AI search is changing not just how consumers find businesses, but how brands get chosen. In this CEO conversation, SOCi’s Afif Khoury speaks to benchmarking the new rules of visibility – from reverse engineering the ways AI engines synthesize answers, to optimizing one’s presence with agentic automation and consistency. In the AI search era, it’s both presence and persistence that determine who gets surfaced.
We’ve always thought there should be a Fortune 500 for local media, advertising, and commerce. That could apply to the sell-side players (networks, apps & tech vendors), or the buy side (brands and SMBs that advertise and operate in local markets). SOCi has accomplished the latter with the SOCi100.
As a corollary to SOCi’s Local Visibility Index (LVI), the SOCi100 lists and ranks the top players among multi-location brands. And like LVI, it can serve as a benchmarking tool. Key questions answered by the SOCi100 include “What are the top-performing multi-location brands?” and “What are they doing right?”
These utilitarian and tactical ends separate SOCi100 from the Fortune 500 and other well-exposed lists that can be a bit of a vanity play. Companies scoring high on the SOCi100 have tangible lessons. Others that are low (or not) on the list can correspondingly use those takeaways as a guide to boost their rank.
“The idea for it really came from a gap that we saw in the market, said SOCi founder & CEO Afif Khoury in a recent sit-down with Localogy Insider. “Marketing is so hard for these multi-location brands. But at some point, you want to answer the question, what does ‘good’ look like? So we realized we were sitting on a massive amount of location data across millions of locations, across all the channels, and we had a unique opportunity to surface that into something actionable, not just rankings.”
SOCi’s LVI Report Reveals How AI Is Rewriting Local Search Visibility
Rules & Ranking Factors.
So what were the success factors among high-scorers? The headline is that AI continues to gain momentum and gravity as a factor in online visibility. More specifically, AI-based search – such as search GPT, Google AI-Mode, and a growing crop of others – have rewritten the rules and ranking factors.
There are several ways that’s playing out but one key driver is that AI engines are conditioning users to search differently. For example, AI-engine queries are about 6x longer than traditional search queries, says SOCi. That means users are getting more nuanced in how they describe what they’re looking for.
That nuance is important because – just like traditional SEO reverse-engineers the way people search – businesses need to optimize their digital presence differently. For example, instead of the traditional art of optimizing for keywords, the name of the game in AI SEO is to optimize for conversations.
For example, all that nuance in user queries means AI engines are reaching deeper into online sources to answer specific questions. They’re not just relaying answers from structured data, such as listings. They’re synthesizing answers using LLMs that dive deep into a range of sources, like business reviews.
This amplifies challenges for marketers. Greater emphasis on unstructured data like reviews – which, to be fair, have always been a factor in SEO – requires more rigor. In addition to static content, it’s about content that’s constantly changing, and therefore requires constant attention and action.
“This year’s SOCi100 is less about who checked the boxes, and more about who’s consistently executing at a high level across all those dimensions,” said Khoury. “Reviews impact SEO, but they also impact how the learning models see you. Responding to reviews impacts the learning models. They’re looking at those responses as a heavy factor.”
SOCi Study: LLM Queries Are 6x Longer Than Traditional Search
Both the Problem & the Solution
But as AI upends the art of online visibility, it can also help put it back in place. For example, though there’s now a need to do things like rapidly respond to and manage business reviews, AI continues to get better at doing just that. Put another way, AI is both the source of the problem and the solution.
“AI shows up in two ways,” said Khoury. “There’s AI as the new channel that you have to manage. But then there’s AI as the latest weapon to help you become more visible.”
SOCi itself is incorporating this philosophy into its product development. For example, it’s building language models for its customers – multi-location brands. And it can scale this effort by training individual models on a given brand’s playbook, then deploy that model across thousands of locations.
That model can do things like learn to speak in the voice of the business, with all of its brand guidelines and other parameters. When deployed to franchisees, they can spend their time on daily operations, rather than worry about constantly managing their Yelp reviews and other marketing grunt work.
And that’s the thing about multi-location marketing. There’s always a balance – and it’s different for each brand – between centralized (franchisor) and decentralized (franchisee) execution. The latter is critical for scale and authenticity, but it raises issues around consistent execution across a brand’s network.
Automating AI SEO to an extent can bring that consistency and quality control. And the best part is that it won’t get much resistance at the location level. Aligned with our checklist for AI applicability in local marketing, when you can take away digital grunt work, you won’t face a lot of adoption friction.
“There is so much to do with thousands of locations. You have to respond to reviews, post content, update the search, update pages,” said Khoury. “Generative AI is not really going help you. That’s still a tool for you to use. You can walk up to it, you can use it. But if you got a thousand locations, it didn’t solve your problem. It’s about building the agents that can really be educated on your brand, and then trained on a workflow to literally execute all the time for you.”
Fight Fire with Fire
All the above findings are valuable not just in their data-backed validity but in their challenge to previous industry assumptions. For example, though it’s been an open question, there’s been some degree of consensus that optimizing a given business for AI SEO (a.k.a., GEO), flows from a strong SEO foundation.
The SOCi100 shows that SEO basics do indeed prime a given business for AI-engine visibility. But any company that relies on that alone – especially multi-location brands – will be be at a competitive disadvantage. This boils down to a mix of traditional SEO and a quickly-developing AI-native playbook.
As for what’s next, all the above isn’t slowing down and will only accelerate. AI’s impact on how consumers find things online will grow as AI’s share of search itself does. Google is still strong, says Khoury, but AI engines are catching up fast. This means that brands need to use AI to fight fire with fire.
“AI is fundamentally changing how you can actually get work done, especially for multi-location brands,” said Khoury. “That’s what’s going to separate this year… but wait till you see next year’s SOCi100. Brands that are adopting an agentic mindset, agentic workflows, and frankly, agentic workforces are going to move a lot in next year’s list, because they’re getting a lot done. And that’s going to get rewarded.”
Header image credit: Giorgio Trovato on Unsplash
