Keyword research used to mean one thing: which phrases does someone type into Google. That definition is quietly breaking down. People now ask questions directly inside AI assistants, get summarized answers from search engines instead of a list of blue links, and never actually visit a page for a lot of the information they used to click through for. For any business with more than one location, that shift changes what “ranking” even means.
Why Traditional Keyword Tools Are Starting to Miss the Point
Most keyword research tools were built around one search engine and one results page. They’re good at telling you search volume and competition for a phrase, but they were never designed to answer a newer question: does an AI assistant actually mention your business when someone asks it something related to what you sell? That’s a completely different kind of visibility, and it requires tracking a different kind of keyword, the conversational, question-style phrases people use when they’re talking to an AI system rather than typing into a search box.
Multi-Location Businesses Have It Even Harder
A single-location business only has to worry about ranking, or getting cited, once. A business with ten, fifty, or five hundred locations has to solve that same problem over and over, for every city, neighborhood, and service area it operates in. Doing that manually with spreadsheets and one-off searches doesn’t scale, and most keyword platforms weren’t built with that kind of repetition in mind either.
This is the specific gap that RankRabbit.ai was built to close. Rather than treating keyword research as a one-time list of search terms, it’s built around tracking how a brand shows up across dozens or hundreds of locations at once, including how often AI engines cite or mention the business when answering location-specific questions. For an agency managing a franchise client or a multi-location service business, that turns keyword tracking from a manual chore into something that can actually be monitored location by location.
What to Actually Look For in an AI Keyword Strategy
- Question-based phrasing — “best plumber near me open now” behaves differently than “plumber [city]” in how AI systems answer it.
- Location-specific citations — whether AI answers mention your specific branch, not just your brand name generally.
- Consistency across locations — one city ranking well while another is invisible is a signal worth investigating, not ignoring.
- Reputation signals feeding the answer — AI engines increasingly pull from reviews and citations, not just website content, when deciding what to recommend.
None of this replaces traditional SEO, it sits on top of it. But agencies and multi-location brands that treat AI visibility as an afterthought are going to find themselves invisible in exactly the channel more of their customers are starting to use first.
Tools built specifically for this, like RankRabbit.ai, are becoming less of a nice-to-have and more of a basic requirement for anyone managing search visibility at scale.
Frequently Asked Questions
What’s the difference between AEO, GEO, and traditional SEO?
Traditional SEO targets search engine result pages. AEO (Answer Engine Optimization) and GEO (Generative Engine Optimization) target how AI assistants and generative search summarize and cite a business when answering a question directly, rather than listing links.
How many locations can this realistically cover?
The point of a purpose-built platform is that it doesn’t matter whether it’s ten locations or five hundred — the tracking approach is the same at any scale, which is the part manual research can’t keep up with.
Does this replace a keyword research tool I already use?
No, it complements one. Traditional keyword tools still matter for classic search; this adds the AI-citation layer most existing tools don’t track.
For more on the platform shifts reshaping how agencies work right now, see our recent piece on this month’s biggest marketing agency changes.
