collecting those refinements across 5-8 runs reveals which deal-breaker topics matter most in your category. He also shares a Claude project that clusters Google Search Console keywords by intent, giving B2B teams a proxy for LLM search demand when no reliable prompt volume data exists.The conversation covers how each model cites differently - ChatGPT prefers general pages, Google AI Mode pulls specific passages from case studies and UGC - and why passage-level optimization matters more than page-level. Steve closes with his Spellbook case study: 90% non-branded organic traffic growth by targeting emerging keywords in the legal AI space and capitalizing on competitor sentiment gaps.--- Key Takeaways1. LLM leads convert 4-5x higher than Google traffic - ChatGPT referral visitors spend 4-5x more time on site and convert at 4-5x the rate. These buyers arrive pre-educated with specific deal-breakers already defined. Your sales team closes them faster.2. Stop tracking brand mentions in LLMs - measure representation accuracy instead - The Trust Alignment Framework compares your ideal sales answer against what the LLM actually says across six pillars (vertical, company size, comparisons, pricing, integrations, features). The delta is your real visibility gap.3. LLM prompt volume tools are unreliable - use intent clustering as a proxy - Every word added to a prompt makes it less likely to be searched twice. Steve built a Claude project that clusters Google keyword data by intent and aggregates volume across the entire cluster, giving directional demand signals for AEO prioritization.4. Each AI model cites sources differently - ChatGPT favors first-party "ultimate guide" pages. Google AI Mode pulls specific passages from case studies and UGC. Claude uses the Brave search index. Optimizing for one model does not guarantee visibility in others.5. Passage-level optimization beats page-level for AI Mode - Google AI Mode uses a passage ranking index, not a page ranking index. It looks for 100-300 token excerpts that support its reasoning chain. You can pepper relevant content across case studies, homepages, and comparison pages rather than building one monolithic page per topic. Learn More- SEO Notebook - https://seonotebook.com - Steve's weekly SEO newsletter, running since 2019- AI Notebook - https://ainotebook.com - Weekly newsletter focused on AEO/GEO strategiesConnect with Gen: - www.supermarketers.ai- www.linkedin.com/in/genfurukawa

Derek Iwasiuk, co-founder of SearchTides, breaks down his 5-layer "AI Search Undercurrent" framework for getting brands recommended by ChatGPT, Google AI Mode, and Perplexity. We dig into how he tracks 500+ prompts per client, why fan-out queries are the new keyword research, and the free tactics any founder can use today to boost their AI visibility, from Reddit AMAs to $80 press releases.Derek has 18 years of SEO experience and now runs operations and strategy at Search Tides, an AI search agency focused on financial services and beyond.Connect with Derek: www.searchtides.com⚡️ Grow 10x Faster with AI-Powered Growth System. No Team, No Agency, No Stress. Go here: https://supermarketers.ai/Follow me 👇https://www.linkedin.com/in/genfurukawa/===============================Who am I, and why should you listen to me?I’m Gen Furukawa — founder, operator, and marketing systems builder. I’ve built and sold a SaaS company, led marketing at a high-growth startup, and now help B2B SaaS teams scale content and demand with automation, AI, and strategy.At SuperMarketers, we don’t just give you content - we create powerful inbound growth engines that generate qualified leads without hiring too many people. I’ll share the exact strategies, processes, and automated systems we use with clients to help you turn your ideas into action, faster.Turn Your Team into a LinkedIn Growth Engine. Learn more: https://supermarketers.ai

In this episode, Gen Furukawa speaks with Carl Holden, founder and CEO of RankBot, to unpack one of the most misunderstood parts of modern SEO: how link authority, automated outreach, and external site signals power both traditional Google rankings and AI-generated answers across ChatGPT, Gemini, and Perplexity.Carl explains why backlinks still matter in an AI-first world, why large language models continue to depend on search results for rankings, and how marketers can build durable visibility in an era defined by Answer Engine Optimization.You’ll learn how to think about off-page SEO in an unreliable, fast-changing algorithm landscape, and why diversifying your presence across blogs, YouTube, TikTok, Reddit, and short-form video is becoming essential for LLM citations.01:35 Understanding Link Building and SEO02:52 Automating Link Building with AI04:22 Challenges and Strategies in Link Building12:02 The Future of Off-Page SEO17:15 Leveraging AI in Marketing22:58 Final Thoughts and Advice for Marketers

Highlights: 01:05 — How Gauge reverse engineers AI answers to uncover why some brands get mentioned while others don’t.04:11 — Why personalization in ChatGPT makes manual tracking nearly impossible without anonymous data.05:28 — The hidden value of showing up in top-of-funnel AI answers even when clicks disappear.07:02 — If you only had five hours a month, here’s the single most effective lever to grow AI visibility.09:15 — The “fat tail” effect in AI search and why cracking the top 50 matters more than ranking #1 on Google.18:47 — Case study: how one brand tripled AI visibility in two weeks and what it reveals about the future of search.=============================== Stop Publishing Content. Start Engineering Demand with SuperMarketers. SuperMarketers is the AI Content Engine for B2B SaaS. Built to rank, answer, and convert. Learn more at www.supermarketers.aiConnect with Gen Furukawa: https://www.linkedin.com/in/genfurukawa/=============================== Find Caelean Barnes on LinkedIn: https://www.linkedin.com/in/caelean/ =============================== ​Gauge helps companies show up in more AI answers. With traffic shifting off of Google and into AI, the way customers are discovering and deciding has changed.Gauge measures responses in AI answers to find the highest leverage opportunities for your brand. This enables you to increase your AI presence and drive more leads. Customers are seeing over a 5x increase in AI answer share with Gauge.Find out more at ​https://withgauge.com

Key Takeaways:AI-Proof Channels: Events, referrals, and trust-based marketing are emerging as "AI-proof" channelsThe 95% Rule: 95% of content cited by AI agents has been updated in the last 10 monthsFour Pillars of Winning Content: Unique/authoritative, persuasive (to both humans and AI), well-structured, and properly citedContent Engineers: New role combining AI knowledge, content expertise, and business process optimizationAEO vs SEO: Answer Engine Optimization requires optimizing for multiple AI agents, not just GoogleTactical Strategies Discussed:Using Google Search Console to find high-impression, low-click pagesInternal linking as an overlooked SEO winSchema markup and proper H1-H4 structure for AI agentsContent refresh frameworks (low/medium/high effort levels)Voice memo workflows for content creationCustom GPT projects for content strategy AirOps website: airops.comBenchmark your top pages against the best in SaaS—and see what’s costing you visibility: https://www.airops.com/answer-engine-visibility

Show Highlights: 5:13 – Why SEO is breaking: AI-generated content, noise in SERPs, and Josh’s pivot to LinkedIn10:40 – Josh’s AI content workflow: repurposing blog posts into LinkedIn posts using ChatGPT17:00 – What is RAG (Retrieval Augmented Generation) and how it helps encode brand voice into AI outputs21:00 – Data prep for vector databases: chunking content, using parsers, and why this step is critical25:00 – How marketers can use segmentation and clustering models for better GTM strategy32:00 – When LLMs work well: text generation, summarization, sentiment analysis, and chat transcript insights34:00 – The future of LLMs in orgs: how they could replace middle management through summarization📍 Visit Josh at Sharp Sight Labs🤝 Connect on LinkedIn: Josh Ebner

Hop OnlineParis Childress on LinkedInShow Notes: 4:00 - Building an ICP Matrix with AI: Understanding customer profiles 8:00 - How To Use client call transcripts to build better AI outputs 14:00 - Creating an AI council: Getting 30 people on board without fear 16:00 - Using GPT-4 as a strategic thought partner for agency transformation

Greg Brooks on LinkedInSearchTides WebsiteHighlight Timestamps:3:45 - Greg explains how AI is changing search behavior and the importance of creating efficient, valuable content9:15 - Discussion on the process of identifying core topics and creating content strategy16:30 - Greg shares a real-world example of content strategy using personal lending as a topic23:45 - Insights on the future of commerce and how AI will impact company growth and consumer choice28:30 - Greg's perspective on the importance of brand equity in the future of marketing

Matt McGrory on Linked INCheck out ArwenTimestamps: 0:57 - Explanation of AI-powered content moderation2:57 - Using RAG for brand-specific engagement5:22 - Balancing AI automation with human oversight8:28 - Prompt engineering and human-reinforced learning9:00 - Using AI for creative storytelling in marketing12:27 - Manual moderation techniques before AI tools14:00 - Creating a structured approach to social media engagement17:32 - Understanding brand health reports and benchmarks21:38 - Analyzing sentiment and emotions in social media campaigns26:53 - Key takeaways for growing on social media using AI30:46 - Closing remarks and contact information

Check out Tykr: https://tykr.com/Show Highlights: 00:00 Introduction and Guest Welcome00:19 Sean Tepper's Background and Journey02:24 Ticker's Growth and Features03:49 Marketing and AI Integration04:17 Content Creation Workflow06:47 SEO and Content Optimization10:39 Leveraging AI for Content19:53 Learning Resources and Final Thoughts