Forty rows in a spreadsheet, sorted descending. The top of the list is publications that will not reply, the middle is trade sites somebody has heard of, and the bottom is a handful of small blogs that got in because one of them sends traffic. A quarter of outreach is about to run against that order, and the order came from one column.
The column is domain rating, or domain authority, or authority score, depending on which tool produced the sheet. It is a reasonable thing to sort by if the goal is a backlink, because that is roughly what it was built to predict.
The goal here is different. The goal is that when a buyer asks an engine which software to use, the answer is assembled partly from pages that mention you. So the question to ask of the sheet is which of these forty pages an engine pulls when that question gets asked, and nobody has asked it.
Your Target List Is Sorted By A Number Nobody Checked Against The Answers
The useful sort key has a plain name. Citation weight is how often an engine pulls a given source for the questions your buyers ask, relative to how many answers it gives. It is a count you can take, not a score anybody sells.
That is a different unit from the one most target lists are built on. We treat a mention as being referenced on a reputable page, with a link or without one, which is broader than a backlink and closer to what the engines seem to read. A page that names you and does not link to you still shapes the answer when the engine pulls it. We have written separately on mentions against backlinks as units.
The mismatch is easier to see when the columns are laid out against the job each was built for:
| The column | What it was built to predict | What a mention list uses it for |
|---|---|---|
| Domain rating / authority score | How much ranking strength a link from this page passes | Whether an engine will pull this page |
| Referring domains | How many sites link here | The same |
| Organic traffic | How many people arrive from search | The same |
| Citation weight | How often an engine pulls this page for your buyers’ questions | The same |
Three of those four were designed to answer a question about links, and are being asked a question about answers. The fourth was built for the question being asked. Only one of them has ever been checked against a real answer in your category.
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A Site With An Authority Score Of 34 Collected Seven Hundred Citations
In September a publisher posted his own figures in r/SEO, against himself. Three-month-old domain, authority score of 34, a hundred and seventy-nine referring domains, most of the growth in the previous six weeks.
“Cited pages sit at roughly 700. Brand mentions sit at five. That’s about 140 citations per single mention. The split is 659 ChatGPT, 32 AI Overviews, 22 AI Mode, zero Gemini.” — u/Proper-Neighborhood6, r/SEO, Sep 2026
An authority score of 34 is the kind of number that puts a site at the bottom of a target sheet, or off it. Seven hundred cited pages is the kind of number the top of the sheet is supposed to produce.
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The last clause of his figure is worth a second look. Zero on Gemini, six hundred and fifty-nine on ChatGPT, from the same pages on the same site. The engines do not share an index, so a brand is not cited or uncited in general.
That makes “are we getting cited” the wrong shape of question before it is even the wrong metric. A single number averaged across engines hides the only thing you could act on, which is that one engine is reaching for your category’s pages constantly and another is not reaching for them at all. Those two facts call for different work, and an average calls for none.
Cited Pages Do Skew High-Authority, And That Is Still Not The Lever
Here is the part that argues against us, and it is better met now than later. Pull a list of pages an engine has cited in your category and those pages will skew toward bigger domains. AuthorityTech, reading Ahrefs’ own citation inventory, put it carefully: a distribution of cited pages is not evidence that the authority metric causes the citation.
The reason the skew exists is dull. Good pages tend to rank, pages that rank tend to get retrieved, and pages that rank tend to sit on domains that have been publishing for years. Authority correlates with all three without being read by anything.
So sorting by it gets you to the right pages by accident. The accident holds often enough to feel like a method, and it fails in three places that are exactly the places a mention program lives:
Each of those is a row the sort key puts in the wrong place, and the first and third are the rows a competitor without your budget can win.
What Happened When We Put The Metrics Side By Side
Empact Partners builds mention target lists inside Generative Engine Optimization, which is the workstream aimed at getting a brand into the answer when somebody asks an engine what to buy in its category. The measurement behind the sort key comes from Qvery, our sister company, which tracks what the engines answer and what they cite.
In September it tested the suspicion directly, on the SaaS brands ChatGPT already named. It put three authority measures against one other thing, which was simply whether a brand appears on the roundup pages the engine was citing for those questions.
Roundup presence tracked naming more closely than any of the three authority measures. Backlinks rank came second, a long way behind. Organic traffic was the weakest of the four. Referring domains landed between them, and the comparison against it did not clear the bar Qvery set in advance, so that one stays open rather than settled.
Both halves of that are ours, so discount the finding as far as you think an interested party deserves, and ask us for the answers it was counted from.
The limits are Qvery’s own and they matter enough to list rather than footnote:
Being Pulled And Being Named Are Two Different Things
The publisher’s ratio of roughly a hundred and forty citations per named mention is not a failure. It is two mechanisms being counted as one, and the top reply in his thread separates them better than our own notes do.
“The citation count is a retrieval signal, your content is getting pulled because it ranks and matches the query, that part your SEO work is clearly earning.” — u/alaattincagil, r/SEO, Sep 2026
Being pulled is retrieval. Being named is closer to recognition the model already carries. They move at different speeds, from different work, and a program that confuses them sets the wrong target:
| Being pulled | Being named | |
|---|---|---|
| What it is | The engine fetches a page for this question | The engine writes your brand into the answer |
| What moves it | Getting onto pages the engine reaches for | Everything, accumulated, over a long period |
| How fast | A quarter, sometimes less | Much longer, and rarely attributable |
| What a re-sorted mention list touches | This one, directly | This one, eventually and indirectly |
A mention list is aimed squarely at the first column. Worth knowing before you set a target, because a program judged on named mentions in its first quarter will read as a failure while the thing it was built to move is moving.
The Re-Sort Takes An Afternoon
Nothing below needs a tool you do not have.
Scoring a cited page once you have the set
A raw count is enough to re-sort by. It is not enough to decide where to spend the quarter, because two pages cited the same number of times can be worth very different things to you. Three questions separate them, and all three are answerable by looking at the page:
| Ask of the page | Why it changes the priority |
|---|---|
| Cited for how many of your ten questions? | A page pulled for one question is narrow; a page pulled for six is structural to the category |
| Does it already name competitors but not you? | The cheapest wins on the whole sheet live here |
| Is it a page that updates, or a fixed article? | A roundup refreshed quarterly can be entered. A dated feature usually cannot |
The best row on most sheets is a page cited for several questions, listing four competitors, updated twice a year. That row is not usually near the top by domain rating, which is the whole argument in a single line of a spreadsheet.
What the four quadrants tell you
Cross your existing sheet against the cited set and every target lands in one of four boxes. The boxes, not the scores, are what the audit is for:
| Cited for your questions | Not cited | |
|---|---|---|
| On your sheet | Work these first. The list was already right here | Ask why it is on the sheet. Usually: it has a high DR |
| Not on your sheet | The find. This is what the audit exists to surface | Correctly absent |
The bottom-left box is the one that pays for the afternoon. In most categories it is larger than the top-left, and it is where the forum, the newsletter and the practitioner blog turn up, none of which survive a sort by authority score.
How often to run it again
Once is a photograph. The cited set moves as pages get published, updated and dropped, so the count has a shelf life and the sheet built from it inherits that shelf life:
What the count will not tell you
Two things, and both are worth saying before somebody treats the sheet as finished. The count shows which pages an engine reaches for; it does not show whether being on one of them changes what the engine says about you. That link is the thing the Qvery finding points at and does not prove.
And the cited set is a record of what the engine reached for on the day you asked. Ask again in a week and some of it will have moved, which is why the cadence above matters more than the precision of any single run.
The List Is Different In Every Industry
The part that does not transfer is the list itself. What counts as a valuable mention target is industry-specific, and across our mention work the weight sits in different places by vertical:
What each one tells you when it shows up in your own cited set is different again:
| What dominates your cited set | What it says about the category | Where the quarter goes |
|---|---|---|
| Review and comparison sites | Buyers shortlist before they talk to anyone | Your profile and its reviews, before any outreach |
| Best-of listicles | The category is crowded and the engine wants a shortlist | Getting added to the roundups already being pulled |
| Practitioners and forums | The category is narrow and under-covered | Relationships, one at a time, with people who write about it |
Which of the three carries your category is a question your own cited set answers and a universal source list cannot. That is the reason we build these per industry and per buyer rather than keeping one roster of good sites, and it is the part of the work that does not survive being templated.
It is also why a borrowed list is worse than no list. A roster built for a category with a strong review culture points a team with none of it at pages their buyers never read, and the sheet looks credible the whole time, because every row on it has a respectable number in the authority column.
What We Would Do With Your Forty Rows
We would run the count above before any outreach, re-sort the sheet by it, and then start the slow half. Getting onto a cited page is manual, personal outreach to an editor who has their own reasons for publishing, repeated across a list.
Our own experience is blunt about the shortcut. AI-written personalization does not yet move a real, busy human on the other end, and a list worked that way mostly produces silence.
The order matters more than it sounds. A team that re-sorts first spends the quarter on twelve reachable pages that the engine already pulls. A team that starts outreach against the original sheet spends it on four publications that will not reply, and has nothing to show at the review except the effort.
What this costs after the afternoon
So the honest split. The re-sort is an afternoon and you can run it this week. Earning the placements it points at is a quarter of somebody’s job, and it is the half that most mention programs quietly stop doing around week six.
Which makes the quit point worth naming in advance. If nobody will own the outreach by name, the audit still has a use: it tells you which pages already name your competitors and not you, and that is a short list somebody can work without a program behind it. What it will not do is produce placements on its own, and a re-sorted sheet with nobody working it is the same silence as the original sheet with nobody working it.
We have written on earning mentions without a high domain rating, which is the same problem from the other side, and on measuring AI search visibility once the placements start landing.
If your own target list is sorted by domain rating and the outreach is about to start, that order is worth an afternoon before the quarter. Book time with me and we can decide together whether it is ours to run.
