Key takeaways
- Nobody has published clean proof that a Google Business Profile (GBP) directly changes what ChatGPT recommends. Anything that claims otherwise is guessing.
- A third-party study reported by secondhand posts found ChatGPT recommended only 1.2% of 350,000+ locations, while Google's local 3-pack showed a location 35.9% of the time. ChatGPT is far pickier than the map pack.
- Promptwatch Data shows ChatGPT answers draw on many domains, so a GBP is at most one source among many.
- The honest approach is to test: run the same local prompts across AI tools, compare them with the map pack, change one variable at a time, and re-test.
- Google AI Overviews and AI Mode are the surfaces where a GBP most plausibly matters, since they sit on top of Google's own local data.
The short answer
Maybe, but less than most local SEO advice implies, and nobody has measured it properly.
The popular theory goes like this: ChatGPT fetches live data when someone asks for "best plumber near me," and that data mirrors the Google map pack, so optimize your GBP and you show up. One Reddit thread on r/bigseo puts it exactly that way: treat it like local SEO because ChatGPT tends to mirror map pack visibility when it fetches live data. That is anecdotal. It is a reasonable hypothesis, not a finding.
The numbers we do have point toward a looser relationship. Keep reading, because the gap between "in the map pack" and "named by ChatGPT" is the interesting part.
What the evidence actually says
The SOCi study: ChatGPT is far pickier than Google
SOCi reportedly analysed more than 350,000 business locations across 2,751 brands. The figures circulating:
- ChatGPT recommended 1.2% of locations.
- Gemini recommended about 11%.
- Google's local 3-pack showed a location 35.9% of the time on average.
One social post summarised this as AI being nearly 30 times more selective than the local pack. Other posts mention retail as the strongest category (45% making the cut) and an average 4.3 rating for ChatGPT-recommended places, but those snippets were truncated, so treat them as unverified.
A caveat I want to be upfront about: I could only find secondhand posts about this study, not the primary SOCi report. Find the original before you quote these numbers anywhere public.
Even with that caveat, the direction is clear. If ChatGPT simply copied the map pack, the two percentages would be close. They are not. Something else is doing the filtering, and a GBP alone probably is not enough to pass it.
ChatGPT pulls from lots of domains
Promptwatch Data looked at how many unique domains ChatGPT cites per response between Sep 28 and Oct 4, 2026. Among responses with citations, 47.6% cite 10 or more domains and 66.1% cite at least eight. Only 6% cite three or fewer.
Organic prompts, the ones with no brand named, are the most crowded: 52.5% cite 10 or more domains. Brand-specific prompts are narrower, with 5 domains the most common count.
What this means for local: a "best dentist in Leeds" style prompt is an organic prompt. The answer is likely assembled from review sites, listicles, directories, forums and maybe map data. Your GBP is one voice in a crowd.
What content types ChatGPT cites
In Promptwatch's content type report for Sep 28 to Oct 4, 2026, product pages made up 22.4% of classified ChatGPT citations, landing pages 16.8%, listicles 13.6%, how-tos 8.7% and reviews 1.7%. For branded prompts, reviews rose to 5.6%, and product plus landing pages together earned almost half of citations.
The report does not classify GBP or Maps listings as a content type. So there is no GBP citation share to quote, and anyone giving you one is making it up. What the data does suggest is that your own site pages and third-party reviews matter for branded questions.
How different AI tools plausibly use your profile
These are hypotheses to test, not facts.
| AI surface | Likely relationship to your GBP | Confidence |
|---|---|---|
| Google AI Overviews and AI Mode | Built on Google Search and local data, so GBP signals plausibly feed in | Moderate |
| Gemini | Google product, higher recommendation rate in the SOCi data (about 11%) | Moderate |
| ChatGPT | Mixed: live web search, third-party pages, possibly map-like data. Very selective | Low |
| Perplexity | Cites web sources heavily; GBP is indirect at best | Low |
| Copilot | Bing-based; Bing Places matters more than GBP | Low |
One claim from a vendor blog says ChatGPT cannot crawl your GBP the way a customer would. That sounds plausible, because a GBP is not a normal crawlable page, but I could not verify it. What you can say safely is that GBP data reaches ChatGPT, if at all, indirectly: through the web pages, reviews and aggregators that repeat it.
What the 2026 GBP changes might mean
A guide from Moore Tech Solutions lists several 2026 changes: Q&A replaced by an AI feature called Ask Maps, more ranking weight on engagement like posting frequency and review response speed, WhatsApp messaging, AI-drafted review replies, native post scheduling, and visibility drops after roughly 30 days without activity.
That guide hedges these as "appears to" and "multiple sources report," and I did not verify any of them against Google documentation. Treat them as unconfirmed. They are still worth watching because they shape the map pack, and the map pack is at least one of the inputs Google's own AI products can see.
The test plan
This is the useful part. You do not need to believe any theory. You can measure your own situation in a few weeks.
Step 1: Build a prompt set
Write 15 to 30 prompts a real customer would type. Mix three types:
- Discovery: "best [service] in [city]"
- Near-me style: "[service] open now near me"
- Branded: "is [your business] any good" and "[your business] vs [competitor]"
Keep the wording fixed. Changing prompts mid-test ruins comparisons.
Step 2: Record a baseline across surfaces
Run every prompt in ChatGPT (with search on), Gemini, Perplexity, Copilot and Google AI Mode. For each, log:
- Whether your business is named
- Position in the answer
- Which sources were cited or linked
- Which competitors appear
Run each prompt more than once. AI answers vary, so a single run tells you very little. Do it at roughly the same time of day and from the same location, or use location-aware tracking.
Step 3: Compare with the map pack
For the same queries, check Google's local 3-pack and expanded Maps results. Build a simple table:
| Prompt | In map pack? | Named in ChatGPT? | Named in Gemini? | Named in AI Mode? |
|---|---|---|---|---|
| best plumber in [city] | Yes | No | Yes | Yes |
The pattern matters more than any single row. If you are in the pack and absent from ChatGPT, GBP is not the bottleneck. If competitors named in ChatGPT are nearly all in the pack, the mirror theory gains support.
Step 4: Check what ChatGPT actually cites
Open the sources behind each ChatGPT answer. Chances are you will see review platforms, local listicles, directories and competitor sites rather than Google Maps. Those are the pages you need a presence on. The free GEO Inspector for ChatGPT Chrome extension from Promptwatch shows fetched versus cited sources, which makes this step faster.
Step 5: Change one variable at a time
This is where most people cheat themselves. If you update categories, photos, description, reviews, and your website in the same week, you will never know what worked.
Pick one lever per cycle:
- Primary and secondary categories
- Profile completeness (services, attributes, hours, description)
- Review volume and average rating
- Review response rate
- Posting cadence
- NAP consistency (name, address, phone) across directories
Wait two to four weeks, re-run the exact prompt set, and compare to baseline. Expect noise. A change that shows up in one run and disappears in the next is not a result.
Step 6: Track third-party mentions alongside
Because ChatGPT draws on many domains, log your presence in local listicles, review sites and forums at the same time. If you gain a mention in a "best of" list and start appearing in ChatGPT answers a week later, that is a far more likely explanation than a tweaked GBP category. Control for it, or at least note it.
What to test first
If I had to rank hypotheses by how likely they are to move results, my guess would be:
- Reviews on sites other than Google, plus rating and volume on Google itself. The SOCi snippet about a 4.3 average rating is unverified but fits the intuition.
- Presence in third-party local listicles and directories.
- Your own website's service and location pages answering the questions people ask.
- GBP categories and completeness.
- Posting cadence and response speed.
That order is opinion, not data. The point of testing is to find out whether I am wrong for your market. Retail, restaurants, healthcare and home services probably behave differently.
Tools that help you run the test
Doing this by hand works for a dozen prompts. Past that you want tracking that samples repeatedly and logs sources.
For AI visibility generally, Promptwatch tracks how brands appear in ChatGPT, Gemini, Perplexity, AI Overviews and AI Mode, including which pages get cited, with country tracking on every paid plan and state and city tracking from the Business plan. That location detail is what you need for local tests. Confirm current plans at promptwatch.com/pricing.
Birdeye is built around local presence and reviews, and its Search AI product adds location-level AI visibility tracking, which suits multi-location businesses.
Otterly.AI is a lower-cost prompt tracker that is fine for a small baseline if you do not need crawler or traffic data.
Peec AI is another straightforward option for monitoring a fixed prompt set across models.
Semrush covers the traditional local and map side alongside its AI visibility toolkit, so you can compare both views in one place.
| Tool | Good for | Local angle |
|---|---|---|
| Promptwatch | Tracking, citation sources, acting on gaps | Country, plus state and city on Business |
| Birdeye Search AI | Multi-location brands with review focus | Location-level tracking |
| Otterly.AI | Cheap baseline monitoring | Limited |
| Peec AI | Fixed prompt set tracking | Limited |
| Semrush | Traditional local SEO plus AI tracking | Map pack alongside AI data |
If you are comparing options more broadly, the GEO software directory at bestgeosoftware.com lists many more.
Mistakes that make test results useless
- Running a prompt once and treating it as truth.
- Testing while logged into a personalised account with chat history. Use clean sessions.
- Ignoring location. "Near me" prompts depend on where the request comes from.
- Changing five things at once.
- Declaring victory after a week. AI answers shift for reasons unrelated to you. Promptwatch Data has documented abrupt changes in ChatGPT behaviour, such as the site: operator rollout on August 8, 2026 and a drop in citations per response around the GPT-5.3 rollout on March 4, 2026. If your visibility moves overnight, check whether the platform changed first.
What I would do this month
Keep your GBP healthy. It is free, it feeds the map pack, and Google's own AI surfaces can see it. But do not expect it alone to get you named in ChatGPT. The data we have says ChatGPT is selective and pulls from a crowd of sources.
Spend the first week on the baseline. Spend the next four changing one thing at a time. And put as much effort into earning mentions on the third-party pages AI actually cites as you do into the profile itself. After a month of logs, you will know more about your own market than any generic 2026 checklist can tell you.
For broader tracking of how AI cites sources, Promptwatch's research hub is a good place to see how citation behaviour shifts month to month.