You ask ChatGPT for the best option in one of your cities, and a smaller competitor with three reviews gets named instead of you. That stings. This playbook is for marketing leads at multi-location and local brands who need to fix the entity, listing, and review signals behind AI near me recommendations, and fix them the same way across every location. By the end you will have a seven-step sequence you can run per city, plus a way to tell whether it worked.
Here is the good news before we start: almost none of this is content strategy. Most of it is data hygiene you already half-own.
Why your locations get skipped
Google's own local ranking model rests on three pillars: relevance, distance, and prominence. "Best in [city]" quietly adds a fourth, quality, read from review volume, review recency, owner responses, brand mentions, and how confident the data layer is that your location is a real, single, findable entity.
Generative engines do not read that model the way Google does. They read the open web. ChatGPT routes location queries through Bing, so it surfaces Bing-indexed pages, Apple Maps data, and platforms like Yelp and TripAdvisor. Perplexity visibly cites Google Maps data, Apple Maps data, Yelp, TripAdvisor, and editorial listicles. Gemini leans on Google's index. Claude leans on the public web and is the least local-pack aware of the group.
That is the whole problem in one line: best in city AI answers get assembled from listings, reviews, and per-location pages, not from your local pack position. Industry studies suggest AI Overviews now appear on a clear majority of local queries, more often than the classic local pack shows up at all. So ranking in Maps and being invisible in AI answers is not a contradiction. It is the normal state right now.
Multi-location brands lose for a handful of fixable reasons. One website with one address. Programmatic city pages that swap a name and nothing else. Name, address, and phone data drifting across aggregators. Reviews piled onto Google and nowhere else. Homepage schema that declares one generic Organization and no per-city entity underneath it.
For an engine to recommend a location, four things have to be true. It has to find you, trust you, place you in a city, and see you as a top option there. The steps below work those four in order.
Step 1: Build one source of truth for every location
Start here even if it feels unglamorous. Every fix downstream reads from this record.
Stand up one canonical file per location holding the legal business name, the brand-display name on the sign, the street address in full USPS format with no abbreviations, a local phone number with the city's area code, regular and holiday hours, primary and secondary categories, the per-location page URL, the service area, a public email, latitude and longitude, the opening date, the manager's name, accessibility attributes, and real photos of the logo, exterior, interior, and staff at work.
How to tell it is done. One signed-off spreadsheet per location lives in a shared folder with version history, and every downstream system reads from it: your CMS, your Google Business Profile dashboard, your listing tool, your schema generator. A monthly diff flags any field that has drifted.
Where people go wrong. They let store managers edit hours and categories directly. They write "Street" in one place and "St." in another. They put a call-tracking number on the website and a different local number on the profile. Phone drift alone is enough to soften the entity signal that AI engines depend on.
If you only do one thing this week, do this one. It takes an afternoon per ten locations and it makes the next six steps mechanical.
Step 2: Claim and verify every listing AI engines read
Claim the profiles first, optimize them second. An unclaimed listing is a guess the platform is making about you, and those guesses are the raw material behind AI near me recommendations.
Tier 1, every location, no exceptions:
- Google Business Profile, one per physical location
- Apple Business Connect, which powers Apple Maps, Siri, Spotlight, and Wallet
- Bing Places for Business, which powers Bing Maps and the Copilot surfaces
- Yelp, claimed with the free owner account
- A Facebook Page with the location set
Tier 2 is your vertical. Legal brands need Avvo, Lawyers.com, FindLaw, and Justia. Medical needs Healthgrades, ZocDoc, Vitals, and RateMDs. Hospitality needs Tripadvisor, Booking.com, and Expedia. Restaurants need Tripadvisor, OpenTable, Resy, and Yelp. Home services need Angi, HomeAdvisor, Houzz, and Thumbtack. Auto needs Cars.com, DealerRater, Edmunds, and KBB. Real estate needs Zillow, Realtor.com, and Homes.com.
Tier 3 compounds quietly: Better Business Bureau, Yellow Pages, Superpages, Citysearch, your city and state Chamber of Commerce, Foursquare, MapQuest, Here, and TomTom.
How to tell it is done. Every location has all of Tier 1 claimed and verified, the Tier 2 set for your vertical claimed and verified, and at least 80 percent of Tier 3 consistent.
Common mistake: skipping Bing Places because "our customers don't use Bing," and skipping Apple Business Connect because "we don't care about iPhone." Neither is really about search share. Bing Places is the data source behind Microsoft's Copilot surfaces, and Apple Maps is the default map on every iPhone, feeding Siri, Spotlight, Mail, and Wallet. Skip those two and you have handed away two of the four surfaces that matter.
If you run ten or more locations, use bulk location management rather than clicking through profiles one at a time.
Step 3: Push your data through the aggregators
Four US data aggregators feed the long tail of directories, voice assistants, in-car navigation, and mapping apps: Neustar Localeze, Foursquare, Data Axle, and Factual, which has since merged into Foursquare but still appears in most listing playbooks. Submit to each, or use a listing-management tool that genuinely pushes to all four.
Foursquare deserves special attention. It feeds Apple Maps point-of-interest data, so a bad record there ripples straight into iOS.
How to tell it is done. You hold a confirmation record per submission per location, and a scan tool reports above 85 percent consistency across the top thirty directories.
Where people go wrong. Submitting once and never re-syncing after a move or a rebrand. Using one set of categories at the aggregator and a different set on Google. Assuming a tool covers all four aggregators without ever checking the documentation.
This step is boring and it is the one that most often unlocks the rest. Consistent data is what turns "some plumbing company" into a specific entity an engine will name.
Step 4: Give every location its own page and its own schema
To get local business cited by AI, an engine needs a page it can point at. That means a unique, crawlable, indexable URL per location. A single "our locations" page gives an engine nothing per-city to cite.
Build each page in this order:
- An H1 with the city and the primary service. "Emergency plumbing in Tampa," not "Welcome to our Tampa location."
- An above-the-fold block with address, click-to-call phone, hours, and a directions link, visible without scrolling on mobile.
- An embedded map of that location.
- A hero image or short video of the actual place, not stock photography.
- Two to four paragraphs of genuinely local copy: neighborhoods, landmarks, who the customers are, what the work looks like there.
- Location-specific FAQs.
- A services list, with starting-at pricing where you are allowed to publish it.
- Local proof: neighborhood reviews, before and after photos, staff photos, testimonials with a first name and a neighborhood.
- LocalBusiness JSON-LD schema.
- Internal links to nearby sister locations and to your brand-level pages.
On the schema, use the most specific type that applies. Schema.org publishes Restaurant, Store, Hotel, MedicalClinic, Dentist, Attorney, RealEstateAgent, AutoRepair, and more, and the specific type declares the page far more clearly than the generic LocalBusiness parent. Include name, image, url, telephone, the full postal address, geo coordinates, opening hours, areaServed, sameAs links to that location's social profiles, and a parentOrganization pointing back to your brand. That parentOrganization line is what binds a per-city entity to the brand entity instead of leaving them as strangers.
Add BreadcrumbList markup, Service markup for major services, and FAQPage markup on the FAQs. Google stopped showing FAQ rich results in May 2026, so do not promise anyone a SERP feature. The markup still parses, and the question-and-answer content is still exactly the shape AI engines like to lift.
How to tell it is done. Every page passes Google's Rich Results Test with no errors, the schema validator agrees, and the page is indexed in Search Console.
Where people go wrong. City-swap doorway pages, which violate Google's spam policy and get ignored by engines anyway. Schema that describes things the page does not show. Review markup with no visible reviews on the page, which breaks Google's structured data policy. Pages under roughly two hundred words, which are simply too thin to rank.
Aim for more than half of each page to be unique against its sister pages. Here is a quick test that beats any word count: remove your brand name from the page. Is it still useful to someone in that city? If yes, you have a real location page. If no, you have a doorway.
Step 5: Earn reviews on the platforms that actually feed answers
Reviews are the quality signal behind "best in," and where they live matters as much as how many you have.
Google carries the most weight for near me queries and Google's AI surfaces. Yelp is heavily weighted for restaurants, beauty, home services, and medical, and it is one of the most-cited sources behind best in city AI answers. Apple Maps does not generate its own ratings; the ratings you see there come from partner data, primarily Yelp and TripAdvisor. Earning Yelp reviews improves two surfaces at once. Then there is your vertical platform: Healthgrades, Avvo, Cars.com, Tripadvisor, OpenTable, ZocDoc, or Houzz.
Cadence beats bursts. Aim for one to three new Google reviews per week per location, plus a steady trickle on Yelp and your vertical platform. Fifty in a week and then silence reads as manipulation to algorithms and models alike. The 2026 Whitespark Local Search Ranking Factors survey puts review recency at the top of the weighted list, with reviews from the last thirty days carrying the most weight, so a location with forty stale reviews can lose to one with twelve fresh ones.
Four things raise the quality of what you earn:
- Respond to at least 80 percent of reviews, positive and negative. One 2026 analysis found businesses responding to three quarters or more of their reviews sat noticeably higher in the local pack on average.
- Encourage detail. A review naming the service, the staff member, and the outcome carries more signal than "Great!"
- Welcome reviews that mention the city and the job, because that is the language an engine matches on.
- Ask for photos.
The compliance rules are short and absolute. Never gate a review behind a discount. Never buy reviews. Never solicit only the happy customers. Never post on a customer's behalf. Each of these can get a profile filtered or suspended, and a suspension costs more than a year of review building.
How to tell it is done. Each location holds thirty or more recent Google reviews at 4.5 or above, has a real presence on Yelp and one vertical platform, and has a documented owner-response workflow with a named owner.
Which cities do you push first? That is where measurement earns its keep. DeepSmith tracks mention rate and citation rate per prompt across ChatGPT, Perplexity, Gemini, Claude, and Google AI Mode, with engine coverage rising by plan tier, so you can see which cities are losing recommendation prompts to which competitors and aim your review velocity at those markets instead of spreading it evenly.
Step 6: Measure local business AI visibility across all three layers
You cannot fix what you are not watching, and one dashboard will not show you all of it. Track three layers in parallel.
Layer A, classic local. Top-three position for "[service] near me" and "[service] in [city]" per location, plus profile insights on discovery searches, calls, direction requests, and website clicks.
Layer B, AI answers. Run a fixed prompt set per location across ChatGPT, Perplexity, Gemini, Claude, and Google AI Mode. For each run, log whether you were mentioned, whether you were cited with a link, which URL was cited, the sentiment, and where you sat among the competitors listed. Refresh weekly.
Layer C, page-level traffic. Every location URL should be earning impressions and clicks in Search Console and Bing Webmaster Tools. Map that data to location URLs, not to the root domain.
Layer B is the one teams skip, usually because doing it by hand across five engines and twenty cities is a week of somebody's month. This is the work DeepSmith was built for: it checks your tracked prompts on a schedule and reports mention rate, citation rate, share of voice, and which competitor pages are winning the prompts you are losing. Local business AI visibility stops being a vibe and becomes a number you can move.
Where people go wrong. Tracking only Maps rank. Treating AI as a black box. Setting no baseline, so no change can be proven. Comparing a Perplexity number to a ChatGPT number as if they measure the same thing. Perplexity is citation-first and shows its sources. ChatGPT will often name a brand without linking to anyone. A mention and a citation are different wins.
Step 7: Make multi-location AEO a quarterly habit
Local data rots. Managers change hours, a location moves suites, a directory rebuilds its database. Multi-location AEO is a maintenance discipline, not a project with an end date.
Run this per location, every quarter:
- Verify name, address, and phone on Google, Apple, Bing, Yelp, and at least three Tier 2 directories.
- Verify hours, including special hours for the holidays ahead.
- Audit categories, attributes, and services on every platform.
- Read the last thirty days of reviews: count, average, response rate, sentiment shift.
- Re-run the page through the Rich Results Test and the schema validator.
- Check indexability and crawl status in Search Console.
- Refresh the unique content: recent projects, seasonal FAQs, neighborhood notes.
- Confirm every sameAs link still resolves.
How to tell it is done. A signed-off review per location with findings and next-quarter actions, owned by a person, not a team.
The refresh in item seven is where most quarterly audits quietly die, because writing fresh local copy for forty pages is a real content project. That is the other half of what DeepSmith does. Content Studio turns each location's gaps into publish-ready articles and pages grounded in your stored brand context, Autowrite generates them on their scheduled dates without anyone opening the app, and Repurpose turns a published page into LinkedIn posts and newsletter sections so local marketing does not fall off the end of the list.
It will not guarantee you a citation. Nothing does. What it does is make the produce-and-measure loop small enough that you actually run it every quarter.
What to do next
Pick one city. Not all of them. Take your weakest market, run steps one through four for that single location, and give it a full quarter of review velocity.
Then compare it against a market you did not touch. That comparison is your business case for doing the other thirty, and it is far more convincing to a leadership team than any framework slide.
If you want the measurement and production halves handled together, start a free DeepSmith trial and track your local prompts for a week before you commit to anything. Seven days is enough to see which cities are actually losing.
You are closer than this article makes it look. Most brands are one clean data sheet and one honest location page away from being findable.
