Everyone understands that AI is changing virtually every aspect of marketing.
Yet while everyone is looking at using AI to create everything from video to podcasts, everyone is focused how to get discovered in AI search queries. Given all the buzz, it is important to unpack this new practice as best we can.
That said – we need a huge, blazing, 10 foot high “caution” marquis as we proceed.
FACT: If any vendor says they definitively know this space, they are either delusional or deceptive. I do not say this lightly because as a firm that has developed AI and an AI search practice, this is a deeply informed opinion.
FACT: GEO is a fast moving, emerging category, and there is much we don’t know yet. Heck, we don’t even all yet agree on a name. GEO is just one name for getting discovered in AI Searches. AEO (Answer Engine Optimization), AISO (AI Search Optimization) and the expected, AI SEO all are used by different firms to define AI search discoverability. Many of these firms like to position GEO as an evolved version of SEO.
Yet it’s more complicated than that.
Both practices, SEO and GEO, have the same end goal – get “discovered” by the digital brain so your brand can get presented to users when users ask questions.
Both practices reflect a content-centric model relying on content’s ability to get digital algo’s to tag a brand’s web content when a relevant query comes along.
Both practices, are data dependent to “translate” a web page written for people into a digital footprint that a mechanical circuit can understand. SEO requires keyword and meta data whereas GEO requires topic data that is deeper in intent signals.
Our CTO characterizes GEO this way: “No one really knows how to predictively influence an AI search because LLMs don’t know or understand facts. They only look for patterns.”
Discoverability in AI is about creating a translation engine between how people understand content and how AI algorithms process patterns of a brand’s content. This is still a WIP – for everyone.
With that cautionary preamble, it is true to say GEO is the next big frontier for brand discoverability. Yet so many questions come out of this new practice.
Is SEO going away? No.
Is GEO replacing SEO? No.
Is GEO just as an evolution of SEO? Not really.
SEO is not going away even as GEO becomes more dominant. So yes – there is one main similarity between SEO and GEO in the overall “discoverability” goal. After that, there are significant and subtle differences between the two.
SEO is in the business of getting a human click to a site whereas GEO’s outcome is to get the AI brain to categorize your content as an authority or resource for its AI machinery. Once AI knows you are a resource for users, then they will present your branded content again and again and again.
So what is the data that triggers GEO to recognize a site as an authority?
This is, as noted above, one of the main differences. While brands can get keywords from many SEO platforms (like Google), GEO needs a different data layer that understand which topics (not keywords) drive actual outcomes, (Topic Intelligence <dot> ai is an example of this type of data). The big SEO data companies are not in the topic business – they are in the narrower keywords data business. Topic data, by contrast, allows brands to dive deeper in developing the type of content that AI first, and then users, will recognize as “authoritative.”
Here is an example.
Let’s say the brand sells clean energy kitchen appliances. Keywords would include clean stovetops or energy efficient refrigerator based on user query.
By contrast, GEO wants authoritative (aka – resource) content. With the right topic data, it will reveal that a key topic for this space is “smart energy.” This is much different than typical SEO keywords as smart energy has a much deeper meaning. It can mean, simultaneously, “save money” and “smart for the planet.”
This “smart energy” concept or theme can become the basis for an entire campaign – from resource content (i.e. – “how to” or “why to”) to social and video content. Smart energy, as a theme, reveals an intent-rich concept that feeds the AI engine while driving better conversions. This is why GEO’s muscly topic-data centric practice can outperform SEO practices.
This also explains why articles written for SEO works well for SEO but not as well for GEO. Given that SEO and GEO are powered by different data, it follows that SEO and GEO have different information architectures to drive different behaviors.
SEO content is meant to be “stand alone” to drive a click from a list of results.
GEO content is organized to get AI to hoover up interrelated topics and content to drive the authority classification to a brand’s content.
In practice, this GEO informational architecture looks like this.
Once the topic data reveals high conversion topics predictively, a brand can create a pillar/ cluster article structure. Pillar articles are dense and deep (often 8,000 words) and cover a category fully. These articles are meant to “seduce” AI engines to suck up all this content. Cluster articles then take one element in a pillar article, a question or process, to delve deeply into that one aspect of a pillar article. Extensive cross-linking between pillar articles and cluster article and between different cluster articles is key part of this architecture.
Together, pillar and cluster content is the information architecture of GEO as that is how AI “absorbs” web content. Since GEO does NOT scrape web pages like SEO but merely aggerates content patterns without understanding any of it, we can appreciate how different SEO is really from GEO.
A parting thought about AI we must all surrender to. AI is never going to be fully comprehensible to humans anymore than we can truly understand how a whale “thinks.” AI really stands for “Alien Intelligence” – discernable but not completely comprehensible.
Which leaves us with a perplexing question; How do we learn to translate AI information back into human intelligence?
That is what we are all still figuring out. Ya – it’s very very complicated.
