Local Keyword Research: Find City and Map Terms

Local keyword research is the process of finding the words people use to look for a service near them, in map results, in city searches and in questions typed to AI assistants, and assigning each phrase to a page or listing that can win it. The main difference from general keyword research is that the place changes the results as much as the words do.

This page is for owners and marketers of service businesses with a defined area. It builds one list from three sources: map-pack queries, service-plus-city modifiers and AI prompt phrasing, then walks through a workflow that uses free tools. For the general method, start with keyword research for SEO. For the wider program around it, see the local SEO checklist.

On this page

• What counts as a local keyword?
• Which three keyword families should a local list contain?
• How do you check that a keyword is really local?
• A free-tool workflow for local keyword research
• How do you map local keywords to pages and your profile?
• Adding AI prompt phrasing to the list
• What local keyword volumes can and cannot tell you
• Where your local keyword research goes next

What counts as a local keyword?

A local keyword is any search where the right answer depends on where the searcher is or where they want something. Semrush’s local keyword research guide draws the main line between explicit and implicit terms. Explicit keywords name a location, such as “plumber boston”. Implicit keywords, such as “locksmith” or “plumber near me”, name none, and Google infers the location from the searcher.

The same guide lists more shapes worth knowing:

• City level: “plumber boston”, usually a city service page.
• Neighborhood level: “plumber south end boston”, often less competitive and a clear local signal.
• Urgency: “emergency plumber”.
• Attribute: “wheelchair-accessible dentist”.
• Availability: “dentist open saturday”.
• Landmark or ZIP code: “dentist near central park”.

Most generic guides stop at “add your city”. That misses half the demand, because implicit searches never contain it. They are won through your Business Profile and the way your site describes where you work, not through a keyword in a title tag. The profile side is covered in Google Business Profile optimization.

Which three keyword families should a local list contain?

A complete local list has three families, because three different systems answer local queries: the map pack, the organic results and AI assistants. The diagram below shows each family with an illustrative example.

Scroll the diagram sideways to see all of it.

Google describes local ranking with three factors: relevance, distance and prominence. Relevance is how well a profile matches the search. Distance is how far the business is from the searcher. Prominence is how well known it is, including links and reviews. Distance is the factor you cannot edit, so keyword work concentrates on relevance: making sure the words on your profile and pages match the words customers use.

How do you check that a keyword is really local?

Search the phrase in Google and look at what appears. A map pack of business listings is the clearest sign that Google treats the query as a hunt for a nearby business. Semrush’s guide makes the same split: transactional queries typically show a Local Pack, while informational ones typically show AI Overviews, featured snippets or People Also Ask.

When we ran a live check on the plain phrase “local keyword research” for this page, the results showed an AI Overview, a YouTube video and several guides, and no map pack. That is the informational pattern, and it is the right outcome: nobody is hiring a business by typing that phrase. Run the same test on “plumber boston” and you should see the opposite.

Two practical cautions:

• Search from the right place. Results shift with the searcher’s location, so a check from your office tells you little about a suburb 20 miles away. Use a location setting or a rank tracker that lets you choose the area.
• Keep the informational terms too. Phrases that trigger articles and People Also Ask boxes are not wasted. They suit guides, not location pages. The distinction is the one explained in search intent: match the page type to what the results show.

A free-tool workflow for local keyword research

You can build a solid list without paying for a tool. The sequence below follows the six-step process in Semrush’s guide, with free sources substituted at each stage.

Scroll the diagram sideways to see all of it.

Step 1: collect customer words. Semrush recommends starting from call logs, chats, reviews and community forums. These give you the phrasing real buyers use, which keyword tools lag behind. Write down problems as well as services: “water heater leaking” will not appear in a list of your service names.

Step 2: expand the list. Type each seed into Google and note the Autocomplete suggestions and the People Also Ask questions. Then open Google’s free Keyword Planner. Google’s help page on refining keywords in Keyword Planner says you can enter countries, regions and cities, and click “Nearby” for ideas in surrounding locations. Semrush notes the tool shows broad volume ranges, such as 100 to 1K, and supports city targeting but not neighborhoods or ZIP codes.

Step 3: check the results page. Use the test from the previous section to separate map-pack terms from informational ones.

Step 4: study competitors. Look at who holds the map pack for your best phrases. Note their primary categories, the words repeated in their reviews, and which of their pages target which city. You are looking for patterns to match, and for gaps: a neighborhood with no dedicated page, or a service nobody describes clearly.

Step 5: map each phrase to an asset. The next section covers this.

Step 6: test AI prompts. Covered in its own section below.

If you want a tool that does some of this for you, Local Falcon’s free local keyword research tool takes a seed keyword and a location and returns two lists: traditional search terms and AI-style phrases. Its page says there are no usage limits and no credits, though it appears to need an account. Treat its suggestions as candidates to check in steps 3 and 4, not as a finished list.

A ready-made version of the process is in the keyword research checklist.

How do you map local keywords to pages and your profile?

Assign each primary phrase to exactly one place, and put the right words in the right asset. Mapping stops two of your own pages competing for the same phrase and shows which phrases have no home yet. The matrix below shows the usual assignments.

Scroll the diagram sideways to see all of it.

The same mapping idea drives keyword clustering: group phrases that one page can satisfy, then give the group a URL. The grouped phrases in a local cluster often differ only by city or neighborhood, which is where the restraint matters. A location page needs something specific to say about that area. Ten near-identical pages with the city swapped are a thin-content risk, not a strategy. The structured-data side of a location page, the business details Google can read directly, is in local business schema.

Do not stuff keywords into the profile either. The business name field should be your real name, and the primary category should be the most specific accurate one. Everything else belongs in the services list and the description, written in plain sentences.

Adding AI prompt phrasing to the list

AI assistants receive longer, more specific questions than a search box does, so your list needs a short set of prompts alongside the compact terms. Semrush’s guide makes the same point and gives a representative example: a question asking for a 24-hour plumber in a specific neighborhood who can come that night.

Build the prompt set from the keyword list rather than from imagination:

1. Take your ten most valuable phrases.
2. Add the detail a real caller would add: urgency, neighborhood, budget, a requirement (“licensed”, “weekend”).
3. Write each as a full sentence a person would type or say.
4. Run each in ChatGPT, Gemini, Perplexity and Google AI Mode, from a logged-out session where possible, and record which businesses are named and which pages are cited.

Semrush describes what the answers weigh as details like availability, service area, pricing, licensing and recurring review themes, and notes that ranking in the top 10 improves the odds of being cited but does not guarantee it. It also reports that over 50% of desktop and 60% of mobile AI Overviews it studied did not link to the top organic result. Take the exact figures as one vendor’s study, but the direction matters: a top local ranking does not settle whether an assistant names you.

The fix is rarely a new keyword. It is usually a missing label. If you serve a neighborhood but no page or profile field says so, an assistant has nothing to connect you to the question. How assistants pick local businesses, and what feeds them, is covered in local AI visibility.

What local keyword volumes can and cannot tell you

Local volume figures are directional, so use them to rank your list, not to forecast leads. Three reasons stand behind that.

• Broad ranges. Semrush notes Keyword Planner shows ranges such as 100 to 1K, which is too coarse to separate two close phrases.
• Small samples. Google’s help page on Keyword Planner forecasts says results are less accurate when you target a small geographic location, because there is less data to base predictions on.
• No data for the new surface. Conversational AI prompts have no standard volume figure in the free tools, so your own testing replaces it.

A low-volume phrase can still be the best one on the list. “Emergency plumber south end boston” may get a handful of searches a month, but it comes from someone with a burst pipe. Rank by intent and fit first, volume second.

Where your local keyword research goes next

Once the list is mapped, the work moves from research to execution. Fix the profile fields first, then write or tighten the location and service pages, then repeat the AI prompt test monthly to see what changed. The phrase list is a working document: add the questions customers ask next quarter, and drop terms that never earn a call.

If you want this done for several cities at once, with the profile, pages and schema aligned to the same keyword map, our local SEO service covers it. If you are doing it yourself, the local SEO checklist and the keyword research checklist are the working documents to keep open.