Fan out analysis, Local rank checks in AI
episode
Voices of Search // A Search Engine Optimization (SEO) & Content Marketing Podcast
45 min
2 speakers
8 chapters
transcribed 1 month ago
Transcript
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Transcript generated automatically by AI and may contain errors.
What is the main topic discussed in this episode?
The Voices of Search Podcast is a proud member of the I Hear Everything Podcast Network. Looking to launch or scale your podcast? I Hear Everything delivers podcast production, growth, and monetization solutions that transform your words into profit. Ready to give your brand a voice? Then visit IHear Everything.com. Welcome to the Voices of Search Podcast, a member of the I Hear Everything Podcast Network. Ready to expedite your company's organic growth efforts? Sit back, relax, and get ready for your daily dose of search engine optimization wisdom. Here's today's host of the Voices of Search Podcast, Jordan Cooney.
AI is changing what it means to create content because in AI-powered search, it's not about who writes the best article, it's about whose content gets cited. In fact, recent research shows that 80% of sources cited by AI systems don't even appear in Google's top results, which means ranking isn't the same as visibility anymore. And that creates a new challenge for SEO teams. So the question is, in a world where AI summarizes everything, how do you create content that actually gets chosen? I'm Jordan Cooney, and joining me today is Carl Kleinschmann, founder at Data Marketing Group, an 18-year SEO veteran focused on local SEO, large-scale data systems, and LLM optimization. Today we're talking about fan out analysis, local rank tracking, and why LLM optimization is really about intent, not keywords.
Carl, welcome to the Voices of Search Podcast.
Thanks for having me back.
Hey Carl, it's so much fun having you on the show. I mean, obviously we've known each other for a long time, but you have Constantly been innovating. You you don't stop innovating. And this is an industry that's going through probably its biggest transformation. I just remember like a few years ago, you were showing me some like heat mapping with uh uh ranking data. Um and now we're looking at a whole new world with with LLMs and how LLMs work. Um and you know, LM's LM optimization. is really all about intent. And I want to know from from your innovator mindset, what does this actually mean and how should should individuals put this into practice?
I think the biggest change is you stop thinking about what people are searching for and what they instead what they want. Right. It's it's no longer the right approach to say, okay, they're gonna search I want a red running shoe. They want a shoe that helps them while they're running, that looks stylish, their favorite color is red. And so you need to figure out what the intent is of what people are actually searching for. And then you can figure out how do I best write the content to fit that intent.
And this this reality of like mapping to like what users are looking for and what this intent is, um, is this a purely data driven thing? Is this a survey thing? Is this a guess and trial and error experiment thing, like where where do you get that insight? I think I think that's one of the things that, you know, historically as SEOs we used to do these like content briefs and we used to do all this other other stuff, right? But now this is changing a little bit and and and I'd love to know your perspective on like where do you get the the knowledge, the the the data source?
So so my approach has been to basically I don't know if teach is the right word, but basically provide L LMs all of the data around a company and then say, I wanna figure out What are the sub-intents of a query, right? Most people call it fan out. Um, but the interesting thing is there doesn't seem to be a clear definition of what fan out actually is, right? Like how you get sub-intents seems to change. Every person does it a little bit differently, every LLM does it a little differently. Um, a lot of the researchers that are really going deep, um, I think they're getting a Uh fan out actually works, but I have not seen any amazing tools that are like this is how you consistently do the perfect fan out.
Right. I mean and like fan out is like one of these components of intent, right? But I mean, even like taking a layer back, like listen before we get in even deep into fan out, like you gotta know what the prompt is, right, to some extent to even get to the fan out.
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Chapters
8 chapters
1
What is the main topic discussed in this episode?
0:00–4:52
2
Why does AI‑generated content need to focus on intent instead of just keywords?
4:52–9:57
3
How does Carl define and apply fan‑out analysis for uncovering sub‑intents?
9:57–16:49
4
What data sources and tools does Carl use to feed LLMs for local SEO?
16:49–23:58
5
How can marketers validate fan‑out queries with Google Search Console and Bing data?
23:58–30:51
6
Why are geographic personalization and travel distance critical for local rank tracking?
30:51–37:39
7
What new metrics should teams measure for AI‑driven local discovery?
37:39–43:50
8
Which SEO tactics does Carl consider overrated in the age of LLMs?
43:50–45:09