06/08/2025
Here's a quick insight on how AI surfaces local businesses on search results:
LLMs use a chain of thought ranking score. This means that a local result within AI is shaped by things like:
> Chain-A = looks at static directory citations
> Chain-B = looks at Google Business detail and proximity
> Chain-C = looks at semantic relevance within website content
Every chain is ranked individually against the relevance to the prompt/query within AI search.
The business with the highest total score for all chains wins.
All chains are logged, so if a user asked a follow up question, like "which store is closest to me", the results can be re-ranked.
One key thing that stands out to me in this process is AI's reliance on static citation information.
If you ask ChatGPT:
"show me where [business name] is", you will see the thought process. It looks something like this:
> The user wants to know where X is. Maybe they need a map
> I'll search for "[location] map. I've found that the address is X"
> Image results don't provide me a map. Maybe I could use a static street map, or an aerial image of the address.
> I think I can show an image carousel with a map of [suburb], which I found, along with maybe a bus route map. I’ll choose the best images to present.
Result comes back and the location details are citied from places like:
> localsearch, Yelp, YellowPages, Cyclex
Public directories like the above give static markup. These are faster to fetch and than Google/Bing, and avoid any API issues.
The key takeaway from this is that:
> Citations matter A LOT for AI search
> NAP (name, address, phone) consistency is also a major factor in chain of thought ranking
> Given Google's AI Overviews & AI Mode both can return aggregated summaries of local businesses, citations are still a key factor in local SEO.
So while citation work itself is the same as ever, it's become a stronger lever for AI.