TL;DR: Ranking in the local pack does not predict being recommended by an assistant. The largest study of this found ChatGPT recommended 1.2 percent of multi-location brand locations against 35.9 percent appearing in Google's local 3-pack, with only 45 percent overlap in retail between the brands winning each. Reviews and data accuracy are the levers, and the engines behave differently enough that they need separate approaches.
Key Takeaways
Local pack success does not carry over. SOCi's 2026 index found only 45 percent overlap in retail between brands leading traditional local search and those recommended by AI platforms.
AI local recommendation is far more selective. Roughly 30 times harder to earn than a local pack placement, on the same set of brands.
Reviews matter more here than anywhere else in this field. Locations ChatGPT recommended averaged 4.3 stars, and brands near 3.4 stars with response rates under 5 percent were effectively absent.
The engines diverge sharply. Gemini grounds local answers in Google Maps, so your Google Business Profile is largely your Gemini position. ChatGPT draws on wider web sources.
Profile accuracy is a live problem. The same study found business information roughly 68 percent accurate on ChatGPT and Perplexity, against 100 percent on Gemini.
Scale changes the method, not the principles. Tiered measurement, centralised data, devolved content, and a review programme that actually runs at every site.
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A note on the acronym
GEO is being used for two different things, and in multi-location marketing the confusion is costly.
Meaning | What it refers to |
|---|---|
Generative engine optimisation | Being cited and recommended by AI assistants |
Geographic or geo-targeting | Local SEO: listings, local packs, proximity |
A brief titled enterprise GEO strategy can mean either, and in practice often means the second while borrowing the language of the first.
Why it matters practically
The two have different owners, different metrics and different work. Local SEO is listings management, local packs and proximity. Generative engine optimisation is about whether an assistant names you in a composed answer.
For multi-location brands both apply, and the central finding of this page is that doing the first well does not deliver the second.
What this page covers
The overlap: how AI assistants handle local recommendations for brands with many locations, what actually moves it, and how the work differs from the local SEO programme you probably already run.
It does not cover local SEO fundamentals. Those are well documented elsewhere, they still matter, and they are necessary rather than sufficient.
For keyword purposes
If you are writing in this space, pick one meaning per page and be consistent. A page that slides between them will rank for neither well, because the two audiences want different things and neither gets a complete answer.
This page uses GEO to mean generative engine optimisation throughout, and says local SEO when it means local SEO.
What the data shows
There is one large study of this, and its central finding is uncomfortable for anyone who assumed local SEO carries over.
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What was measured
SOCi's 2026 Local Visibility Index analysed roughly 350,000 locations across 2,751 multi-location brands, with the average brand represented by about 67 locations. It measured how often each was surfaced by AI assistants and compared that against traditional local search performance.
It is the seventh year of the index and the first to include AI platforms, which makes it the best available evidence on this specific question.
The headline numbers
Finding | Figure |
|---|---|
Locations recommended by ChatGPT | 1.2% |
By Gemini | 11% |
By Perplexity | 7.4% |
Same brands in Google's local 3-pack | 35.9% |
Overlap in retail between local search leaders and AI-recommended brands | 45% |
Average rating of ChatGPT-recommended locations | 4.3 stars |
Profile accuracy on ChatGPT and Perplexity | ~68% |
Profile accuracy on Gemini | 100% |
How to read the selectivity
An AI local recommendation is roughly thirty times harder to earn than a local pack placement, measured across the same brands.
That is not a reason to despair, it is a reason to expect different numbers. If you are used to reporting local pack presence in the tens of percent, an AI recommendation rate in low single digits is not a failure of your programme, it is what this surface currently looks like.
The number that should worry you
The 45 percent overlap. Strong traditional local performance is roughly a coin flip for whether you also appear in AI recommendations, which means the work is genuinely separate rather than an extension.
That is the finding this whole page follows from.
Local pack success does not carry over
The most important practical finding, and the one that catches experienced local teams out.
The scenario
Fifty cities, solid local pack positions, grids mostly green. A regional manager asks an assistant for the best option in their city and your location, holding position two in the pack, is not in the answer.
That is not a bug in your local SEO. It is a different system reading different signals with different thresholds.
Why the two diverge
Local pack | AI recommendation |
|---|---|
Proximity is heavily weighted | Proximity matters less, and location may not even be set |
Returns a ranked list of several options | Composes an answer naming three to five |
Driven by Google's local index | Driven by a mix of sources, varying by assistant |
Rewards presence and relevance | Applies reputation and accuracy signals far more aggressively |
The study's own framing is that AI systems rely on the same fundamentals as local search, accurate data, reputation and engagement, but apply them far more aggressively.
What that means for your programme
Your local SEO work is necessary and insufficient. Nothing in it is wasted, and none of it guarantees the other surface.
The practical consequence is that the same fundamentals need doing to a higher standard than local pack performance required, particularly on reviews and data accuracy.
The concentration effect
Visibility clusters among a few leaders. In restaurants, the study found one chain reaching recommendation rates of 30 percent on ChatGPT and 45.8 percent on Gemini while most competitors barely registered, and 83 percent of restaurants did not appear in AI local recommendations at all.
That shape, a few brands taking most of the recommendations, is what thirty-times selectivity looks like in practice.
The implication for targets
Do not set AI recommendation targets by scaling down your local pack numbers. They are different distributions, and a rate that would be alarming in local search may be respectable here.
Measure your own baseline, compare against competitors measured the same way, and ignore any benchmark that does not state its method.
The engines diverge
In local, the assistants behave differently enough that one strategy does not serve all three. This is the clearest evidence of per-engine divergence anywhere in AI search.
What the differences look like
Engine | Recommendation rate | Profile accuracy | What it grounds in |
|---|---|---|---|
ChatGPT | 1.2% | ~68% | Wider web sources, review sites, discussion |
Perplexity | 7.4% | ~68% | Live web retrieval |
Gemini | 11% | 100% | Google Maps and Business Profile data |
What follows for each
Gemini is a Google Business Profile problem. It grounds local answers in Maps, which explains both the higher recommendation rate and the perfect accuracy. If your GBP data is right, Gemini has it right. Your existing local SEO work transfers here more directly than anywhere else.
ChatGPT is a wider web problem. It is the most selective by a wide margin and draws on sources beyond Google's local data: review platforms, publications, community discussion. Your GBP being immaculate does not settle it.
Perplexity sits between them, retrieving live and sharing ChatGPT's accuracy problem.
Why this matters for how you work
A blended figure across three assistants hides exactly the information you need. If your Gemini rate is healthy and your ChatGPT rate is near zero, that is a diagnosis: your Google data is fine and your wider web presence is not.
The reverse pattern, strong on ChatGPT and weak on Gemini, points straight at your Business Profile data.
Measuring per engine
Record and report each assistant separately, always. Combining them produces a number that describes none of them and conceals the only thing that tells you what to fix.
This is true generally in AI search and it is unusually stark in local, because the mechanisms genuinely differ rather than merely producing different numbers.
Where to start
If you have to pick one, start with the Gemini side, because the work is listings hygiene you probably already do and the payoff is measurable. Then take on the harder ChatGPT problem, which is reviews and wider presence and runs on a longer timescale.
Reviews
In local AI recommendations, reviews carry more weight than anything else you control. This is the clearest lever in the whole page.
What the data shows
Locations ChatGPT recommended averaged 4.3 stars. Brands sitting near 3.4 stars with review response rates below 5 percent were effectively invisible in AI recommendations.
The study also found the review signals that matter are broader than the star average alone: diversification across platforms, volume, freshness and response rates all contribute.
The five dimensions
Dimension | What to do |
|---|---|
Rating | The headline number. Below about 4 stars is a serious handicap |
Volume | Enough reviews per location to be credible, not just brand-wide |
Freshness | Recent reviews. A location with nothing for a year looks dormant |
Response rate | Respond to reviews. The sub-5-percent finding is stark |
Diversification | Not only Google. Presence across the platforms your category uses |
The response rate finding deserves attention
Response rate is the cheapest of the five to fix and the most neglected at scale. Responding to reviews is unglamorous work that nobody owns in most multi-location organisations, and the data suggests it separates visible brands from invisible ones.
If you do one thing after reading this page, build a review response process that actually runs at every location.
Doing it at scale, legitimately
Do | Do not |
|---|---|
Ask every customer at a natural moment | Offer incentives for reviews |
Template the response structure, personalise the content | Post identical responses everywhere |
Route responses to someone accountable per region | Leave it to individual site managers with no process |
Track response rate as an operational metric | Script what customers should say |
Incentivised reviews breach most platforms' terms, and FTC endorsement guidance requires disclosure of material connections between an endorser and a brand. The ask is fine; directing or paying for the content is not.
The honest limit
If a location has a genuine service problem, reviews will reflect it and no amount of response management fixes that. The reviews are telling you something true, and routing that to operations is the actual fix.
Data accuracy
A quieter problem than reviews and a measurable one. The study found business profile information roughly 68 percent accurate on ChatGPT and Perplexity, against 100 percent on Gemini.
What that means in practice
For roughly a third of locations, something an assistant says is wrong. Hours, address, phone, services, whether the site is open at all.
At one location that is an inconvenience. Across several hundred, it is a steady leak of customers who were told you closed at five when you close at nine, and who will never tell you that happened.
Why Gemini is accurate and the others are not
Gemini grounds local answers in Google Maps, so it inherits whatever is in your Business Profile. If that is right, Gemini is right.
ChatGPT and Perplexity assemble from wider sources, including directories and pages that may be stale, which is where the inaccuracy enters. That makes the fix different: not just your GBP, but the wider set of places your location data appears.
The audit worth running
Take a sample of twenty locations across regions. For each, ask three assistants for the opening hours, the address and the services offered. Record what comes back against what is true.
Twenty locations times three questions times three assistants is 180 checks, about half a day, and it will give you an accuracy rate you can act on and report.
The fix
Problem | Where to fix it |
|---|---|
Wrong on Gemini | Your Google Business Profile. Direct and quick |
Wrong on ChatGPT or Perplexity only | Directories, aggregators and your own location pages |
Wrong everywhere | Your own location pages first, then the major directories |
Hours wrong seasonally | Whatever process updates holiday hours. It is probably manual |
The seasonal trap
Holiday and seasonal hours are the most common inaccuracy and the least tracked. A brand that updates GBP for holidays but not its own location pages or the directories will be right on Gemini and wrong elsewhere, which is precisely the pattern the accuracy data suggests.
Listings at scale
The unglamorous foundation. At one location it is an afternoon; at six hundred it is a system.
What has to be true at every location
Element | Standard |
|---|---|
Ownership claimed | Every profile, every platform, no orphans |
Name, address, phone | Identical everywhere, including formatting |
Categories | Accurate primary and secondary, per location |
Hours | Current, including seasonal and holiday |
Services and products | Per location, not copied from head office |
Photos | Real, recent, location-specific |
Attributes | Accessibility, amenities, payment, whatever applies |
Why consistency matters more here
Contradictions across sources make a location harder to describe confidently. Where local search might tolerate a mismatched suite number, an assistant assembling from several sources has to pick one, and may pick the wrong one or hedge.
This is the same corroboration principle that applies to brands generally, applied per location and multiplied by however many you have.
The orphan problem
At scale there are always profiles nobody owns: a location that moved, a franchise that changed hands, a duplicate created by a well-meaning manager. Each is a source of contradictory data.
An audit for unclaimed and duplicate listings is tedious and it is usually where the worst inaccuracies live.
Tooling
Beyond about fifty locations, manual management stops working and a listings management platform becomes the practical option. That is a well-established category and this page has no particular view on which.
What matters is that bulk updates, duplicate detection and per-location overrides are all possible, because the last one is where most platforms disappoint.
The per-location override
The common failure is a system that makes everything consistent by making everything identical. Locations differ: services offered, hours, parking, accessibility.
A system that cannot express those differences produces consistent, confident, wrong data at the locations that differ from the template, which is worse than inconsistency.
Location pages
Most multi-location sites generate location pages from a template with the city name swapped in. Those pages are near-duplicates, and near-duplicates give a retrieval system nothing to prefer.
What a template page contains
The address, a map, the hours, a phone number and three paragraphs of corporate copy with the city name inserted. Several hundred of those are several hundred versions of the same page.
What makes a location page worth retrieving
Element | Why |
|---|---|
What is different about this location | The only genuinely unique content available |
Services available here specifically | Often varies, rarely stated |
Staff, named | Real people, not stock |
Local photographs | The actual premises, inside and out |
Parking, access, transport | Practical and frequently asked |
Reviews from this location | Specific and credible |
LocalBusiness schema, correct per location | Machine-readable, and often wrong at scale |
The question to answer per page
What would someone local ask about this specific site that the corporate page cannot answer? Parking, which services are offered here, whether there is a wait, what the building is like, who works there.
That is the content worth adding, and it is the content nobody has because it has to come from the location itself.
Getting it from the locations
A short structured request to each site: five questions, free text answers, ten minutes of a manager's time. What do customers most often ask you, what is different about this site, what should people know before coming.
Do that across a hundred locations and you have a hundred genuinely distinct pages, built from material head office could not have written.
The realistic approach at scale
You cannot do this for six hundred locations at once. Start with the twenty highest-revenue or most competitive markets, measure whether those pages change anything, and extend if they do.
That also gives you a controlled comparison: twenty enriched pages against the templated remainder is the closest thing to a test you will get.
Centralise or devolve
The organisational question underneath all of this, and getting it wrong is why multi-location programmes stall.
Centralise | Devolve |
|---|---|
Listings data and ownership | What is distinctive about this location |
NAP format and brand facts | Review responses, with templates and oversight |
Schema implementation | Local photographs |
Measurement and reporting | Answers to what customers here ask |
Platform relationships and tooling | Local events and community involvement |
Policy on incentives and disclosure | Nothing about compliance |
The principle
Centralise anything where consistency is the point. Devolve anything where local knowledge is the point.
Most organisations get this backwards in one specific way: they devolve listings management, which produces inconsistency, and centralise content, which produces templates. Both failures are the reverse of what works.
Review responses specifically
The hard case. Fully centralised responses read as generic and risk being wrong about what happened. Fully devolved means they do not happen at all at most sites.
The middle: a central process that ensures responses happen and tracks response rate, with the content coming from or verified by someone who knows the location. That is more work than either extreme and it is what the data suggests is worth it.
What local teams need to make this work
Need | Why |
|---|---|
A clear, short request | Ten minutes, not a project |
A reason to care | Show them their own location's visibility |
Templates for responses | So the ask is editing, not writing |
Someone accountable regionally | Otherwise it decays in a quarter |
The decay problem
Every multi-location content initiative decays. Location pages get written once and never updated, review responses stop after the launch push.
Build for the second year: a quarterly refresh request rather than a one-off campaign, and a response rate metric that someone is accountable for. A programme that only works when someone is enthusiastic is not a programme.
Measuring across locations
You cannot run a full prompt set per location. Six hundred sites times twenty prompts times three assistants times five runs is not a measurement programme, it is a budget.
Tier instead
Tier | Locations | Coverage |
|---|---|---|
Core | 15 to 25 markets, chosen by revenue and competitiveness | Full prompt set, five runs, three assistants, monthly |
Sample | A rotating sample across regions and formats | Reduced set, quarterly |
Accuracy sweep | Everything, eventually | Three factual questions, rotating, quarterly |
The accuracy sweep is the one that scales, because three questions per location is cheap and inaccuracy is the problem most likely to be costing you customers silently.
Choosing the core markets
Not just the biggest. A mix of your strongest and weakest performers, competitive and uncompetitive categories, and at least one of each format you operate.
That gives you a sample you can reason from rather than a list of your best sites, which would flatter the numbers and teach you nothing.
What to record
Field | Why |
|---|---|
Recommended, per assistant | Separately, always |
Which competitors appeared | The only valid comparison |
Whether the detail given was accurate | The accuracy rate |
Which sources were cited | Where to work |
The location, the city, the format | So you can segment |
The segmentation that pays
Compare your strong markets against your weak ones on the inputs: rating, review volume, response rate, data accuracy, page quality.
With twenty core markets you have enough to see which inputs track with visibility in your own estate. That is better evidence for your business than any published study, including the one this page rests on.
Reporting it
By tier and by assistant, never blended into one organisational figure. A single company-wide AI visibility number for a multi-location brand averages across markets with completely different competitive conditions and tells you nothing about any of them.
Six mistakes
1. Assuming local pack performance carries over. Only 45 percent overlap in retail between brands leading traditional local search and those AI platforms recommend. Your local SEO is necessary and not sufficient.
2. Blending the assistants into one number. Gemini grounds in Google Maps and was 100 percent accurate; ChatGPT draws on wider sources and was around 68 percent. A blended figure conceals which problem you have.
3. Neglecting review response rate. Brands near 3.4 stars with response rates under 5 percent were effectively invisible. It is the cheapest of the five review dimensions to fix and the most often unowned.
4. Templated location pages. Several hundred near-duplicates give retrieval nothing to prefer. The distinctive content has to come from the locations themselves, which is why nobody has it.
5. Consistency by uniformity. A listings system that makes everything identical produces confident wrong data wherever a location genuinely differs. Per-location overrides matter.
6. Trying to measure every location. Tier it: full measurement in fifteen to twenty-five core markets, a rotating sample elsewhere, and a cheap three-question accuracy sweep across everything.
The pattern
Most of these come from applying a local SEO mental model to a surface that applies the same fundamentals far more aggressively. The inputs are familiar; the thresholds are not.
The one to start with
Review response rate, because it is operational rather than technical, it is measurable this week, and the data points at it more directly than anything else here.
The accuracy sweep is the close second. Three questions across a sample of locations will tell you how often assistants are wrong about you, and roughly a third is the number to expect.
FAQs
Does ranking in the local pack mean AI assistants will recommend us?
No. SOCi's 2026 Local Visibility Index found only 45 percent overlap in retail between brands leading traditional local search and those AI platforms recommended. The two surfaces read similar signals but apply them far more aggressively.
How often do AI assistants recommend multi-location brands?
Across roughly 350,000 locations, ChatGPT recommended 1.2 percent, Gemini 11 percent and Perplexity 7.4 percent, against 35.9 percent appearing in Google's local 3-pack. An AI local recommendation is roughly thirty times harder to earn.
Why do the assistants give such different results?
Gemini grounds local answers in Google Maps, which is why its recommendation rate is higher and its profile accuracy was 100 percent. ChatGPT and Perplexity assemble from wider web sources and showed around 68 percent accuracy.
What matters most for local AI visibility?
Reviews, across five dimensions: rating, volume, freshness, response rate and platform diversification. Locations ChatGPT recommended averaged 4.3 stars, and brands near 3.4 stars with response rates below 5 percent were effectively absent.
How accurate is the information assistants give about our locations?
Expect roughly a third to be wrong on ChatGPT and Perplexity, based on the study's finding of about 68 percent accuracy. Gemini was accurate because it reads your Google Business Profile directly.
Do location pages need unique content?
Yes. Templated pages with the city name swapped in are near-duplicates that give retrieval nothing to prefer. The distinctive material, what differs here, which services, local staff and photographs, has to come from the locations themselves.
How do we measure visibility across hundreds of locations?
Tier it. Full measurement in fifteen to twenty-five core markets chosen for a mix of strong and weak performance, a rotating reduced sample elsewhere, and a cheap three-question accuracy sweep across everything quarterly.
Should we centralise or devolve this work?
Centralise anything where consistency is the point: listings data, NAP format, schema, measurement. Devolve anything where local knowledge is the point: what is distinctive here, local photographs, the content of review responses.
Where to start
Run the accuracy sweep. Twenty locations across regions, three factual questions each, three assistants. Half a day, and it gives you an accuracy rate you can report and a list of specific wrong facts to fix.
Then look at review response rate across your estate. If it is under 5 percent at most locations, that is the finding, and it is operational rather than technical.
The order of work
Accuracy sweep, then fix what it finds, starting with your Google Business Profile for the Gemini side.
Review response process that actually runs at every location, with someone accountable regionally.
Listings audit for orphans, duplicates and locations that differ from the template.
Location pages for the top twenty markets, built from what those sites tell you.
Tiered measurement, core markets monthly, sample quarterly, accuracy sweep quarterly.
If location page content is the constraint
Twenty genuinely distinct location pages, built from material each site supplies, is a real production job. Blazly SEO covers research, planning, drafting and direct publishing from about $39 a month.
The local material still has to come from the locations, and the listings and review work sits with operations rather than content.
Summary
For multi-location brands, ranking in the local pack does not predict being recommended by an assistant: the largest study found only 45 percent overlap in retail, and AI local recommendation is roughly thirty times more selective. The engines diverge sharply, with Gemini grounding in Google Maps and showing perfect profile accuracy while ChatGPT draws on wider sources and was accurate about two thirds of the time, so measure and treat them separately. Reviews are the strongest lever, across rating, volume, freshness, response rate and platform diversification, with response rate the cheapest to fix and the most often unowned. Expect assistants to be wrong about roughly a third of your locations, and run a cheap recurring sweep to find out where. Centralise the data, devolve the local knowledge, and tier your measurement rather than attempting every site.