How we find which local directories AI cites
How we use page- and domain-level citation data and offsite mentions, city by city, to find the directories and local sites behind AI answers, and how we decide where a client should get listed.
Every local client has heard that they should "get listed in the right directories." Very few have been told which ones, and most lists we've seen are the same generic set regardless of city or trade. For AI search we don't start from a list. We start from what the answers in each city actually cite, and work backwards.
Why we do this per city
The sources behind a local answer can differ from one town to the next. A regional trade association, a local newspaper's "best of" page or a city-specific review site might matter in one market and not exist in the next. If we pooled every city into one citation report, the national sites would crowd out the local ones and we'd miss exactly the sources that are worth chasing.
So the starting point is the monitor setup we use for every local program: one monitor per city or per walk-in location, with the same prompts in each. That setup is described in how we set up local GEO tracking for a new client. Once those monitors have a few weeks of daily data, there's enough citation data per location to read.
Step one: domain level, one city at a time
We run client programs on Promptwatch, and its citation analytics work at two levels: the domain and the specific page. We start at the domain level, filtered to one city's monitor.
The question is simple: which domains keep showing up behind answers to the unbranded prompts in this city? We read down the list and sort what we find into rough groups. Directories and listing sites. Review platforms. Local news and community sites. Competitors' own websites. Forums, Reddit threads and YouTube videos (Promptwatch has separate Reddit and YouTube citation reports for those). And the client's own pages, which we separate out with the "My channels" filter so they don't clutter the view.
We don't name directories in advance. Whatever turns up in the citation report for that city is the list we work from. If a site we'd have expected doesn't appear, that tells us something too.
Step two: page level, to see the actual listing
A domain showing up doesn't tell us much on its own. A big directory might be cited for its category page in one city and for a single business profile in another. So for each domain worth a closer look, we go to the page level and look at the exact URLs being cited, along with the position each URL holds in the answer.
That answers the questions that matter for a listing decision. Is it a category page ("plumbers in this town") or an individual business page? Is the client on that category page? Are competitors? When a single competitor's profile page on a directory is cited over and over, that's a strong hint about what the engine is reading when it recommends them.
When a cited page matters enough that we want to follow it over time, we add it to Page Tracker, which watches specific URLs, ours or anyone else's.
Step three: offsite mentions
Not every source links. Some local pages name a business without linking to it: a council page, a roundup in a local magazine, a community forum post. Promptwatch's offsite mentions report shows third-party pages that name the brand without a link. For local clients we use it two ways.
First, to find places the client is already mentioned in each city, which are often the easiest wins: correct the details, ask for a link, make sure the listed address and hours are current.
Second, to build an outreach list. We set it next to the page-level citations from step two. A local page that keeps getting cited, names two competitors and leaves the client out is a much better outreach target than anything on a generic list, because we know an engine is already reading it.
Step four: decide where to get listed
With the citation list for a city in front of us, we sort each source into one of four outcomes.
The client is listed but the details are wrong or thin: fix it first. This is usually the cheapest work on the list and it removes a source of wrong facts.
The client isn't listed, the source is cited across several prompts, and listing is free or reasonable: get listed, with the same name, address, phone and hours as the Business Profile and website.
The source is cited but listing is paid: we look at how often it's cited and in how many cities before recommending the spend. A paid listing cited once in one city isn't worth it to us.
The source turned up once and never again: ignore it for now and revisit at the next review. AI answers move, and a single citation isn't a pattern.
We'd rather recommend five listings that show up in the client's own citation data than fifty from a generic list.
Step five: watch whether it worked
Once a new listing is live, we add it to Page Tracker and watch two things. Does the page start being cited in that city's answers? And does the client start appearing in the answers that cite it?
Citation Trends helps here. It classifies every citation by content type and source type and shows the citation rate over time, plus when a page was first cited, how many days it took to ramp up, its peak rate and its decay half-life. For a directory page, that tells us whether a listing is gaining ground or fading. We don't promise any particular timing to the client, because we don't know it in advance; we show what the data shows.
What we report
For each city, the client gets the top cited domains, which of them carry the client, which carry competitors, and what we did about each one. The same structure repeats per city, so it's easy to spot a source that matters everywhere versus one that matters in a single market. Our wider take on citation data is in citation analytics for AI answers.
If you're paying for directory listings and don't know whether any AI engine reads them, send us your city list at hello@1001seomedia.com and we'll start with the citation data.