I have been running a small experiment for a few weeks, and this one genuinely stopped me for a second.
More and more buyers ask ChatGPT or Claude for a tool recommendation before they ever open Google. So I have been measuring what the models actually say when someone asks for the best tool in a category, and how much the answer moves when you change nothing but the wording.
Shared inbox software gave me the clearest example so far.
I asked two versions of the same question, 10 times each, on ChatGPT, Claude, Perplexity and Gemini. First the plain category label, "best shared inbox software". Then the way a real person actually types it, "how do we collaborate on shared email inboxes without losing our normal email workflow".
On the label question, Missive got named on all 10 runs. On the reworded one, ChatGPT named Missive zero times out of ten, while it kept naming Front and Help Scout on all ten. Same company, same site, same ten runs. The only thing that changed was the sentence.
To be fair it was not every model. Claude actually leaned the other way and named Missive more on the reworded question. But the ChatGPT drop was clean and it repeated across all ten runs.
The thing I keep taking from these is that a single check will lie to you. If I had asked once, seen Missive missing, and stopped there, I would have sworn they had some deep AI problem. They do not, they are 10 out of 10 on the other phrasing. One question on one model on one run tells you almost nothing.
And the tools that held did not do anything clever. Their pages just describe the actual job in the words a buyer uses, so the model can place them no matter how the question is asked.
I wrote up the full data, every number and every model, here: https://www.bersyn.com/blog/shared-inbox-phrasing-flip
I am running one of these teardowns roughly every week, poking at a different category. If that is your kind of rabbit hole, follow along. And I am curious, in your own category, is the recommendation this sensitive to phrasing, or did shared inbox just happen to be a swingy one?
Top comments (2)
the volatility is the real story here, and it's why AEO isn't just SEO with a new name. in search you optimize for a known query. here the "query" is whatever phrasing a buyer happens to type, which is basically infinite and you don't control it. what actually moves the model isn't on-page anything, it's how often you show up in the corpus it learned from and retrieves against: comparison posts, reviews, threads where real users name you. so the fix for a 0/10 reword usually isn't the wording, it's not being cited widely enough that the model reaches for you regardless of how the question gets asked.
Completely with you on the corpus point, that is the bigger lever for most companies and it is exactly why this is not SEO with a new coat of paint. You do not control the query, so you cannot optimize a page for it the way you would a keyword.
The one thing I would add, and it is the reason I run every phrasing multiple times, is that the data lets you tell two different failures apart. If a company is zero across every phrasing and every model, that is the corpus gap you are describing, they are simply not cited enough anywhere, and no amount of rewording their own site fixes it. But Missive was ten out of ten on one phrasing. The model clearly has it in the corpus and reaches for it easily. Its zero on the reword is not an absence of citations, it is that the citations and its own pages tie it to the category label instead of the workflow the buyer actually described.
So I think it is both, and the shape tells you which one you are dealing with. Absent everywhere is a get-cited problem, your version. Present on the label and gone on the job is a get-cited-for-the-right-job problem, where the corpus still matters but it has to be corpus about the specific workflow, not just more mentions of the category. Curious whether you see that split too, or whether you would still put almost all of it on raw citation volume.