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.