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Rank Dough compared 6 UK travel AI SEO agencies, ours included, and charted 11 client sites in Ahrefs. Only 4 of the 6 had client results we could check, and 9 of the 11 sites charted ended above their first month.

Not sure which of the 8 travel SEO problems matches your site? Take the quick quiz. Pick one and the matching section opens, with a question to put to any agency.

Which UK travel AI SEO agencies did we compare?

We compared 6: Adido, Ink Digital, SALT.agency, north9, Boost Brands and Rank Dough. Rank Dough is ours, so it is disclosed and scored on the same evidence as the rest.

Source: agency websites and trade press, checked 12 October 2026. Years trading counts from founding year to 2026. Team sizes are as stated by each source, and sources conflict for Ink Digital and north9.

What happened to their clients’ organic traffic?

We pulled estimated monthly organic visits for each client’s whole domain from Ahrefs Site Explorer on 12 October 2026. All 11 charts run to June 2026.

Source: Ahrefs Site Explorer, estimated organic visits a month, subdomains included, pulled by Rank Dough on 12 October 2026.

Over each chart window, 9 of the 11 sites ended above their first month. Girls’ Guide to the World grew 5.1 times, Cheaper Than Cars 4.4 times and Grand Prix Grand Tours 3.6 times. Mercury Holidays (0.8 times) and Oliver’s Travels (0.9 times) ended below where they started.

Aer Lingus’s June 2026 figure (196,387) is far below May (579,333) and may be an incomplete month.

Sites tied to Adido, Ink Digital, north9, Hostelworld and Aer Lingus all fell between March and May 2026. That looks like a market-wide pattern, not one agency’s doing. It is a hypothesis, and I have not tested why.

What do Adido’s clients show?

SPL Villas had 667 visits in March 2024, the month its partnership with Adido started on 1 March (Nitravelnews). It reached 1,205 in June 2024, a peak of 1,986 in March 2026 and 1,118 in June 2026. The peak is 3.0 times the March 2024 figure.

Latin Routes shows the opposite. It had 719 visits in January 2025 and 255 in March, after a rebrand and new website that Travel Weekly covered in February 2025.

Adido says it prepared the site for launch and now leads the SEO. I do not have Adido’s start date, so this is a pattern, not proof.

What do Ink Digital’s clients show?

Girls’ Guide to the World is the clearest result. It had 605 visits in January 2024, a peak of 3,226 in February 2026 and 3,061 in June 2026, which is 5.3 times the start. Ink does not publish its start date, so I have used January 2024 as the first month of the chart only.

Mercury Holidays went from 65,333 visits in January 2024 to 140,319 in August 2025, then 50,358 in June 2026. Ink’s case study marks the project completed on 8 April 2025 and reports 181 percent more keyword impressions. Ocean Florida peaked at 32,672 in September 2024 and had 18,307 in June 2026.

What do north9’s clients show?

Rainforest Cruises rose from 28,242 visits in January 2022 to 132,958 in May 2025, then fell to 53,212 in June 2026. north9’s card says organic traffic rose 326 percent year on year but gives no dates. Oliver’s Travels reached 261,623 in June 2024 and had 78,974 in June 2026.

Grand Prix Grand Tours is the most volatile. It had 78,248 visits in May 2022, 9,172 in August 2022 and 99,758 in April 2026.

north9 claims a 16,057 percent traffic rise, and the client quote says about 1,500 visits a month grew to over 20,000. Ahrefs shows no month below 6,028 between January 2022 and June 2026, so a 1,500 start would predate our chart.

What do Rank Dough’s results show?

Rank Dough has one client in the data, Cheaper Than Cars. The other two sites, Aer Lingus and Hostelworld, are my work before Rank Dough.

At Aer Lingus I worked at iProspect from January 2021 to March 2022. Site traffic went from 89,003 to 435,642 visits a month, 4.9 times. Travel reopened over the same months, so I do not credit that to SEO alone. The site peaked at 895,506 in June 2024 and had 196,387 in June 2026.

At Hostelworld I was in-house from May 2022 to January 2025. Whole-site traffic went from 801,060 to 954,250 visits a month, up 19 percent. It peaked at 1,522,154 in May 2025, after I left, and had 883,602 in June 2026.

Cheaper Than Cars is a Rank Dough client. The earliest file I have is from February 2023, when the site had 649 visits. It had 4,619 in June 2026, but I have not confirmed the engagement dates, so I make no claim from it.

What makes travel SEO hard?

Travel SEO is hard because sites are international, built on old systems, database-heavy and seasonal. Eight topics follow, and under each are the items we have seen. Five of the eight come from my own work.

Which of these travel SEO problems does your site have?

Pick the problem closest to yours. The matching section opens below, with a question to put to any agency.

How should you choose a travel AI SEO agency?

Four checks follow from the data above. They are my judgement. I have not tested whether they predict results.

1. Ask for the client’s domain and the dated start of the work. Only 1 of the 8 other-agency sites we charted came with one.
2. Put the agency’s chart next to the market, not next to zero. Sites tied to 5 different agencies or teams fell between March and May 2026, so a flat line may be a good result.
3. Ask how it protects traffic through a migration or relaunch. Latin Routes fell from 719 to 255 visits in two months after its relaunch.
4. Check a percentage claim against Ahrefs. Grand Prix Grand Tours’ 16,057 percent needs a start month nobody published.

What do people ask about UK travel AI SEO agencies?

Who is Rank Dough, and what are the limits of our own record?

Rank Dough is a small team. Our site names 2 people, Roman Sadowski and David Mason. We trade as Fortunata and work from Loughrea, Co. Galway and from 700a London Road, Essex, SS0 9HQ.

I have worked in search since 2004. My travel work is Aer Lingus at iProspect from January 2021 to March 2022, Hostelworld in-house from May 2022 to January 2025 and Cheaper Than Cars as a Rank Dough client. The first two were not Rank Dough engagements, so I do not count them as agency clients.

We have traded for 1 year and we are not travel-only. Our site lists 3 awards from my earlier work, and I have not linked the winners lists yet, so I do not count them here. This article is published by Rank Dough, which appears in the table, so check every claim against the named domain in Ahrefs.

The limits are clear. The traffic is estimated at domain level from one pull on 12 October 2026. The search results check covers 3 UK searches in one snapshot. There is no before-and-after test, and 7 of the 8 other-agency sites have no known start date.

Sources

• Ahrefs Site Explorer, estimated monthly organic traffic for 11 client domains, and Ahrefs SERP Overview for 3 UK searches, pulled by Rank Dough on 12 October 2026
• Google Search Central, How to move a site
• Nitravelnews, SPL Villas partners with Adido, start date 1 March 2024

The post Best UK Travel AI SEO Agencies (2026): Compared on Client Results appeared first on Rank Dough Digital.

Claude can handle most of the hands-on work in SEO, from keyword research and content briefs to technical audits, publishing and Search Console analysis, once you connect it to your tools and give it written rules. Rank Dough tested every step of this guide on its own site between 5 and 9 October 2026, and took one new page from an 80-keyword research pull to a Google-indexed URL in a single day.

The guide covers which Claude features matter for SEO, how to set them up, how to use Claude for each SEO job, the prompts to start with, 12 hacks and the limits. Boxes marked “From our build” show what happened when we did it ourselves, with real screenshots.

What can Claude do for SEO, and what can’t it do?

Claude can do the research, writing, analysis and repetitive build work in SEO. It reads data you give it or pulls through connectors, writes to rules you set, runs code to process files, and can publish drafts to a CMS through its API.

It can’t replace judgement. It doesn’t know your margins or which customers matter most, it can be confidently wrong about numbers it hasn’t checked, and without connectors it has no live data at all.

Source: Rank Dough’s working view from client and in-house SEO work, October 2026.

What are the best Claude SEO hacks?

These 12 come from running the workflow, not from a list of tips. We scored each one on impact and on the effort to set it up; start with the bubbles in the top-left corner. Each hack is explained in context in the sections below.

Which Claude features matter for SEO?

Ten Claude features do the work in an SEO workflow. Each one replaces a step you would otherwise do by hand: exporting a file, opening a tab or re-explaining your brief.

Source: Claude features as used by Rank Dough, 5 to 9 October 2026. Custom connectors via remote MCP are available on Free, Pro, Max, Team and Enterprise plans, per Anthropic’s help centre.

How do you set up Claude for SEO?

Set up four things before your first SEO task: data, projects, rules and memory. It takes an afternoon, and every later task gets faster because you stop re-explaining.

1. Connect your data. In Claude, go to Settings, Connectors, and add the tools you already pay for. A tool without an official connector can often be added as a custom connector by URL.
2. Create one project per site. Put the brief, target markets, past audits and recent exports inside it, so answers come from that site’s context.
3. Write your rules as skills. Page structure, tone, banned phrases, what needs a source, who approves a publish. A skill loads these rules every time the task comes up.
4. Turn memory on and correct it. When Claude gets a preference wrong, say so once; it carries the correction into later chats.

How do you do keyword research with Claude?

Use Claude to pull, sort and question keyword data, not to invent it. Without a connected tool or an uploaded export, its search volumes are guesses.

1. Give Claude your seed topics: your services, what customers ask in emails and calls, and your own category language.
2. Pull volume, difficulty and the top 10 for each seed through a connector, or upload an export from your keyword tool.
3. Ask Claude to group the keywords by search intent and map each group to one existing or new page.
4. Check the search results for every keyword you plan to target before you commit.
5. Add the follow-up questions each keyword leads to (cost, time, minimums, comparisons), even at zero volume, because buyers and AI tools ask them next.

Source: Ahrefs Keywords Explorer and SERP Overview through the Ahrefs connector, United States, pulled 5 and 9 October 2026. Keyword difficulty is the figure the connector returned; the Ahrefs app can show a different, newer figure.

How do you write content briefs and articles with Claude?

Let Claude assemble the brief and the first draft, and keep the facts and the final edit with a person. A good brief from Claude lists the target keyword, the questions the page must answer, what the current top results cover and what your page can add that they don’t.

1. Pull the People Also Ask questions and the headings of the top results for the keyword.
2. List the facts only you have: your prices, your data, your results, your process. These are what make the page worth citing.
3. Draft against your skill rules: answer first, one idea per paragraph, every heading a question.
4. Fact-check every number against its source, including titles, meta descriptions, image alt text and FAQ answers.

How do you plan content and avoid cannibalisation with Claude?

Ask Claude to scan your own site before it plans anything new. A plan built without knowing what already exists will happily commission pages you already have.

Source: WordPress REST API scan of rankdough.com (29 pages, 19 posts) and Yoast focus keywords, 9 October 2026.

Keep the plan in an artifact rather than a chat message. Claude can build a sortable dashboard where every row shows its keyword, volume, status and live URL, and you can update it as pages go live.

How do you build and publish pages with Claude?

Let Claude build and publish drafts through your CMS’s API, and keep the publish decision with a person. On WordPress, an Application Password lets Claude create drafts, set categories and upload images without your main login.

1. Copy the structure of a page that already works on your site, so the new page matches without a redesign.
2. Open with the keyword in the H1 and one fact sentence: who you are, where, and what you do.
3. Create it as a draft, read the preview, then publish.
4. Add internal links from the menu and related pages only once the page is live.

Location pages need care. Two near-identical pages for two countries is a doorway page, so each version needs facts that only apply to that market.

How do you run technical SEO and UX audits with Claude?

Give Claude the crawl data and let it do the sorting and joining. Upload your crawler exports and it can list non-indexable URLs, missing or duplicate titles, redirect chains and orphan pages, then join each issue to its Search Console clicks so you fix what has traffic first.

Claude can also check the live site. In its own sandbox it can run a headless browser that opens public pages, takes screenshots and measures what actually renders, such as font sizes, image weights and whether content needs JavaScript to appear.

For a UX review, ask Claude to walk the site like a buyer, page by page, on desktop and at phone width. The question for each page is whether a prospect understands the offer, trusts it and knows what to do next.

Ask for the output as a report you can reopen and share: a verdict and score first, then an annotated screenshot per page, each finding with its fix and an evidence label, and the fixes in order.

How do you analyse Search Console data with Claude?

Connect Search Console, or upload exports, and ask questions in plain English. With a connector, “is this URL indexed?” and “what dropped against last month?” each take one sentence.

Then ask Claude to turn the data into a dashboard you can reopen. The one below mirrors the Search Console performance view for a client’s site: 16 months of weekly clicks and impressions, built in one chat from connector data.

Compare periods by page to see every new or changed URL at once. Pages with high impressions and a weak click-through rate usually need a title that matches the query better.

Source: Google Search Console, rankdough.com property, pulled through the Rank Dough GSC connector on 9 October 2026. Performance data lags about three days.

How do you write titles, meta descriptions and schema with Claude?

Ask for options with limits stated: titles under 60 characters with the keyword near the front, meta descriptions under 155 characters with a number or a reason to click. Then generate FAQ schema from the exact text of the FAQ on the page, so the two never disagree.

Check every one by hand before publishing. Google judges the page, not the tool, and a wrong figure in a meta description is still a wrong figure.

How do you run an AI visibility audit with Claude?

Treat AI answers as a channel you sample, not a ranking you read. In SparkToro’s study of 2,961 runs across ChatGPT, Claude and Google’s AI, the chance of getting the same brand list twice was under 1 in 100, and the same list in the same order about 1 in 1,000. What holds steady is the share of answers that mention a brand.

So Rank Dough built its AI audit around visibility as a rate. Claude researched how practitioners and tracking tools run these audits, checked the evidence on sample sizes, and turned it into a five-phase process: scope, prompt library, baseline sampling, diagnosis, and an action plan where every fix ships with a test.

1. Build a prompt library. 8 to 12 intent clusters with 5 to 8 phrasings each, tagged by funnel stage, market and branded or non-branded.
2. Sample each cluster on each AI surface about 100 times. Standard binomial maths puts that at about ±10 points at 95% confidence. Spread runs over several days, logged out, from the target country.
3. Report each surface and market separately. Show the rate, the run count and the interval. Never a rank position and never one blended score.
4. Diagnose where the brand drops. Recognised by the model, matched to the buyer’s question, retrieved as a source, and finally selected.
5. Tag every number. Observed, own sample, third-party estimate or modelled, so the client knows what to trust.
6. Pair each action with a test. Control clusters, sample size and a readout date, before anyone claims a result.

Our own ChatGPT tests found that ranking first in Google did not mean being cited, and that the pages it cited stated prices, minimums and turnaround in plain sentences. The full method and results are in How ChatGPT chooses which websites to cite.

How do you get screenshots and visuals for SEO content with Claude?

Use real, annotated screenshots for how-to content, so readers can follow the steps. Claude’s headless browser can capture public pages, and Claude in Chrome can capture tools behind your login, such as Ahrefs and Search Console.

Headless browsers stop at bot checks: Google returned a reCAPTCHA and the Ahrefs checker a “verify you are human” screen in our tests. Then run a privacy pass on every capture before it goes public.

What are the limits of Claude for SEO?

• Data is only as good as the source. Our Ahrefs connector and the Ahrefs app gave different difficulty scores for the same keyword.
• It can be confidently wrong. A chart and a claim in this guide’s first draft were both wrong until the fact check.
• Logins and bot checks stop it. Headless browsers can’t pass reCAPTCHA or reach logged-in tools; use Claude in Chrome or your own exports.
• It makes changes fast, including wrong ones. A menu link to an unpublished page went live briefly before Claude caught it on the live site.
• It has no business context unless you give it. Margins, priorities and which customers matter stay with you.

Every limit has the same fix: a person reads the live preview before anything is published, and every number names where and when it was pulled.

What should you set up first?

Start with data, then rules. Connect your keyword tool and Search Console, create a project for your site, and write your page and content rules as a skill.

Then run one small job end to end, such as a single page from keyword to indexed URL, and fix the process before you scale it. Rank Dough runs every workflow on its own site first, and I’ll update this guide with the three-month Search Console numbers for the pages built here.

FAQ

Sources

• Rank Dough build record: Ahrefs pulls, WordPress build and Search Console data for rankdough.com, 5 to 9 October 2026
• SparkToro, New research: AIs are highly inconsistent when recommending brands or products, January 2026: sparktoro.com
• Google Search Central, Google Search’s guidance about AI-generated content: developers.google.com

The post How to use Claude for SEO: the complete guide, built from our own workflow appeared first on Rank Dough Digital.

To get cited by ChatGPT, put one complete fact sentence in the first 50 words of your page and repeat its numbers in the meta description. Rank Dough scored 23 pages across six buying prompts in September 2026: 11 of the 15 pages ChatGPT cited stated their offer in the first 50 words, against 1 of the 8 Google-ranking pages it skipped. Ranking didn’t help. The brand at #1 in Google for both head terms earned zero citations.

I spent three weeks finding out why, across three rounds of testing and two GPT set-ups (the ChatGPT app and a GPT agent workspace). The short answer is that ChatGPT cites pages it can lift a fact from, not pages that rank.

How do you get cited by ChatGPT?

You get cited when ChatGPT can lift a complete fact sentence from the text it already holds about your page. In our September 2026 test every verbatim quote in ChatGPT’s answers came from a page’s opening, a heading or its meta description. Nothing came from body copy further down, and nothing came from schema.

So the job has four parts, in the order the model works through them.

1. Be known. ChatGPT seeds brand names it already knows into its searches. If nobody talks about you on Reddit, in reviews or in editorial lists, you fight for the generic slots. An accommodation brand we track was named in 2 of 168 category answers in October 2026, and 5 of 5 times when asked about by name. The category answers were built from “top 10 websites” articles the brand wasn’t in.
2. Match the words it searches. ChatGPT rewrites your buyer’s prompt and adds criteria: minimum order, turnaround, price, location. State those facts in plain text, not in a PDF or behind a quote form.
3. Get into the pool. Your page has to come back in ChatGPT’s own search results. Google position does not carry over.
4. Win the extract. Put the offer, with numbers, in the first 50 words under the H1, and repeat the numbers in the meta description. 11 of the 15 cited pages did this. 1 of 8 ranking pages did.

Everything below is the evidence for those four steps, and the changes we’d make first.

Source: Rank Dough citation test workbook, 1 September 2026; GPT workspace audit, 7 September 2026.

How did we test which websites ChatGPT cites?

We picked one vertical, custom softball jerseys in the US, where a brand we know ranks #1 in Google for the two head terms. I wrote six buying prompts, ran each in a fresh ChatGPT chat with web search on, and recorded every linked source URL.

Then I fetched every cited page plus every page in Google’s US top 10 for the matching keywords as raw HTML, and scored all 23 pages on 22 attributes: opening content, headings, stated facts, FAQ, schema, speed, robots.txt and more.

Methodology: all citation data comes from ChatGPT runs on 1 September 2026, re-tested with a GPT agent in a separate workspace on 7 September; Google positions come from Ahrefs SERP Overview (US, checked 30 July and 12 August 2026); page attributes come from raw HTML fetches on the same days, which we used as a stand-in for what ChatGPT’s crawler sees. That it doesn’t run JavaScript is our observation from these tests, not something OpenAI documents.

Source: Rank Dough citation test log, ChatGPT app, 1 September 2026; Ahrefs SERP Overview, US, “custom softball jerseys” checked 12 August 2026 and “custom slowpitch softball jerseys” checked 30 July 2026. Prompts are not Google keywords, so each was compared with the closest of the two head terms.

The brand at #1 in Google for both head terms, custom softball jerseys and custom slowpitch softball jerseys, was cited on none of the six prompts. ChatGPT cited 15 other pages instead, 33 times in total because several were cited on more than one prompt, and none of them ranked in Google’s top 10 for those terms. Google’s own AI Overview for custom softball jerseys didn’t cite the brand either: it listed 12 other URLs.

Why does ranking #1 in Google earn zero ChatGPT citations?

Because ChatGPT is not reading Google. It rewrites your prompt into its own search queries, pulls candidates from its own index, and picks sources from the text extracts attached to those results.

Our reading is that Google rank never enters that pipeline. In the 7 September rerun, the #1 Google brand didn’t appear in any of the six retrieval outputs, so it lost before its page was ever judged. That rerun used a GPT agent in a separate workspace, not the ChatGPT app, so treat it as a strong pointer rather than proof.

The page had a second problem as well. The #1 Google result was a shopping grid whose first 1,300 lines of its raw HTML were a cart drawer, navigation and product counts.

The first commercial fact on the page, a 3 to 4 week lead time, sat at line 1,303 of 1,754. A competing page that got cited stated its lead time at line 45.

Position inside ChatGPT’s own result list did not decide selection either. In a follow-up audit the model cited results sitting at positions 12 and 14 and skipped another competitor holding position 1 in ChatGPT’s own result list with three URLs. The #1 Google brand wasn’t in that list at all.

The #1 brand had a third problem we only found in a later audit. Its www address returned fetch errors to the model while the non-www address loaded, and its robots.txt and llms.txt failed the same way. A hostname fault kept it out of a thorough search pass before any page was judged. Check your own: fetch both hosts the way a crawler would, not in a browser that follows redirects for you.

Source: GPT-6 Astra audit, 5 runs, September 2026; verified live against both hosts.

My working model after three rounds, labelled as a hypothesis, is a four-stage funnel: the brand has to be recognised, the page has to match the model’s rewritten queries, the URL has to enter the result set, and the extract has to win selection. In this test, most pages failed at stage two or four, not stage three.

Source: Rank Dough citation test and GPT workspace audit, September 2026; line positions from raw HTML fetches, 21 September 2026.

What did cited pages have that ranking pages didn’t?

We scored the 15 cited pages against 8 pages that ranked in Google’s top 10 but were never cited. Six attributes separated the groups.

Word count did not: cited pages averaged 1,726 words against 2,143 for the ranking pages, and the two shortest cited pages ran 137 and 213 words. Price being shown, Product schema and robots.txt access did not separate the groups either, because every page in both groups already allowed the AI crawlers.

Source: Rank Dough page scoring workbook, 23 pages, September 2026. These are observed gaps in one vertical, not proven causes.

The pattern behind the numbers is extraction. ChatGPT’s answer bullets repeated fact sentences from the cited pages near word for word: a 21-day production claim from one page’s opening, a 10-player minimum and a $109.97 price from another’s meta description, a 3 to 5 week turnaround from a single sentence on a 137-word page.

Pages that gave the model a liftable sentence got quoted. Pages that made it hunt got skipped, and when the model could not find a number it either wrote “request a quote” or, twice in our logs, invented a turnaround figure the page never stated.

How does your page compare with the pages ChatGPT cited?

Tick the traits your key page already has. The checker shows how common each trait was among the 15 pages ChatGPT cited and the 8 Google-ranking pages it skipped, and which gaps to close first.

What does ChatGPT actually read when it cites a page?

A cached text extract, not your live page. Across two audited runs ChatGPT cited 5 of 17 and 6 of 16 returned results while opening zero pages.

Each result carried a stitched extract of the page: headline, intro copy, bullets, prices and FAQ text, with per-page crawl ages running from two weeks to a year. Five things follow from that.

1. Your structured data is invisible at the moment of citation. The extract carried no JSON-LD, no schema.org markup and no meta tags in any audited run. Keep schema for Google; stop selling it as a ChatGPT citation factor.
2. Bot walls block page opens, not citations. One cited page in our test sat behind a password wall and another behind a bot challenge, and both kept earning citations from stale extracts. The same lag works against you: a fixed page stays invisible until the crawler returns, so plan in weeks.
3. Template order looks like a citation factor, though we haven’t tested it directly. Whatever your theme prints first in the HTML is what fills the extract. A cart drawer and a mega menu in front of your content push the facts out of the window the model reads.
4. Internal links still move citations. In one audit the brand’s price list was cited only after the model followed a link from the collection navigation. A page nobody links to from a crawled page doesn’t get an extract. And once you fix a page, expect weeks before the new extract shows: crawl ages in our audits ran from two weeks to a year.
5. It reads your pages against each other. On a branded “is this brand legit” prompt, ChatGPT named an accommodation brand, cited its own site, then told the reader to check carefully before paying. The reason: one page stated a booking fee, another said the service was free, and the footer’s founding year didn’t match the company register. Fix contradictions on your own site before you chase mentions elsewhere.

Source: GPT workspace audit ledger, 7 September 2026; web.run interrogation, 21 September 2026; Rank Dough AI visibility tracking, 168 ChatGPT runs, October 2026.

What does a ChatGPT retrieval pool look like?

About two thirds of every commercial pool we audited was Reddit, Wikipedia and PDFs. Each pool held exactly 12 Reddit results, and that held in 12 of 12 pools across five unrelated verticals. That leaves 7 to 12 slots per pool for supplier pages, and those are the slots you’re competing for.

Who fills the non-Reddit slots changes by vertical. In dental it was provider pages, in standing desks editorial only, in toys retailers. Audit your own category before you decide which page type to build.

One more thing we expected to matter and didn’t. Running the search at thorough depth instead of minimal returned the same pool. Depth changed which entries got cited, not who was in the pool. So entry is decided early, on retrieval; effort by the model only reshuffles selection. One audit, so a pattern, not proof.

Source: Rank Dough retrieval pool audits, GPT-6 Astra rounds 6 to 8, September 2026, five verticals. Counts come from one model surface; the 12-Reddit pattern is observed there, not a documented rule of the ChatGPT app.

How do fan-out queries decide which pages compete?

In neither audited run did ChatGPT search the words we typed. Both times it rewrote the prompt and appended buyer decision criteria: minimum order, turnaround, pricing, youth sizing.

Those appended terms are retrieval keys. A page that states its minimum and turnaround in plain text matches the queries the model actually sends; a page that keeps those facts in a PDF or behind a quote form competes for nothing.

The rewrites also seeded brand names the model already knew from training into the searches, which means well-mentioned brands enter the candidate pool by name while unknown brands fight for the generic slots. Our reading, not yet measured: that makes third-party mentions a retrieval problem, not a vanity metric.

You can see this layer first-party and free. Bing Webmaster Tools added an AI Performance report in February 2026 and expanded it in June 2026 with intents, topics and citation share.

Its grounding queries show the reformulated searches the AI ran before citing or skipping you on the Microsoft surface. Verify your site, pull 30 days of grounding queries, and you have a proxy for the searches AI systems run. It comes from Microsoft’s AI, not ChatGPT, so treat it as a guide rather than a mirror.

Source: Rank Dough audit logs, September 2026; Bing Webmaster blog, February and June 2026.

What should you change on your pages first?

Is ChatGPT SEO different from Google SEO?

Yes, at the selection step. Google ranks pages. ChatGPT rewrites your prompt, pulls candidates from its own index and cites from cached text extracts without opening the page. Across our six prompts its citations overlapped with Google’s top 10 zero times.

What carries over from Google SEO: crawlable pages on a working host, plain-text facts, one URL per intent, internal links. What doesn’t: schema (absent from every extract we audited), word count (cited pages averaged 1,726 words to 2,143 for ranking pages) and Google position itself.

“ChatGPT SEO” is the extract and the fan-out terms. There’s no holy grail beyond that, and anyone promising a specific citation is guessing.

Source: Rank Dough citation test workbook, 1 September 2026; web.run interrogation, 21 September 2026.

What stayed unstable across our tests?

Three things refused to settle, and I’d rather tell you than sell you certainty. Listicles got cited in one round, where a competitor’s dated comparison post became the source for a third brand’s recommendation, and got skipped in another round a week later.

Reddit threads entered 12 of 12 commercial retrieval pools we audited but rarely won selection, so pool presence and citation are different outcomes. And only 5 of the 18 URLs from the first run reappeared in the 7 September rerun, which means a single test proves nothing and any agency promising a specific citation is guessing.

Run your prompts across several days, track visible retrieval, citation and accuracy as three separate numbers, and judge changes against that baseline.

Source: Rank Dough citation logs, 1 and 7 September 2026; retrieval pool audits, September 2026.

Limits of this test. The attribute table comes from a single run on 1 September. We scored the HTML as it was on the day, while ChatGPT read extracts up to a year old. And we didn’t change a page and re-test it, so everything here is correlation, not proof of cause.

How can you run this test on your own brand?

Run the same three steps we did: ask, record, compare. It takes an afternoon for a first pass, and the first pass tells you which pages to fix before you invest in tracking.

1. Write six buying prompts. One each for a head term, a niche term, a natural question, a best-of, an audience-specific ask and an attribute such as fast turnaround.
2. Run each in a fresh ChatGPT chat with search on. Record every cited URL, whether your brand is named, and any fact the answer states about you.
3. Pull Google’s top 10 for the matching keyword. Compare the two lists; overlap tells you whether ranking is helping at all.
4. Read the cited pages’ openings and meta descriptions. Note which facts the answer lifted and where they sat on the page.
5. Repeat over several days before judging anything. Only 5 of 18 cited URLs repeated between our runs a week apart.

For tracking over time, we now run every prompt three times per check and average the result, once a week, on ChatGPT, Google AI Overviews and AI Mode, with mentions and citations logged as separate numbers. Our AI visibility audit process explains the set-up.

FAQ

Sources

• Rank Dough citation test workbook: 6 ChatGPT prompts, 23 pages scored, 1 September 2026; second-model rerun ledger, 7 September 2026; retrieval pool audits, September 2026; web.run interrogation, 21 September 2026
• OpenAI, Overview of OpenAI Crawlers: developers.openai.com/api/docs/bots
• Microsoft Bing, Introducing AI Performance in Bing Webmaster Tools, February 2026: blogs.bing.com

The post How to get cited by ChatGPT (and why ranking #1 in Google didn’t) appeared first on Rank Dough Digital.

Yes for ChatGPT, on the evidence so far, but nobody has proved cause. Ahrefs found branded web mentions correlate at 0.664 with AI Overview visibility across 75,000 brands, against 0.218 for backlinks.

Rank Dough’s own test on six custom softball jersey prompts found zero overlap between the pages ChatGPT cited and Google’s top 10. Every study I could find is correlational, so treat mentions on the right pages as the best bet you have, and test it on your own brand.

Why did I put this question to four expert AI profiles?

I wanted more than the usual “mentions are the new links” posts. So I took one question to SuperMe, a network that builds AI profiles of experts from their talks, writing and notes, and lets you compare several of them on one question.

I asked the AI profiles of Ethan Smith (Graphite), Kevin Indig (Growth Memo), Alex Halliday (AirOps) and Eli Schwartz the same thing: do unlinked brand mentions get a brand cited in ChatGPT and Google AI Overviews?

Read their answers for what they are. An AI profile summarises what an expert has published and said.

It isn’t the person. Every time I quote one below, I label it “AI profile”, and I’ve only kept numbers I could trace back to the expert’s own published study. Where a profile gave a figure I couldn’t verify, I left it out.

Then I checked their answers against our own test data from September 2026. That’s the part you won’t find anywhere else.

What does the public data say about mentions versus links?

Three published studies carry most of the weight in this debate. They all point the same way, and they all measure correlation.

Source: ahrefs.com/blog/ai-overview-brand-correlation/, semrush.com/blog/the-ghost-citations-study/, airops.com/lp/the-state-of-aeo-report. Checked 6 October 2026.

I pulled each figure from the original study page on 6 October 2026 and dropped any number I couldn’t trace back to its source.

The Ahrefs gap is the headline. Mentions, linked or unlinked, track AI Overview visibility three times more closely than backlink counts.

The AirOps figure tells you where those mentions live: on other people’s sites, not yours. So for most brands, the work that moves AI visibility happens off-site.

What did our own softball jersey test find?

In September 2026, Rank Dough ran a small citation test on a custom softball jersey store. We put six buying prompts through ChatGPT, logged every result it retrieved and every page it cited, and compared that with Google’s top 10 for the same terms. We reran it a week later with a retrieval audit.

Source: Rank Dough first-party test, custom jersey vertical, 1 and 7 September 2026. Small sample, one vertical.

Three things stood out. First, ranking first in Google predicted nothing here.

Second, Reddit showed up in every retrieval pool but rarely got the citation. Third, ChatGPT never searched the words we typed.

It rewrote each prompt and added buyer criteria like “minimum order”, “turnaround” and “youth sizing”.

For a small brand, that last point matters most. The pages ChatGPT pulls are the ones that answer those rewritten queries. If a roundup page already ranks for “custom softball jerseys low minimum order”, a mention on that page with your minimum stated next to your name is worth far more than a link from a big site that never comes up.

Where do the four expert AI profiles agree?

On four points, all four profiles landed in the same place.

1. The page matters more than the domain. A mention helps only if it sits on a URL the engine retrieves for your prompts. The Ethan Smith profile put it plainly: a mention on a big site’s page that never gets cited does nothing for you.
2. “Go win Reddit” is weak advice. All four profiles rejected it. That matches our test: Reddit made every pool and still lost most of the citations.
3. Track mentions and citations as two numbers. A citation means the engine used your page. A mention means a buyer read your name. They move separately.
4. Nobody has proved cause. The Kevin Indig profile said it straight: a cross-sectional chart can’t tell you whether mentions drive visibility or whether strong brands collect both.

Where do the expert AI profiles disagree?

The first split is about links. The Eli Schwartz profile said he tells companies to pause paid link campaigns and has never seen it hurt them.

But he and the Ethan Smith profile both made one exception: brands with little awareness still benefit from earned links. That’s the exception that matters for small ecommerce brands, and it’s the one the big studies can’t speak to, because their samples lean on well-known brands.

The second split is about measurement. The Ethan Smith profile argued that most citation tracking measures a layer that often doesn’t exist.

When ChatGPT answers without searching, there are no citations at all. The answer then rests on how often the model has seen your brand next to your category across the web.

Tracking tools that force a search can show you a very different picture from what a buyer sees. The Alex Halliday profile accepted that point, which narrows what any citation study can claim.

Why does a ghost citation matter if you still get the link?

A ghost citation is an AI answer that links your page as a source but never names your brand. In the Semrush and Kevin Indig study, 61.7% of AI appearances worked this way.

Because buyers act on names, not footnotes. Kevin Indig’s breakdown of a clickstream study of 846,000 US Google sessions from February and March 2026 found that in AI Mode, 74% of users picked the top result and 64% clicked nothing at all. If the answer links your page but names a competitor, the competitor gets the sale.

The Alex Halliday profile described what gets a brand named rather than just linked: the brand name sits right next to the claim. “Brand X is the best option for youth leagues because the minimum order is 10” survives extraction.

“A recent study found the minimum is 10” lets the model lift the fact and drop your name. Our own test agrees.

The sentences ChatGPT lifted whole were the ones that carried brand, product, audience, turnaround and price in one line.

What should a small ecommerce brand do first?

Here’s the order I’d work in, based on the evidence above and our own test. Treat the order as my judgement, not a proven sequence.

1. Map the pages first. Run your real buying prompts several times on each engine. Record every third-party page that gets retrieved and cited. Each engine pulls a different set.
2. Rank those pages by how often they appear. Work down the list from the top.
3. Ask for a sentence, not a logo. Get your brand named next to one specific use case and one number: “best for youth teams, minimum order of 10”.
4. Repeat one claim. Five pages that say you’re best for the same use case beat twenty loose mentions.
5. Fix your own pages for the rewritten queries. Put minimums, turnaround, pricing and sizing in plain text near the top, so third parties have something to quote.
6. Keep earned links in the plan. If nobody knows your brand yet, links still help your own pages win the follow-up questions.

Is a mention on this page worth chasing?

Use this before you spend time or money on a placement. Answer five questions about the page you are considering and you get a verdict, the reason for each answer with a link to the evidence in this article, and the exact line to ask the site to publish.

How would you prove mentions cause citations?

You’d need a test with controls, and nobody I could find has published one. Two of the AI profiles described designs, and they control for different things.

The Ethan Smith profile splits the cited URLs into treatment and holdout groups. The Kevin Indig profile compares the brand against matched brands that get no new mentions, and checks that any lift shows up only on the prompts you targeted.

The test I’d run combines both:

• One low-awareness brand in one narrow category, such as custom softball jerseys.
• Cited URLs split into treatment and holdout within high, mid and low citation-frequency buckets.
• Targeted prompts compared with untargeted prompts for the same brand.
• Two or three matched brands of similar size as controls.
• Mention rate, citation rate and ghost citation rate tracked separately, on forced and unforced runs, several times a week.
• At least three months, with every placement date logged, and tracking kept running after outreach stops to see how fast any lift fades.

A lift that appears only on targeted prompts, only after placement and only for the test brand would be real evidence. Anything less is another correlation. Rank Dough is planning this test on the custom kit vertical, and I’ll publish the results here.

Frequently asked questions about unlinked brand mentions

Sources

• Ahrefs, An Analysis of AI Overview Brand Visibility Factors (75K Brands Studied): ahrefs.com/blog/ai-overview-brand-correlation/
• SuperMe AI profiles of Ethan Smith, Kevin Indig, Alex Halliday and Eli Schwartz, answers collected 6 October 2026 (AI summaries of each expert’s published work, not the experts themselves)
• Rank Dough first-party citation test, custom jersey vertical, 1 and 7 September 2026

The post Do Unlinked Brand Mentions Get You Cited in ChatGPT? What the Data Shows appeared first on Rank Dough Digital.

This is a content gap analysis I ran on a real site from start to finish: trackbarn.com, a US track and field retailer, from one keyword export to 455 published question pages, 1,939 ranking queries of which 922 were new, 10 to 145 keywords in AI Overviews, and the third of the pages I would not build again. The step where I made that mistake is shown, not hidden, because Rank Dough gets hired for the method and the method includes the fixes.

What did the site look like at the start?

February 2026. A Shopify store for track and field equipment, three brands under one owner, just redesigned.

Search Console showed clicks down from around 150 a day to single figures after the redesign, with product pages that used to rank removed in the process. Ahrefs had the site at 161 ranking keywords in January.

The first decision, which is on my February update to the owner, was to restore the removed product pages before building anything new. A page that ranked once gets its traffic back faster than a new page earns it.

That is step zero of every gap analysis we run. Read the site, list what it sells, find what it used to rank for. The export comes after.

Source: Rank Dough monthly client update recording, TrackBarn, February 2026; Google Search Console, trackbarn.com

Step one: how was the keyword universe built and split?

Inputs: a client questionnaire, the site itself, an Ahrefs organic keywords export for the category, and Search Console queries. The system expanded seed terms with modifiers (event, age group, level) into a keyword universe, then applied the one rule that does most of the work: questions become articles, generic terms become collection pages, never the other way round.

“Vaulting poles” went to the collection list. “What is a good 5k time for cross country” went to the article list.

The generic terms were the pages that sell. The questions were the pages that could rank fast.

Keeping them apart stopped the system writing a 2,000-word guide to vaulting poles that would have competed with the collection and answered nothing. The keyword method in full is in from 6,000 keywords to 12 silos.

Source: Rank Dough content system build record, questions-versus-generic rule, 6 March 2026

Step two: how were the questions clustered and deduplicated?

Questions were clustered into silos capped at 12, grouped the way a reader groups them: cross country, pole vault, throwing, sprints, uniforms, and so on. I checked the silos by hand, because at scale the clustering had thrown baseball in with dental implants on another project and had dropped terms that were in the source file.

Dedupe ran twice before publishing: on the keyword list, and against the site’s existing titles and headings so nothing got briefed that already existed. The third pass, on search-result overlap, cannot run until pages have ranked, and it is the one that catches “can I” versus “when can I” pairs. Why we keep that order is in dedupe three times.

Source: Rank Dough content system build record, clustering cap and batching, May 2026

Step three: what did each idea carry before writing?

Table 1. What each candidate page carried into the queue. The last row was added after the results came in.

Source: Rank Dough content system build record, value promise (7 April 2026), information-gain gaps per angle (15 April 2026)

Step four: what did generation and publishing look like?

Pages were written in volume order in programmatic mode: 100 to 500 words, direct answer with figures first, one table, five FAQs, a source line per section, a CTA block pointing at the matching collection, CSV out to Shopify. Every page passed an independent check that read the finished HTML for the figure count, the sources and the links before it went live.

The writer never signed off its own work. That check is written up in the writer never grades itself.

455 pages went live between March and June 2026, alongside the restored product pages and rewrites of existing content that already performed. On the June update I said improving Karl’s existing content would get to results quicker than writing new. It did.

Source: Rank Dough monthly client update recordings, TrackBarn, April and June 2026

What did the data show after six months?

• Ranking queries in Search Console, 28 days to 22 September: 1,939. 922 of them had zero impressions in the same window a year earlier.
• Page-one keywords in Ahrefs: 103 in September 2025, 1,667 in September 2026. Top 3: 22 to 643.
• Keywords with an AI Overview: 10 in February, 83 in April, 145 in June. 40 percent of everything the site ranked for.
• Impressions: 113,040 to 1,581,842. Clicks: 1,121 to 6,095.
• Homepage clicks: 546 to 310. Pole collections: 31 to 2 and 23 to 0.

That last line is the one that matters if you are deciding whether to run this on your own site. Traffic rose five times.

The pages that sell lost clicks. Whether the question pages caused that is not proven; the homepage improved in position while losing clicks, which points at the results page rather than cannibalisation. But it showed me the step that was missing.

Source: Google Search Console, trackbarn.com, 28 days to 22 Sep 2026 vs 2025; Ahrefs keywords history, trackbarn.com; monthly client update recordings, TrackBarn, Feb to Jun 2026

What went wrong, and where in the process?

I scored buyer distance after the pages ranked, not before they were briefed. Applied in hindsight, roughly a third of the 455 pages sit at distance 3: the searcher is a parent or a school runner, and the client sells $800 poles.

“What is the javelin world record” is the clean example. 58,339 impressions, 67 clicks, no customers. The pages did exactly what they were built to do. I built them for the wrong readers.

The fix is a position in the pipeline, not a new rule. The score now sits between the silo review and the research brief, because once a brief exists the page gets written. The rest of the method is unchanged, and the version with the score in the right place is in content gap analysis that finds buyer questions.

Two other things the data forced. 98 of the 455 pages have zero clicks and are being pruned. And 61,996 impressions came from India for a store that ships in the US, so the country mix is now a monthly check.

Source: Google Search Console, trackbarn.com, Pages and Countries reports, 28 days to 22 Sep 2026

What does the monthly routine look like now?

Four checks, same order every month, and they take about an hour with the exports open.

• Position and click rate per page, side by side. Rising position with falling clicks means the results page is answering above the result.
• Clicks on the money pages year over year, before I look at anything else.
• Duplicate pairs: any page that lost position while a sibling with overlapping queries exists. Merge, 301, move on.
• Pages that held a top-3 position last month and return no URL this month. Restore before building.

What would I do differently on the next site?

1. Score buyer distance before the brief. Cut the 3s before research starts.
2. Restore and improve what already ranks before writing new, as I did here, but budget it from day one.
3. Give every distance-1 page its conversion element at brief stage: the collection link, the sizing block, the price table.
4. Run the third dedupe pass and the zero-click prune monthly from month three.
5. Report clicks on money pages beside the content numbers from the first report, so the gap shows in month two, not month six.

What makes each page worth ranking once the gap is chosen is in information gain. How the client’s own facts get quoted by AI answers is in proprietary data and AI citations. See what you think.

FAQ

Sources

• Google Search Console, trackbarn.com, Queries, Pages and Countries reports, 28 days to 22 September 2026 versus same period 2025
• Google Search Console Help, Performance report
• Ahrefs, keyword cannibalisation

The post A content gap analysis on a real site, start to finish appeared first on Rank Dough Digital.

Information gain is what a page adds beyond what the other pages ranking for the same query already say. We treat it as a count, not a feeling: five sourced figures per article, three of them in the first 30 percent, and every paragraph carrying a number, a named failure, a distinction or a claim against the consensus.

On dentaltourismalbania.com every FAQ page at position 1 to 3 opens with a figure. Every page at 8 or 9 opens with reassurance. That is the whole article in two sentences, but the data is worth a look.

What does information gain mean in practice?

Ten pages, same question, same facts. An engine has nothing to choose between them.

An AI answer squashes all ten into one paragraph and names one or two. Information gain is the measure of what one page adds to that pile.

Google’s guidance on Google Search Central, creating helpful, reliable, people-first content asks whether a page provides substantial value compared with the other results, which is the same question asked from the reader’s side.

For one writer, “add something new” is a fine instruction. For a system putting out hundreds of pages it is not checkable, so I made it a count.

A data point is a figure with a unit and a source I can name. “Implants are more affordable abroad” is a claim.

“Implants in Albania cost 60 to 80 percent less than in the UK” with the source under it is a data point. Five of those per article or the article goes back.

Source: Rank Dough content system, information-gain rule, raised from three to five data points on 29 May 2026

Why is the floor five, and why do three come early?

The floor used to be three. In May I went through the dental aftercare pages and the pattern was hard to miss.

The pages holding positions 1 to 3 had figures in the opening. The pages drifting from position 2 down to 5, 7, 9 had their figures, if they had any, buried in the middle behind an opening that told the reader not to worry.

So two things changed on 29 May. The floor went to five.

And I added a placement rule: three of the five sit in the first 30 percent of the text, because that is what a reader sees before they decide to stay and that is what an answer system lifts. A figure at the bottom of 1,500 words is invisible to both.

The check runs after generation on the finished HTML by a pass that did not write the page. Short count or wrong placement, the article comes back with the section flagged.

The rule itself is written up in the five-data-points article. This page is about what it did.

Source: Rank Dough content system build record, 29 May 2026

What does the ranking data show?

Table 1. Ahrefs top pages, dentaltourismalbania.com, 22 Sep 2026. “Opens with” is my reading of each page’s first paragraph.

It holds across the top ten. Every page at 1 to 3 gives a number or a straight answer in its opening.

Every page at 8 or 9 opens with the consensus. The three in that second group were written before the floor went up and are being rewritten now.

I will publish the before and after once it has three months of data behind it. Not before.

Source: Ahrefs Site Explorer, top pages, dentaltourismalbania.com, 22 September 2026

Did the rule move the site, or did something else?

Top-3 keywords went from 12 in September 2025 to 593 at the July 2026 peak. The long tail at position 11 or worse fell from 884 to 59 in the same year.

I am not going to tell you the information-gain rule did that by itself. The answer-first opening rule and the five-FAQ minimum went in during the same window and I cannot pull the three apart on this data.

What the data does support: no page generated before the floor went up holds a position-1 ranking on the site today, and every position-1 page was generated after it. That is a correlation with a mechanism behind it. I will call it that and nothing more.

Source: Ahrefs Site Explorer, keywords history by position band, dentaltourismalbania.com, monthly, Sep 2025 to Sep 2026

Where do the figures come from?

A rule that demands five figures is an invitation to make them up, unless you control where they come from. Every article in our system has a research brief produced for that one question, and the brief is the only place facts may come from. The writer sees the relevant passages for the section it is writing, not the whole document, so the figures land where they belong instead of drifting to the end.

Each figure carries a source line under the section that uses it. The article ends with at least four external references.

The client’s own domain is never one of them. And when the brief has no figure for a claim, the page says “no public data” instead of inventing one.

That refusal is the single thing that separates this from every mass-content tool I have tested, and it is the reason a marketplace page can sit above the NHS on a medical query.

Source: Rank Dough content system, sourced-figures and references rules, enforced from 8 June 2026

How does the rule change what gets written?

The five-point floor kills a whole category of article before it starts. “Everything you need to know about dental implants” cannot meet it, because no brief on a topic that wide has five figures that belong in one opening.

“How much do implants cost in Albania compared with the UK” meets it in the first paragraph. So the rule pushes every idea toward a question specific enough to answer with numbers, which happens to be the kind of question a buyer asks late in a decision. The gap-finding method that produces those questions is in content gap analysis.

It changes titles too. “Screwless dental implants: what are they?” became “Screwless dental implants don’t exist: what clinics mean when they say it.” The second title makes a claim the article then has to earn. The seven checks behind that are on the non-commodity test page.

What does a page with no gain look like, honestly?

I know because I have published them. The TrackBarn page on the javelin world record is one.

It has a number, it ranks at position 8, it took 58,339 impressions in 28 days, and it adds nothing a reader could not get from the results page itself, which is why 67 people clicked. Gain is not just having a figure.

It is having a figure the other pages do not, that a reader needs to act. That page has the first and not the second. It is on the prune list.

Source: Google Search Console, trackbarn.com, Pages report, 28 days to 22 Sep 2026

What should you check on your own site?

• Open your five best-ranking informational pages. Count figures with units in the first 30 percent. Fewer than three and the opening is prose, and prose loses extraction.
• Check where each figure came from. If you cannot name the source, neither can an answer engine, and it will cite a page that can.
• Compare your pages at 1 to 3 with your pages at 6 to 10 on the same site. On both sites in this article the difference is figure density, not word count and not links.
• Once the figures are in, phrase them so an AI answer lifts them with your name attached. That is the next article.

FAQ

Sources

• Ahrefs Site Explorer, top pages, dentaltourismalbania.com, 22 September 2026
• Google Search Central, creating helpful, reliable, people-first content
• Ahrefs, keyword cannibalisation

The post Information gain: what makes one page win when ten say the same thing appeared first on Rank Dough Digital.

Proprietary data is the one thing an AI answer cannot write without you, and it only earns a citation when it is phrased for lifting. In the test I ran in September 2026 the pages ChatGPT cited had turnaround, minimum order, origin and audience as plain facts in their first 50 words.

The pages that merely ranked in Google were product grids whose extracted text read “add to cart”. On trackbarn.com, pages built from the retailer’s own figures took the site from 10 to 145 keywords in AI Overviews in four months. Rank Dough builds every client page this way now.

What counts as proprietary data for a small business?

You do not need a research department. Any business that trades already holds facts no competitor can publish.

• Operational: turnaround, minimum order, where things are made, what a reorder costs, how long a treatment takes to settle.
• Behavioural: what gets bought together, which size runs out first, which questions hit support most. All sitting in your orders and tickets.
• Measured: Search Console exports, conversion rate by page, what happened after a change you made, with the date.
• Collected: reviews across markets. I built a tool that scraped clinic reviews across Turkey, Poland and Albania so a dental client could put a per-country rating on its pages that no competitor had.

One test for each: could a competitor write this sentence without asking you? If not, it is the sentence your page should open with. Which gaps deserve a page in the first place is decided earlier, in content gap analysis.

Source: Rank Dough content method; Dental Review Insights tool, built April 2026

Why does ranking in Google not get you cited by ChatGPT?

Because they pick differently. In September 2026 I ran six buying prompts for a custom-jersey retailer through ChatGPT.

The retailer ranks first in Google for its main terms. ChatGPT cited it zero times, and across all six prompts not one of ChatGPT’s sources appeared in Google’s top ten.

Zero overlap. I ran it twice to be sure I had not fumbled a prompt.

Then I scored the 15 pages that were cited against 8 pages that rank in Google, on 22 attributes. The difference was not authority.

The cited pages carried a fact in the first 50 words: turnaround, minimum, where it is made, who it is for. The same facts sat in their H2s and meta descriptions.

The ranking pages were product grids, and when you extract their text what comes out is navigation. A machine cannot quote a cart. It can quote “custom jerseys ship in 10 days with a minimum of 12”.

Source: Rank Dough citation test, September 2026: six prompts, 15 cited pages vs 8 control pages, 22 attributes; one run, replication scheduled

How do you phrase the fact so the citation carries your name?

Four rules. Every one of them came from the pages that got cited, not from a guide.

1. First 80 words. The answer with the figure comes before any context. Retrieval scores passages, not pages. A page that opens with history has handed the extraction to the page that opened with the number.
2. Self-contained sentences. No sentence in a body section leans on the one before it. No “This is why”. No “These are the”. A sentence that starts with a pronoun has no subject once it leaves the page.
3. Brand as the subject. “TrackBarn ships vaulting poles in 10 days” travels with the data when it is quoted. “The TrackBarn shipping guide” does not; the brand turns into a possessive and the citation goes anonymous. The brand is the grammatical subject in the opening, in one heading and in the close.
4. Method sentence. One line after the first data section: “This data was compiled from Google Search Console exports for the 28 days to 22 September 2026.” Original data with a disclosed method is the kind a model can attribute.

Table 1. The four phrasing rules, weak and citable versions. Figures in the right column are illustrative unless sourced elsewhere on this page.

Source: Rank Dough content system, extraction and ghost-citation rules, in force since May 2026

What happened when I used a client’s own figures this way?

TrackBarn sells track and field equipment. The question pages I built for it from February 2026 carried the retailer’s own facts: shipping windows, minimums, what fits which age group, measured from the store, not lifted from guides.

Keywords with an AI Overview went from 10 in February to 15 in March, 83 in April and 145 by June, which was 40 percent of the 363 keywords the site ranked for. Total keywords went from 161 in January to 363 over the same months.

I said on the June update that it was moving up nicely. It was.

Two things the data does not say, so I will not either. It does not say those Overview placements sent clicks; the position-versus-click-rate pair for this site is in the position up, CTR down article and it is not pretty.

And it does not separate proprietary data from the other rules I applied in the same months. What it shows is that pages built from a client’s own figures entered the AI layer at a rate the old content never did.

Source: Rank Dough monthly client update recordings, TrackBarn, April and June 2026, figures as reported from Ahrefs; Google Search Console, trackbarn.com

How do you measure whether it worked?

Separately. Never blended. Three numbers, each with its own source.

• AI referrals. GA4, Source / Medium, filtered to chatgpt.com, perplexity.ai, copilot and the rest. I build it as a Looker Studio filter because the tools do not tag their traffic consistently, and I check that each one I care about is actually arriving with a referrer.
• Citation rate. A fixed prompt set per client, run five times per engine across several days, logging which URLs get cited. One screenshot of one conversation is a conversation, not a measurement.
• Overview presence. Keywords with an AI Overview where the site appears, from Ahrefs or Search Console, tracked monthly.

An agency that sends you one “AI visibility score” has blended three things that move for different reasons. Ask for the three.

Source: Rank Dough client process recording, AI traffic acquisition in GA4, 2026; Rank Dough citation test design, September 2026

What goes wrong when people try this?

Three things, all of which I have done.

• Putting the data on the page but not in the first 80 words. The brief has the figure, the writer buries it under context, the extract never sees it. This is why the placement check exists.
• Letting the brand become a possessive. “Our guide shows” reads fine to a human and loses the name in every quote. I now check for the brand as subject in the opening, one heading and the close on every page.
• Publishing a figure that was estimated, not measured. It gets quoted, repeated, and attributed to you. If the number is not measured, the page says “no public data”. A gap costs a bit. A wrong number costs the lot.

What should you do with your own data this month?

1. List ten facts only you can state: turnaround, minimum, origin, what fails and how often, what customers ask most.
2. Put one in the first sentence of the page that answers it. Brand as subject. Source line under it.
3. Add the method sentence after the first data section.
4. Set up the GA4 AI-referral filter and a ten-prompt log. Run both before you change anything, so you have a baseline.
5. Check the page passes the figure count in information gain and the paragraph test on the non-commodity content page.

Give me a shout if any of it is unclear.

FAQ

Sources

• Rank Dough citation test, September 2026: six buying prompts in ChatGPT, 15 cited pages scored against 8 Google-ranking pages on 22 attributes (one run)
• Rank Dough monthly client update recordings, TrackBarn, February, April and June 2026
• Google Search Central, creating helpful, reliable, people-first content

The post Proprietary data: the facts an AI cannot write without you, and how to get them cited appeared first on Rank Dough Digital.

At Rank Dough we run content gap analysis as a hunt for buyer questions nobody answers with a figure, not as a list of keywords a competitor ranks for. On trackbarn.com that gave me 455 question pages ranking for 1,939 queries inside six months, 922 of them queries the site had never ranked for.

It also gave me a third of pages I would not build again. Both numbers are in here.

What is a content gap, and what is not?

A content gap is a question a buyer asks before they buy that nobody answers with a specific, sourced figure. That is the whole definition.

It is not a keyword your competitor ranks for and you do not. A tool export with 4,000 of those is a list of other people’s pages. Most of them are either answered badly by everyone or asked by nobody who will ever buy.

The two produce different pages, which is why I care about the difference. Chase the keyword list and you get a definition and a bulleted guide for every term.

That is the most interchangeable page on the internet. Chase the unanswered question and you get a page with a number in its first sentence.

That is the page that ranks and gets quoted. Our rule is simple: if we cannot answer it with a figure, we do not write it until I can.

Source: Rank Dough content method; the questions-versus-generic rule has been hard-coded in our content system since 6 March 2026

Why does the tool export mislead?

Because it measures overlap, not need. A competitor ranking for “best softball jerseys” tells you a page exists.

It does not tell you the page answers anything. Three things go wrong in every export I have worked from.

• Volume with no buyer behind it. “What is the javelin world record” pulled 58,339 impressions in 28 days on TrackBarn. 67 clicks. Not one of those people needs an $800 vaulting pole. I built that page. It ranks. It should not exist.
• Generic terms dressed up as topics. “Dental implants” is a category page, not an article. Write an article from it and you get a definition followed by a list, which an AI answer summarises and never cites.
• Duplicates in disguise. “Can I eat chocolate after tooth extraction” and “when can I eat chocolate after tooth extraction” look like two gaps. They are one. Publishing both cost Dental Tourism Albania position 1 on the first; it was at 9 by September. Google Search Central, consolidate duplicate URLs covers how Google resolves pairs like that.

Source: Google Search Console, trackbarn.com, 28 days to 22 Sep 2026; Ahrefs top pages, dentaltourismalbania.com, 1 Jul vs 22 Sep 2026; Google Search Central, consolidate duplicate URLs

How do I find the real gaps?

Four steps, in this order, and we do not open a volume column until step four is done.

1. Read the client’s site first. Homepage, prices, policies, service pages. Every gap page has to land the reader one click from something the client sells. If I cannot connect a question to a product, I do not fill it.
2. Scrape three competitors and write down what they all say. That shared material is the consensus. In the finished page it gets one sentence, no more.
3. List what a buyer asks next and cannot find. Cost, turnaround, minimum order, what goes wrong, which category fits them. I add these rows even at zero search volume, because they are exactly the questions an AI answer expands a prompt into.
4. Split questions from generic terms. Questions go to the article list. Generic terms go to collections and landing pages. I built that split into the content system on 6 March because every article I ever wrote from a generic term came out as a commodity page.

Then, and only then, we pull volume, difficulty and current rankings from Ahrefs. They order the queue. They never decide what gets in.

Source: Google Search Console, trackbarn.com, Queries report, 28 days to 22 Sep 2026 versus same period 2025

How does buyer distance decide what gets written?

Buyer distance is the number of decisions a searcher still has to make before they could buy from the client. We score it 0 to 3 per question, before the research brief, because once a brief exists the page gets written. That is how content operations work and pretending otherwise wastes money.

Table 1. Buyer distance applied to real queries from trackbarn.com and dentaltourismalbania.com.

This score explains the one result in our data that content quality cannot. On Dental Tourism Albania the aftercare pages sit at positions 1 and 2 and the homepage gained 93 estimated visits over the same period.

A patient who just had an extraction is one decision away from choosing a clinic. On TrackBarn the same template put pages at positions 3 to 6 and the homepage lost 43% of its clicks year on year.

Parents asking how far middle-school cross country runs do not buy poles. Same system. Different distance.

Source: Ahrefs top pages, dentaltourismalbania.com, 1 Jul vs 22 Sep 2026; Google Search Console, trackbarn.com, Pages report, 28 days to 22 Sep 2026 vs 2025

What makes the filled gap useful rather than just present?

Google’s guidance on Google Search Central, creating helpful, reliable, people-first content asks whether a reader leaves feeling they learned enough to get what they came for. We turn that into a test that runs twice, before and after writing.

• Before the brief: one sentence saying what the reader will be able to judge after reading. “After this page the reader can tell whether a 48-hour turnaround is realistic for 12 jerseys.” If I cannot write that sentence, I do not brief the page.
• After generation: every promise is checked against the text, point by point, by a pass that did not write it.
• No hedge without a number. “Typically”, “usually” and “depends” are allowed only in a sentence that also carries a figure.
• When the figure does not exist, the page says so. A visible gap costs a bit of credibility. An invented price costs all of it. On a medical page it turns into a one-star review, which is why the implant pricing brief I wrote for Dental Tourism Albania asked for every single price to be verified before anything went live.

Source: Google Search Central, creating helpful, reliable, people-first content; Rank Dough content system value-promise check (19 Feb 2026); client brief recording, implant pricing article, 2026

What did the method produce?

Two results from the last six months, windows stated.

• trackbarn.com: 455 question pages from one keyword export, ranking for 1,939 queries in the 28 days to 22 September 2026. 922 of those had zero impressions a year earlier. Page-one keywords in Ahrefs went from 103 to 1,667 in twelve months.
• proplayerteam.com: an FAQ hub built on the same atomic-answer rules straight after the Shopify migration was ranking for 81 keywords within the first month, with 150 pages live, while the rest of the site was still climbing out of the migration dip.

And the honest line. Roughly a third of the TrackBarn pages sit at buyer distance 3.

They rank. They were not worth writing.

The score now runs before the brief, not after the data comes in. The full walk through that project, export to published pages, is in the worked example.

Source: Google Search Console, trackbarn.com; Ahrefs keywords history, trackbarn.com, Sep 2025 to Sep 2026; monthly client update recording, ProPlayerTeam, April 2026

Why does this matter more now than two years ago?

Because the search results page answers the easy questions itself. A definition, a distance, a date: Google shows it above the results and an AI assistant says it in one line.

The click for those is gone, and I have the numbers to show it. TrackBarn’s mobile position went from 12.5 to 6.2 in a year while its click rate fell from 1.92% to 0.49%. Rankings up, clicks down, same site.

What still earns a visit is the question the results page cannot answer because the data is not public: your price, your turnaround, your minimum, what breaks and how you fix it. Those are the gaps. They are also the only pages a machine can quote with your name attached, which is where proprietary data and AI citations picks up.

Source: Google Search Console, trackbarn.com, Devices report, 28 days to 22 Sep 2026 vs 2025

What should you run on your own export this week?

1. Open the client’s site and list what it sells, with prices and turnaround. Keep that list beside the export.
2. Delete every generic term from the article list. Move them to collections.
3. For each remaining question, write the buyer distance beside it. Cut the 3s.
4. For each 0 and 1, write the one figure the page will give that no competitor gives. If it is not in your data or a checkable source, mark the gap as open and move on.
5. Write the reader outcome in one sentence. Then brief.

What goes into the page once the gap is chosen, and how many figures it needs, is in information gain. The paragraph-level test every page passes before it ships is on the non-commodity content page. See what you think.

FAQ

Sources

• Google Search Console, trackbarn.com, Queries, Pages and Devices reports, 28 days to 22 September 2026 versus same period 2025
• Google Search Central, creating helpful, reliable, people-first content
• Ahrefs, Content Gap tool

The post Content gap analysis that finds buyer questions, not keywords appeared first on Rank Dough Digital.

Rankdough creates non-commodity content by building a data source first and writing second. In this travel example, an agent visits 200 Barcelona hostels through Google Maps, logs six dated observations per property, and that log feeds pages for a query cluster whose head term, “best hostels in Barcelona”, gets 27,000 global searches a month at keyword difficulty 0 (Ahrefs, 4 October 2026).

No competitor holds the same numbers, so Google and AI assistants have a reason to pick our page over the other ten.

What is non-commodity content?

Non-commodity content is a page whose core input a competitor or a language model can’t reproduce. If you could write the page from the top ten results, it’s commodity, however long or well edited it is.

You get a non-commodity input from one of four places:

1. Proprietary data. Orders, outcomes, prices, response times, observations you logged yourself.
2. Direct experience. You did the thing, and you have photos, numbers and the mistakes to show it.
3. Access. Interviews, expert quotes, a community you run.
4. A defended position. A contrarian view with evidence behind it and a name on it.

Google calls the extra value “information gain”. Ethan Smith of Graphite describes the method as converge, then diverge: cover what competitors cover, then add something they don’t have (Maven). Most content teams stop after converging.

Why does commodity content stop working?

Commodity content loses traffic once Google’s quality systems re-evaluate it, and AI assistants have no reason to cite one copy over another. Lily Ray tracked 220+ sites that were public customers of AI content tools and measured each site’s drop from its peak organic traffic.

Source: Lily Ray, published May 2026, Ahrefs data checked against SISTRIX (Search Engine Journal).

Method: Lily Ray identified the sites from public customer lists of AI content platforms and compared each site’s organic traffic with its peak in Ahrefs, checked against SISTRIX.

AI writing the words didn’t sink those sites. Volume did: thousands of pages carrying nothing the web didn’t already have, at a scale Google’s scaled-content systems notice.

How do you find a data source nobody else has?

Start with the question your audience asks that no ranking page answers with a number. Then find a source you can turn into that number at a cost competitors won’t pay.

For solo travellers in Barcelona, the unanswered question is concrete: what does the walk from the night bus to my hostel look like at 2am? Every safety guide repeats the same city-wide advice. None measures the last 400 metres.

Three tests decide whether a source is worth building:

1. It answers a real question with a number. Minutes, counts, shares, dates.
2. It costs effort to collect. If a competitor can copy it in an afternoon, it’s commodity next quarter.
3. It gets better with time. A dataset you refresh builds a history that nobody can rebuild later.

Google Maps passes all three. The imagery and routing are public, but nobody has scored 200 hostel approaches against a fixed checklist, dated every image and checked a sample by hand.

How did Rankdough build the Barcelona agent?

Rankdough built an observation agent with a traveller’s profile: someone arriving alone, late, with a bag. The profile decides the checklist, and the agent runs the same checklist at every property:

1. Time the walk from the nearest night-bus stop and metro entrance at a 02:00 departure.
2. Classify the last stretch of street: main street, side street or passage.
3. Look for street lamps and a 24-hour reception sign.
4. Check whether the door is visible from the corner.
5. Record the capture date of every image and flag anything it couldn’t see.

Change the profile and the checklist changes with it. A wheelchair user’s agent counts steps and looks for ramps; a family’s agent checks lift signage and pushchair access. The pipeline stays the same.

What does the dataset look like?

Each visit becomes rows in the agent’s log, and the log is the only thing the agent remembers. The table below is an illustrative example of the format; the Barcelona pilot hasn’t collected real observations yet.

Source: illustrative example of the Walk the Map log format, Rankdough, October 2026.

Agent method: the agent scores Street View imagery against the checklist in two independent passes, times walks with the Routes API, and a person checks every disagreement plus a random sample of 20 properties.

The agent never edits a past row. When it goes back next quarter, it writes new ones, so the log turns into a history: a hostel that gained a reception sign, a street with new lighting, a property that closed. That history is the part competitors can’t buy.

Can readers ask the agent directly?

Yes. The agent answers questions from its log, so readers get a conversation that stays inside the evidence. An illustrative exchange:

You: What’s the walk like from the night bus to Example Hostel B?

Agent: Eleven minutes from the nearest night-bus stop at 02:00. The last stretch is a passage under three metres wide. I couldn’t make out street lamps in the imagery, and I saw no 24-hour reception sign. The images are from November 2024, and no person has checked this one yet.

You: Would you feel safe walking it?

Agent: I can’t tell you that. I haven’t walked it at night, and daytime images don’t show how busy or lit a street is at 2am. If you’re arriving late, the hostel I visited in Gràcia has a main-street approach, a reception sign and a six-minute walk from the bus.

Each sentence the agent says points to a row in the log. That traceability is what you’d want a journalist, a forum moderator or an AI assistant to see before they cite you.

How does the agent collect each observation?

The agent works through one visit in six steps, and every step writes to the same log:

1. Find the property address with the Places API.
2. Look at the street with the Street View Static API: three headings at the entrance and one from the nearest corner, each with its capture date.
3. Score the images against the checklist in two separate vision passes.
4. Send disagreements to a person; agreements go straight into the log.
5. Time the walk from the nearest night-bus stop and metro with the Routes API at a 02:00 departure.
6. Store image date, model version and checker on every row. The agent and the pages read only from those rows.

Where is the line between original data and fabricated experience?

The agent knows what it observed, not how a place feels. That boundary is what makes its answers worth citing.

It can tell you:

• What the approach and the door looked like, and the date of the image.
• Whether street lamps and a reception sign were visible in that image.
• Walking times the routing engine returned, and when it asked.
• Where it couldn’t see anything, which is a finding in itself.
• Whether a person has checked the observation.

It won’t tell you:

• Whether a street is safe, busy or well lit at night. Daytime imagery can’t show that, and it says so.
• Current conditions. Street View imagery can be two to four years old, so every answer carries the date.
• Anything phrased as lived experience. It says “in imagery from March 2025”, never “when I walked there”.

An agent that invents memories produces what any model can produce, so it has no scarcity, and presenting it as testimony runs against Google’s guidance on first-hand, people-first content. An agent that records dated observations produces something only its owner holds.

Which search terms does the data target?

Rankdough doesn’t publish the log as a log. The data feeds pages built for the queries travellers type, and the numbers decide which pages earn the build.

Source: Ahrefs Keywords Explorer, pulled 4 October 2026. Keyword difficulty not reported for “hostels barcelona”.

The table points to three pages, each with a different job:

1. The head term. “Best hostels in Barcelona” is a list page. A list backed by 200 measured night walks is the only one on that results page with information gain.
2. The parent topic. “Is Barcelona safe for solo female travellers?” gets answered with district-level numbers from the log instead of the usual Las Ramblas warning.
3. AI citations. Long questions such as “hostel in Gràcia on a main street near the night bus” go to ChatGPT and Perplexity, where only 12% of cited sources sit in Google’s top 10 (Ahrefs). AI questions average about 23 words against 6 on Google, according to Ethan Smith (Maven).

We checked accessibility terms too and dropped them. At 30 US searches a month for the top query, the cluster can’t justify its own page.

Mentions do the rest. Ahrefs studied 75,000 brands and measured how three signals correlate with AI Overview visibility.

Source: Ahrefs study reported by Search Engine Land.

A dated city report with a published method gives travel forums, hostel associations and YouTube creators something specific to mention. Rankdough treats each quarterly re-run as a new edition and a new reason to cite; we’ll report whether mentions compound once two editions are live.

What does a data source like this cost to build?

One city costs less than a day of consulting. The figures below are estimates until the pilot is billed.

Source: Rankdough build estimate, 4 October 2026, before Google Maps Platform billing.

Before anything goes live, read the current Google Maps Platform terms on storing Places content. The agent keeps its own observations, never Google’s review text. Show Street View attribution wherever an image appears, and give property owners a way to correct an observation, with the image date beside it.

How can you apply this to your own niche?

The same five steps work for any site, with or without an agent:

1. Write down the question your buyers ask that no ranking page answers with a number. For a team-uniform store, it’s real turnaround by sport and month. For a dental clinic abroad, it’s the full cost including flights and nights.
2. Find the source. Your orders, quotes, support tickets, returns, or a public source nobody has cleaned up.
3. Fix the checklist. Same fields, same method, every time. Publish the method.
4. Date everything and re-run it. A history beats a snapshot.
5. Write the pages the queries ask for, with the numbers in the first paragraph and a source line under every table.

Rankdough runs this process for clients whose content has stopped earning traffic or citations. We start with the question, not the keyword list.

FAQs

Sources

• Search Engine Journal: Lily Ray, AI content tools and traffic loss, May 2026
• Google Search Central: Creating helpful, reliable, people-first content
• Ahrefs Keywords Explorer, Barcelona solo-travel and hostel queries, pulled 4 October 2026

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