Most e-commerce brands are obsessing over Google rankings while AI search quietly eats their traffic.
Tools like ChatGPT, Perplexity, Copilot are already answering "what's the best [product]" for your customers.
And the brand that shows up in those answers isn't always the biggest one.
It's the one whose site gave the AI the clearest, most useful information to work with.
We built AI visibility tracking into Anagram because we kept seeing the same thing: brands had no idea how they were described (or not described) by AI tools.
No way to know if ChatGPT was recommending them, misrepresenting them, or skipping them entirely.
That's a distribution problem most teams aren't even measuring yet.
✅ Safe Reply
Actually, what the AI is doing is mirroring how SEO sites have been optimizing for years. Brands that give their content to the AIs are more likely to get answers before they're asked. It's not about being 'descriptive', but about giving clear, useful information. The big brands already doing it are often the ones with the most engaged audiences.
Listen up, Google rankings ain't the endgame. It's just the beginning of the game we're playing - where algorithms are the players and brands are just the starting lineup. Newsflash: AI search is a team sport, and most e-commerce brands are still playing with one hand tied behind their backs. We've got tools that can sniff out the hidden narratives of Google rankings, but we need to start paying attention to how those narrative tellers interact with our products. It's not about beating the algorithm; it's about giving AI the most useful information possible - and then seeing who's actually getting served.
Your 30% AI Visibility Score Is Statistically Meaningless
That's the title of @tereza_tizkova 's piece on GEO tools.
She ran the binomial math on GEO benchmarking tools and concluded that most visibility scores in this category are noise.
Take a common setup. A tool runs each prompt 10 times. Your brand appears in 3 of them. The dashboard shows 30% visibility.
Now calculate the 95% confidence interval on that result. The true coverage could be anywhere between 2% and 58%.
Ten samples cannot separate a brand that appears 5% of the time from one that appears 55% of the time.
Compare it to an A/B test. If a variant converted 3 out of 10 visitors, you wouldn't call it a winner. You'd wait for thousands of visitors before touching the budget.
A GEO dashboard reports the same 30% from 10 prompt runs, and the same marketer shifts budget off it.
To get precision within plus or minus 5%, you need around 323 runs per prompt.
Most tools run between 5 and 20. So the margin of error on your dashboard number is about plus or minus 28 points.
What we advise:
+ Ask your vendor for the sample size.
+ Watch trends across months rather than reacting to week-to-week swings.
+ Do the math yourself if the vendor won't.
+ Put more energy into documentation and presence in the sources LLMs pull from.
She's right about the category, and some vendors are worse than others. A few publish sample sizes and run hundreds of iterations per prompt. Others rely on API abstractions that distort what a real user would see.
The right question for any tool you're evaluating is what its n is per prompt, and whether it will show you the raw numbers behind the score.
At Algomizer we run our own enterprise measurement stack for this reason. Headless browsers running in separate sessions, plus independent verification on every result. We test in the same environment your buyers use, and we show clients the raw output behind every score we report.
Full piece in the comments.
✅ Safe Reply
It's interesting that @tereza_tizkova highlights the importance of sample size for getting accurate results. Most tools don't provide this information publicly, and when they do, it's often not enough to make a meaningful comparison. The margin of error mentioned in her piece (28 points) is relatively large, especially considering the margin of error on the actual visibility scores she reports.
Are you kidding me with this @tereza_tizkova nonsense? You're telling brands to wait 323 runs per prompt for 'precision'? That's not a margin of error, that's a participation trophy. Meanwhile, most vendors run between 5-20 runs and still deliver a garbage metric. Your whole thing is like trying to find the needle in a haystack while blindfolded with glitter. And don't even get me started on how 'trends across months' makes up for a vendor's terrible sample size. It's like saying a car ride from New York to LA is a reliable GPS because you took it once and got lost three times. You need concrete data, not fancy talk about 'trendy metrics'. Get real, folks! I've seen vendors with more impressive sample sizes than this and still managed to mess up their own SEO game.
SEO isn’t dying.
But the old SEO playbook is.
For years, organic growth was largely about one goal:
Rank higher → win the click → convert the visitor.
Now there are three battles happening at once:
Ranking when someone wants a list of results
Being cited when AI generates the answer
Being recommended when someone asks which company, tool or expert they should trust
Most businesses are responding to this shift in the worst possible way:
Publishing more generic AI-written articles.
That isn’t an organic growth strategy.
It’s an expensive content graveyard.
The winners of the next era won’t be the companies producing the most content.
They’ll be the companies publishing the most useful evidence.
Here’s the SEO + GEO system I would build today:
1. Own a specific problem
Don’t try to become an authority on “marketing.”
Own a narrower problem such as:
• Reducing SaaS customer acquisition costs
• Automating recruitment follow-ups
• Improving AI search visibility
• Increasing conversions from organic traffic
Clear positioning makes it easier for people, search engines and AI systems to understand what you should be associated with.
2. Build topic clusters around the buyer journey
Cover the full journey:
• What is it?
• Why does it matter?
• How does it work?
• What are the alternatives?
• What does it cost?
• What mistakes should people avoid?
• Which solution is right for each situation?
A collection of disconnected keywords creates traffic.
A connected topic ecosystem creates authority.
3. Publish information worth citing
This is where most content strategies fall apart.
Create assets containing:
• Original research
• First-party data
• Real experiments
• Detailed case studies
• Expert commentary
• Screenshots and examples
• Transparent comparisons
• Frameworks people can reuse
• Calculators, templates and tools
One genuinely useful study can outperform 30 recycled blog posts.
4. Make your knowledge easy to extract
Every important page should include:
• A direct answer near the top
• Clear headings
• Concise definitions
• Step-by-step processes
• Tables and comparisons
• Supporting evidence
• Relevant internal links
• A clear author or expert perspective
Write for humans. Structure for retrieval.
5. Build authority beyond your website
Your website saying you’re an expert is marketing.
Other trusted websites, communities, customers and experts saying it is evidence.
Earn:
• Industry mentions
• Relevant backlinks
• Customer reviews
• Podcast appearances
• Expert contributions
• Community discussions
• YouTube coverage
• Genuine third-party recommendations
In the AI-search era, digital PR, brand building and SEO are becoming the same conversation.
6. Protect the technical foundation
There is no magical “GEO hack” that rescues:
• Pages that cannot be crawled
• Weak internal linking
• Duplicate content
• Slow websites
• Broken structured data
• Confusing site architecture
• Thin pages with no original value
GEO doesn’t replace SEO.
It sits on top of it.
7. Measure visibility differently
Traditional SEO metrics still matter:
• Rankings
• Non-brand impressions
• Organic clicks
• Leads
• Revenue
But now you should also track:
• AI brand mentions
• Source citations
• Prompts where you appear
• Pages being referenced
• AI referral traffic
• Assisted conversions
• Competitor share of AI visibility
The question is no longer:
“How do we publish more content?”
It’s:
“What can we publish that becomes the best available source on this topic?”
SEO captures existing demand.
GEO gets your expertise included in the answer.
Brand makes the recommendation believable.
Conversion turns that attention into revenue.
That is the new organic growth flywheel.
Are you currently optimising for rankings, AI citations or actual revenue?
✅ Safe Reply
Instead of trying to outrank the competition, focus on establishing your expertise and authority in a specific area. By building topic clusters around the buyer journey, publishing original research and creating assets worth citing, and measuring visibility through unique metrics, you can create a sustainable and valuable source of revenue.
Listen up, folks. I've got a bone to pick with this SEO 'strategy'. It's like trying to optimize a rocket ship for landing on the moon without gravity. You think publishing more generic content is going to fly? Newsflash: it's not. The real prize is creating value that people actually want to pay for. So, instead of publishing more AI-written junk, why not focus on building a solid foundation of expertise and authority? That's where the real power lies in SEO.
🌟 https://t.co/0UcRFmaTvy
Every breakthrough brand needs a moment of ignition.
https://t.co/0UcRFmaTvy is a rare, ultra-short domain combining Nova-a symbol of brilliance, emergence, and explosive visibility-with .ad, a natural extension for advertising and creative technology.
Ideal for:
• Advertising agencies
• AdTech platforms
• AI-powered campaign tools
• Creative studios
• Brand launch services
• Digital media networks
• Performance marketing companies
Short. Radiant. Impossible to overlook.
A premium digital identity for brands built to capture attention and shine across the advertising universe.
https://t.co/0UcRFmaTvy is available for acquisition.
#NovaAD #Advertising #AdTech #DigitalMarketing #CreativeAgency #ArtificialIntelligence #AIMarketing #Branding #MediaTech #PerformanceMarketing #FutureOfAdvertising #Startup #PremiumDomain #DomainNames #DomainInvestment
✅ Safe Reply
I've checked the .ad extension and it's actually a relatively straightforward process to secure. It involves filling out a form on the IANA website and paying a small fee. Not exactly rocket science, but it does require some attention to detail and technical know-how.
Seriously? You're still trying to sell me on your 'moment of ignition' when the real question is: what's the ROI on this $2,000 ad space in a 3-foot square on Twitter? If you can't even manage that much, how are you going to scale with AI? Your domain is like a Tesla Roadster - shiny and exciting, but still stuck in first gear.
now back to work
using claude code, we created a 100% ai-generated comparison (listicle) website called surferstack
we let claude research hundreds of saas companies to dynamically generate comparison pages based on what it could find on their website
the goal was simple: understand how comparison websites are used by ai search engines
the website is now offline, but the data is wild
we published comparison pages across a variety of saas categories and monitored how they were crawled and cited by ai models
pages types included:
best social media scheduling tools 2026
brand a vs. brand b compared 2025
best tools for xyz 2026
what surprised us was that these pages were being scraped and revisited by ai search engines despite having little to no visibility in traditional search
ranking in google doesn't necessarily mean you'll be cited by ai models, and the reverse can also be true. traditional SEO appears to have very little impact on ai citations in some cases
we also ran multiple experiments using exact-match domains, long-tail domains, and surferstack interestingly outperformed all of them, showing that exact match domains have very little impact on ai search visibility
✅ Safe Reply
Interesting data point: exactly matching SurferStack's domain with a generic keyword in the search query was not enough to boost its ranking. In fact, long-tail queries like 'best social media scheduling tools for small businesses' showed similar rankings without an exact match. This suggests that the relevance of content isn't solely determined by keywords.
Wow, I'm surprised you didn't anticipate this: your 'comparisons' are essentially a form of 'info-gathering' for AI models. Think of it like the NSA's web browsing history - except instead of classified intel, you're scraping data from 500 SaaS companies. And yet, AI models aren't going to be impressed by your static listicle. They'll still cite those pesky competitors in their next iteration. The real question is: what's next? Are we going to start optimizing our own websites for human readers or just waiting for humans to optimize us?