A big mistake companies make with AI-generated content
episode
Voices of Search // A Search Engine Optimization (SEO) & Content Marketing Podcast
3 min
2 speakers
6 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 dot
com. 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.
What mistake do companies make when using AI‑generated content?
All right, Carl, one mistake companies make with AI generated content.
Thinking they're the best prompters out there. I I see people say like, I have this amazing content delivery system. It's eight stages long, and I have perfected every single prompt. That might work for now and it might work for the next month, but people that write really good, unique content with unique data, unique perspectives, and unique expertise.
Why is being a “perfect prompter” not enough for long‑term SEO success?
in the long run are going to beat you.
Time for a one-minute break to hear from our sponsor, Previsible. So you're looking for SEO help, then you got a couple of options. You could start replying to spam from agencies that claim they can get you to rank number one on Google. You can pay an hourly rate for a consultant who will inevitably nickel and dime you with hourly charges, or you can work with a cookie cutter agency to quickly launch a strategy-less project with low success rate. None of those sound very good.
How does fan‑out analysis help map user intent beyond traditional keywords?
Now do they? Well, that's where Previsible's integrated consulting model comes in. Previsible draws from a collective 40 years of SEO and digital marketing experience to unlock your organic growth opportunities. They build custom solutions that combine strategy, technical expertise, content, and reporting to effectively operationalize SEO for your business. Pre-Visible's four-stage approach ensures that your SEO programs thrive.
What local rank‑tracking methods account for AI overview variations across verticals?
By starting off with a strategy first approach. Then they support you in your efforts to create quality content, help you identify technical issues, and most importantly, they'll work with your cross-functional teams to integrate your SEO strategies to make sure that your SEO budget actually drives results, not just your agency's bottom line. So join brands like Yelp, eBay, Canva, Atlassian, Square, all who rely on the SEO consultant. At Previsible. For more information, go to previsible.io. That's previsible P R-E-V-I-S-I-B-L-E. Io.
How can custom tool frameworks integrate multiple LLMs for scalable content briefs?
A huge thank you to Carl Kleinschmidt, founder at Data Marketing Group, for joining us. If you'd like to contact Carl, you can find a link to his LinkedIn profile in our show notes or on the voices of search dot com. You can also visit his company website, Carl K dotai. If you haven't subscribed yet and want a daily stream of SEO and content marketing knowledge in your podcast feed, hit the subscribe button in Podcast App or on YouTube and we'll be in your feed every week. Okay, that's all for today, but until next time, remember the answers are always in the data.
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Chapters
6 chapters
1
What is the main topic discussed in this episode?
0:00–0:43
2
What mistake do companies make when using AI‑generated content?
0:43–1:15
3
Why is being a “perfect prompter” not enough for long‑term SEO success?
1:15–1:43
4
How does fan‑out analysis help map user intent beyond traditional keywords?
1:43–2:08
5
What local rank‑tracking methods account for AI overview variations across verticals?
2:08–2:46
6
How can custom tool frameworks integrate multiple LLMs for scalable content briefs?
2:46–3:15