AI search visibility for a multi-location brand is two measurements, not one. "Best physio in Bondi" is a location question, answered from what the web and Google Maps hold about one address. "Is Coastline Physio any good" is a brand question, answered from what they hold about the whole network. A brand can pass the second at every assistant and fail the first at forty of its sites.

For a network of 20 to 400 sites, use how AI search decides which local businesses get named as the starting point. At one location the check is ten minutes a month and none of the sampling below applies.

The category itself does not make the distinction. Search "ai search visibility" today and page one is trackers: Ubersuggest, Rankscale, Semrush's free checker, a couple of scoring tools. They define the measurement the same way, as the share of AI answers that name your brand, and they all track a brand. Not one of them has a row for an address. That is a reasonable unit for a software company with one website. It is the wrong unit for a business with forty front doors.

The brand answer and the location answer are different questions

Marketing leads find this out the same way every time. Somebody asks ChatGPT about the brand, gets a warm paragraph back, and concludes the network is fine. Then a franchisee three train stops away asks for the category in their own postcode and the brand is nowhere. Both results are correct. The assistant answered two different questions out of two different piles of material.

The brand question retrieves what the web says about the name: your website, your press, roundups, aggregator pages, and the reviews of whichever sites happen to be documented well enough to surface. One answer for the whole network, mostly built out of things head office controls.

The location question retrieves what the web says about one suburb. Google defines query fan-out as "a set of concurrent, related queries generated by the model to request more information and fetch additional relevant search results to address the user's query", and for a local question those concurrent queries are about hours, parking, a health fund, whether you see children. Almost none of that lives on the brand website. It lives on one location's Business Profile, in one location's reviews, and on the listings for one address.

Gemini makes the split literal. Google's Grounding with Google Maps documentation describes a retrieval step where the service "queries Google Maps for relevant information (e.g., places, reviews, photos, addresses, opening hours)", and the tool call in Google's own sample takes a latitude and a longitude.

Maps is organised by address. There is no network row in it.

Two ways a network fails, and only one of them is obvious

A weak site drags only itself, until the brand answer picks it up

In the location answer, a weak site drags only itself. Nothing about a thin profile in Parramatta stops the Bondi site being named in Bondi.

The brand answer does not work that way. It is assembled from material about the name, and nothing makes the model prefer your best-documented site over your worst. A location carrying a disconnected phone number, an old address, a "permanently closed" flag or a 3.1 rating is part of what an assistant reads when somebody asks whether the brand is any good. One site can put a sentence into the brand answer that is true of that site and false of the other forty.

Nobody measures this, because head office only ever asks the brand question and it usually comes back clean. It comes back clean until the day retrieval happens to pull the wrong site. When AI gets your business details wrong covers tracing a wrong detail back to whatever is carrying it.

The brand is named, but the wrong branch is

The second failure is quieter and costs bookings the same week. A customer in Newtown asks for the category near them, the assistant names your brand, and it names the Bondi site because that is the address the web documents best. The customer rings a clinic forty minutes away, or more often does nothing.

No brand-level check shows you this, because the brand was named. It surfaces only when the check runs in the suburb the customer is standing in, which is the whole argument for prompting per location. The cause is nearly always the same: the suburban profile is thin, so retrieval falls back to the site with enough material to retrieve.

What gets read per location, and what gets read once for the brand

Sort the inputs by whether the thing exists once or once per site. That decides who fixes it, and how many times.

Most of what decides a location answer is not centrally editable. Head office can mandate it, fund it and chase it, but the material sits on 200 profiles and in 200 review corpora. That makes it a governance problem, which is where the multi-location review management playbook arrives from the reviews side.

The cheapest wins are structural rather than editorial. A duplicate profile splits one location's reviews across two cards and halves the rating a customer sees, which is a retrieval problem as much as a reputation one. Multiple locations with the same name covers clearing a duplicate, and getting a location onto Google Maps properly covers the profile. Neither is glamorous and both outrank anything you could write about AI.

How to check AI search visibility across 200 locations without a full-time job

Two hundred sites, eight prompts and three assistants is 4,800 runs a month. Nobody does that by hand, and a network that tries will do it once and never again. Do not measure everything every month. Run two tracks instead.

Track one: a fixed panel, every month. Pick ten sites and do not change them. Take them from the top of whatever you already rank on, revenue or review volume, and add one or two you know are weak so the panel is not flattering. Run the eight prompts from how to monitor AI search visibility against each, same day each month, signed out, one fresh chat per prompt, suburb written into the sentence.

Ten sites, eight prompts and three assistants is 240 runs, which is about a day for one person once a month. If that is too much, cut the assistants before you cut the sites. One assistant across ten sites tells you more than three assistants across three.

Track two: every site once a quarter, on rotation. Two hundred sites over three months is roughly 67 a month, or about 17 a week. Give each site two prompts and one assistant: the plain discovery prompt for its category and suburb, and one constrained prompt built from a filter your customers apply, such as opening on a Sunday, taking a particular health fund, or seeing children. That is 34 runs a week, which is an hour.

Do not spend rotation runs on the named prompts. "Is {brand} any good" is a brand question and the panel answers it already.

Fix the sentences once and change only the values inside them. The moment somebody rewrites a prompt, the quarter stops comparing to the last one.

Use one sheet with a location column. Date, location, assistant, prompt, named or not, rivals named, sources cited, facts correct. The location column turns 200 separate checks into a network measurement, and it is the column people forget when they set this up in a hurry.

If running it by hand does not fit, Cloutly's AI search visibility asks ChatGPT, Gemini and Perplexity discovery questions such as "best {category} in {locality}" once a month and records whether each location was named and which rivals were.

How to read the results

Three things come out of the sheet, and only three are worth an executive's attention.

Named or not named, per site. Treat it as a binary and ignore where in the list you appeared. The ordinal is one sample of a system that is not deterministic, so a site that was second in September and fourth in October has told you nothing. Named in September and absent in October has told you something worth checking.

Which rivals were named. The most useful column, and the one people skip. The same rival in every suburb is a brand-level competitor whose brand-side material is better than yours, and that is a head-office job. A different rival in each suburb is normal, and it means the fight is local and the work goes to the sites.

Whether the facts came back right. A wrong phone number in an answer is worse than not being named, because the customer tried. Log it separately from the naming result and fix it at whatever source carries it, rather than by complaining to the assistant.

Report two network numbers, both carrying their denominator: how many of the sites you sampled were named by at least one assistant, and how many had a fact wrong. "31 of 67 sites named this quarter, 4 with a wrong phone number" survives being questioned. Report movement as flips rather than as a percentage. "Three sites started being named, one stopped" is defensible on one sample per site. "Share of voice up 2.4 points" is not, and somebody in the room will eventually ask what the denominator was.

The count tells you the size of the problem and nothing about where it is. At 200 locations the same proportion can mean one weak region or a thin spread everywhere, so put the count on the slide and the list of sites that missed in the appendix. Only the list can be worked.

What to do with a location that is never named

A site that misses once is noise. A site that misses in three consecutive quarters is a work list, and it is usually the same list in the same order.

You can only work 200 profiles if you can reach 200 profiles. Google's business groups are the mechanism, and the trade-off is written down: one group gives you every profile in one dashboard and one spreadsheet, several groups keep each region's people out of profiles they should not be editing and cost you the single view.

1. Check it has a profile, and only one. A duplicate splits the reviews and the rating across two cards, so neither card is strong enough to be retrieved. Clear it before anything else.
2. Finish the profile. Primary category first, then hours, the description, photos, every remaining field. One hour of work that changes more than the rest of the list combined.
3. Read the constrained prompt result. If "physio in {suburb} that bulk bills" names three rivals and not you, and you do bulk bill, the problem is not the assistant. Nothing public says you do.
4. Read the citation list. If the same suburb roundup or directory is cited every time and the site is not in it, that is a job with a name and an owner.
5. Get the site asking for reviews the same way as the rest of the network. A site running on a manager's memory produces a corpus nobody can match a question against. The trigger and the timing are set centrally, the wording stays local, and how to get more Google reviews covers the ask itself.
6. Give it two quarters. Nothing on this list moves an answer in a fortnight, and a site panicked over weekly gets worse rather than better.

One thing not to do, and it comes up at every network this size: do not filter who gets asked. Routing happy customers to the review sites and unhappy ones to a private form breaches Google's contributed-content policy, which says merchants must not "discourage or prohibit negative reviews, or selectively solicit positive reviews from customers". It also produces a corpus that does not describe the location, which is the last thing you want when a machine is reading that text back to a customer. Cloutly has not had a rating step in front of the review sites since 2021.

Reporting AI search visibility to the board

Two sources, measuring different things. Say which is which on the slide.

Google's own surfaces have a first-party report. Search Console's generative AI performance report covers AI Overviews and AI Mode, and as of 31 August 2026 Google has rolled it out to all websites worldwide. Read it for what it is. It reports impressions, defined as "how many times links to your site were shown to a user in a generative AI feature on Google Search", and nothing else. No clicks, no position. You can group by pages, countries, dates or devices.

Two things about that report bite at network scale. The usual Search Console limits apply, the 1,000-row limit included, so a 400-site network with a page per location will not see every location in one view of the page table. And nothing outside Google Search appears in it at all, so it says nothing about ChatGPT, the Gemini app or Perplexity.

Everything else is your sweep, and a sweep is a sample. Report it as the counts above, each with its denominator.

Put the caveat on the slide rather than in the speaker notes. These results are a point-in-time sample of a non-deterministic system. One sample each, and a re-ask can differ. Say that up front and the first quarter's number stays a reading instead of becoming a target.

Google adds a warning that belongs on the same slide, because it applies to every vendor in this category including us: "Be wary of third-party tools that promise ranking success or claim to use 'internal' Google metrics."

What not to put on the slide: a share-of-voice percentage with no stated denominator, an average across the network, and any number presented as a rank.

Frequently asked questions

Should we check AI search visibility by brand or by location?

Both, and separately. The brand check answers "what does an assistant say about us", which is one answer for the whole network. The location check answers "does an assistant name this site when somebody in this suburb asks", which is a different answer at every address. Networks that run only the brand check are usually measuring their best-documented site by accident.

Can one bad location affect how AI describes the whole brand?

In the location answer, no. A thin profile in one suburb costs that suburb and nothing else. In the brand answer, yes: a site carrying a wrong phone number, an old address or a much lower rating is part of the material an assistant reads when somebody asks about the brand by name, and nothing makes the model prefer your strongest site.

Is AI search visibility the same as our local ranking?

No. Local ranking is one of the inputs an assistant reads, not the output it produces, and an assistant can name a site that is nowhere near the map pack for the same query. Track both, separately, and do not let a rankings report stand in for the sweep.

Multi-location review management is the job of getting every site in a network asking for reviews the same way, landing every review in one place, and deciding who answers each one. It is a governance problem before it is a software problem. The brand carries one rating in the customer's head, and fifty owners decide what that rating is.

Written for whoever owns that number across 20 to 400 sites. At one site, most of this is overkill.

What breaks as a network grows

Every site asks differently. One franchisee has a QR code on the counter, another blasts a monthly email out of their own Mailchimp, eleven do nothing, and six point customers at a Facebook page nobody has claimed. Together that produces a rating swinging between 4.1 and 4.9 for reasons unrelated to the service.

The reviews land nowhere central. Each Google Business Profile notifies whoever set it up: a franchisee's personal Gmail, an agency, a person who left in 2023. Head office finds out about a bad quarter at conference, because no list of last week's reviews exists.

And head office replies to everything or to nothing. Everything means a coordinator writing "We're sorry to hear this, please contact support@" under a review about a specific Tuesday, which reads worse than silence. Nothing leaves the silent sites silent.

Underneath all three sits the same fact: a customer does not distinguish between franchisees. They searched your brand, Google showed them the nearest site, and that site's rating is the brand's rating as far as they are concerned. So the network average is a vanity number. A network averaging 4.5 with nine sites at 3.6 is in worse shape than one averaging 4.3 with nothing below 4.1.

Claim and organise every profile before anything else

The unglamorous part, and the one every competing guide skips. Nothing works until you can reach every profile.

1. Inventory what exists. Every location, its Google Business Profile, its Facebook page, and who holds owner access to each. Expect duplicates, and at least one profile still owned by an agency the franchisee stopped paying in 2024.
2. One profile per location. Google's guidelines for representing your business say there should only be one profile per business, and that locations of a chain in the same country use the same name unless a site consistently trades under a different one. Duplicates split your reviews across two cards and halve the rating that shows. More on multiple locations with the same name.
3. Put the profiles in a business group. Google describes business groups as a more secure way to share access, a shared folder for your profiles: add a colleague as a manager or owner and they reach every profile in the group, current and future, without anyone handing over a password. Almost nobody sets this up.
4. Decide the access model, and write it into the franchise agreement. Owner and manager roles differ in one way that matters: managers can do nearly everything, but only owners can add or remove users and delete the profile. Head office should be an owner on every profile, the franchisee a manager or co-owner. A profile has one primary owner, and that owner cannot step away until they have transferred the role. Settle that now rather than on the day a franchisee sells.

If some sites are not on Google properly yet, start with getting a business onto Google Maps.

Get every location asking the same way

The corporate instinct is a standard: same message, same timing, everywhere. The franchisee instinct is autonomy: my customers, my words. Standardise the trigger and leave a little of the wording local.

The ask fires off whatever system that site already runs on, at the moment it says the job is done: the booking system when the appointment is marked complete, the point of sale when the invoice is paid. If it depends on someone remembering, it happens on quiet days and stops on busy ones.

Then leave the local half local: the location name, the customer's name where you have it, and one line the franchisee writes themselves. Timing, follow-up spacing and the review link are set once at head office. Franchisees will accept a standard they can put their own sentence in front of.

Make it smart rather than loud. At network scale a dumb ask does real damage, because the same customer visits four of your sites. One contact per person however often they return, a second booking moving the existing ask rather than adding another, anyone who reviewed you in the last 90 days skipped, opted-out contacts never re-enrolled, follow-ups that stop the moment the review lands. Without those rules a 200-site network asks one person four times in a month.

And no rating step, ever. The temptation at scale is a filter: ask how it went, send the happy ones to Google, route the unhappy ones to a private form. Do not build it, and do not accept a vendor that offers it. Google's contributed-content policy says merchants must not "discourage or prohibit negative reviews, or selectively solicit positive reviews from customers". The US Federal Trade Commission's rule on consumer reviews, in force since 21 October 2024, bans claiming a review section represents all the reviews submitted when reviews have been suppressed on rating or sentiment. Ask everyone, take what comes, answer it well. The tactics are in how to get more Google reviews.

One inbox for every location

Once the asks run, reviews arrive faster than the current arrangement can absorb. The requirement is one list, every location, filterable down to a single site. That buys you a number for last week without asking anyone, a queue of everything nobody has replied to, and a route for the ones that need head office. A 1-star review naming a staff member, or alleging something legal, should reach head office within hours. It cannot if it only ever notified the franchisee.

Who replies, corporate or the franchisee?

This is the question franchisors lose sleep over, and the competing guides do not answer it. Two clean models exist, plus the hybrid almost every network ends up running.

Corporate replies to everything: consistent voice, no legal exposure, nothing unanswered, and replies that are visibly generic because the writer was not there. It breaks past 30 sites or so, when the volume outgrows one coordinator.

The franchisee replies to everything. Specific, fast, occasionally catastrophic. They know the customer and the Tuesday in question, which is what makes a good reply good. They also argue in public and go quiet in a busy season.

Build the tiered model around the review, not who owns the site.

Two things make that hold rather than sit in a manual nobody opens. The first is saved replies: templates written once at head office, with placeholders that personalise on insert, in a Templates menu in the composer where the franchisee already works. A franchisee will use a good template. They will not open a PDF. Positive review response examples is the starting set, though the 1-star replies get read by the most people.

The second is drafting with a person approving. An AI draft of a review reply, edited and sent by a human, closes the gap between "the franchisee has no time" and "the reply has to be specific". Head office sets the tone; the franchisee edits the two sentences only they could write.

Some franchisees will not reply at all, so write the escalation down before it happens: the standard in the agreement, the unanswered count visible to everyone at conference, head office replying for them after 14 days. The visibility usually does the work.

The league table, and what it does to franchisee behaviour

Publish a table of every location, ranked, and update it monthly. It changes behaviour faster than anything else here, for a reason that has nothing to do with software: franchisees compete, and they do not like being 47th.

Rank on the period's rating, not the lifetime one. Lifetime ratings are a monument to whatever happened in 2021, and they hide the franchisee who has turned a site around.

Do not show all 200 rows. Nobody reads 200 rows. Show the sites that need attention and the sites doing well, and put the full list behind a click. A table with five problems and three wins on it gets read every month.

Credit inside the site, too. A staff-level view starts the same conversation inside a salon or a workshop, and franchisees often adopt it fastest. Attribution is the catch: matching a name in review text can credit the wrong person, so give each staff member their own review link and let the link do the attributing. That also keeps you the right side of Google's policy, which says merchants should not request that specific content be included in a review, staff identification included.

Read the reviews as themes, not as a score

A rating tells you a site is struggling. It never tells you why, and at 60 locations nobody is reading 4,000 reviews a quarter to find out. The temptation is a sentiment score, a number per location tracked over time. Resist it. A score cannot be checked and cannot be argued with by the franchisee it accuses. What an operations lead needs is a theme with a count and a quote under it. Not "Northgate scored 62 on sentiment" but "wait times were mentioned in 31 reviews at Northgate this quarter, up from 9, and here is one of them in the customer's words".

Cloutly does this as Signals, where a signal is any specific, evidenced mention, either polarity. It is a per-review pipeline Cloutly switches on for an account rather than something you turn on yourself, and every signal carries its denominator and a verbatim quote with a date.

Whatever you use, the test is the same. Can the franchisee check the number, and is there a customer's sentence under it.

Keep the listings consistent across the network

Reviews are half of what a customer sees. The other half is whether the hours, phone number and address are right, and those drift constantly: a franchisee changes their Sunday hours in one place and not the other four, a number is ported, a site moves two doors down. Vendors overpromise here, so be precise about what is achievable.

Google Business Profile and the Facebook page can be written to: name, phone, website, description, address, regular hours and special hours. A field your record does not carry is not pushed, so nothing gets blanked by accident. Name and address changes carry a re-verification risk with Google, so treat those as a project rather than an edit. Yelp, TripAdvisor and Google's public view are watched, not written, which is the honest position for most of the directory web. You get told when the phone number on Yelp disagrees with your record, on a weekly check rather than in real time.

Publish preview-first: a dry run against Google, a field-by-field table of what is there now and what it will become, and a human confirming. Bulk belongs on the publish rather than the edit. Select the venues, preview, publish. A blind fan-out edit across 200 profiles is how a network blanks its own phone numbers. And special hours are usually closures only: a full-day holiday closure can go out across the network, shortened Christmas Eve hours generally cannot. Better learnt in October than on 23 December.

Rolling it out to franchisees who did not ask for it

Head office bought this. The franchisees did not, and a mandate in the next agreement is 18 months away. The networks that have done it well used the same pattern.

1. Pick 8 to 12 sites, and not the best ones. Two strong operators, two struggling, the rest ordinary. The strong ones prove it works; the struggling ones prove it works on hard mode, which is the objection you will get.
2. Run 30 days and set the number in advance. New reviews per site over the 30 days, against the same 30 days last year. Write the target down first, or the pilot becomes an argument about whether it worked.
3. Do the setup for them. Every hour of franchisee effort in week one costs you sites. Claim the profiles, connect the booking or point-of-sale system, load the templates, then hand it over working.
4. Report by name at conference, not by email. The pilot sites and their numbers, on a slide, with the operators in the room. This is where the next 40 sites sign up without being asked.
5. Roll out in waves of about 20, each owned by a named person at head office for its first month.

You are not selling franchisees a platform. You are showing them what eleven of their peers got in a month, which is the only argument that moves a franchise network. Hospitality groups run the same thing venue by venue, in restaurant reputation management, and where an agency runs it for the network, reputation management for agencies is the other side of the deal.

Measuring multi-location review management

Read the league table per location, never as a network average, and add the one number that exists only at network level: the rating spread, best site minus worst, with the count of sites under 4.0. That is the brand-risk figure, and the one for the executive team. A shrinking spread means the programme is working, even in a month when the average is flat. Keep the monthly exports: in month nine the question is which sites moved, and what they did differently. Whether an assistant names your outer suburbs is a separate measurement, in AI search visibility for multi-location brands.

Franchise reputation management at 20, 200 and 400 locations

The programme is the same at every size. What changes is where the effort goes.

At around 20 sites, one person at head office can hold it. They know every operator by name and can chase a reply personally. The risk is that the whole thing lives in one head and leaves when that person does.

At around 200 sites, chasing people stops working and the standards have to be written down, in the agreement and in the templates. This is where the tiered reply model and the monthly report become the programme rather than a description of it. Just Cuts runs a network this size:

"Cloutly made it easy for us to onboard and roll out across 230+ locations. Our salons saw a 45% increase in reviews in just the first month. It's truly intuitive, effective and backed by a team who genuinely care."

Kelly Gaunt, National Marketing and Brand Projects at Just Cuts Franchising

The number to look at there is not the percentage. It is "the first month", which is what a rollout across 230 owner-operators looks like when the setup is done for them.

At 400 sites and above, head office runs a reporting function rather than a review programme. The work goes regional: someone owns 40 sites, sees their own league table, and is measured on the spread inside their region.

Cloutly is built for networks of 400+ locations, and the multi-location page covers how the pieces fit.

Frequently asked questions

What is multi-location review management?

Running one review programme across every site in a network: a standard ask that fires off each location's own system, every review landing in one inbox, a written rule for who replies to what, and a monthly league table ranking locations by rating, volume and response rate.

Should franchisees or head office respond to reviews?

Both, split by review type. Locations answer 3 to 5-star reviews in their own words. Head office drafts or approves replies to 1 and 2-star reviews, and owns anything alleging injury, discrimination or a legal claim outright. Saved templates make the split workable.

How do you get all franchise locations to ask for reviews the same way?

Standardise the trigger, not the sender. The ask fires off each site's booking system or point of sale when the job is marked complete, with timing and follow-up spacing set centrally. Leave the location name and one line of wording local, so franchisees keep a say.

Can one Google Business Profile cover multiple franchise locations?

No. Google allows one profile per business location, and locations of a chain in the same country carry the same name unless a site genuinely trades under a different one. Group the profiles into a business group so head office holds access without sharing credentials.

How do you compare franchise locations by review performance?

Rank every location on the period's rating, new reviews, response rate and days since the last review, then flag each row with one plain status. Rank on the period rather than the lifetime rating, so a site that has turned around can see it.