Do Self-Promotional 'Best Of' Listicles Help AI Visibility?

Writing a "best [category]" list on your own blog and putting yourself at the top is one of the most common AI visibility tactics in B2B software. It works at getting cited. It mostly does not work at getting recommended. In Lily Ray's tracking of 100 "best B2B software" queries, Google AI Overviews cited self-promotional listicles 323 times between April and June 2026, and left the publishing brand out of the actual recommendation 69% of the time. Her September re-run put that at 83%.

That is one dataset on one engine. A second, larger study across seven assistants appears to disagree sharply on how often competitors benefit. Read the two side by side and they mostly agree. What follows is that reconciliation, the lab research that seems to say the opposite, and a rule for checking your own pages.

Key takeaways

Two independent groups have measured what happens when an AI answer cites a vendor's self-ranking list. Their designs differ in ways that explain most of the gap between their headline numbers.

Sources: Ray, June; Ray, October; Scrunch.

The publisher-recommended figures look far apart until you line up the definitions.

Ray counts a brand as recommended when it appears as a pick in the Overview's text. On a "best X software" query, the Overview is itself a list of picks, so being in the text and being recommended are nearly the same event. Scrunch splits those apart: named (the brand appears) and recommended (the engine actively steers the user to it). Ray's 31% in June falls between Scrunch's two figures for the same window. It is below the 39% named rate (pooled across all seven engines) and well above the 8.9% recommended rate on AI Overviews. That is where you would expect a measure that sits between the two definitions to land.

So both datasets support the same reading. When your own list is cited, an engine names you about a third of the time and actively endorses you far less often. Ray's September drop to 17% then fits a trend rather than a disagreement. It is consistent with the same engine getting stricter over the summer, which Ray's 38% fall in citations of these pages also suggests.

The competitor figures differ by a factor of almost three, and the denominators explain it.

Ray's queries are all recommendation-shaped. Every one asks for the best software in a category, so every Overview that answers recommends someone. If the publisher is left out 83% of the time, a competitor is almost by construction what fills the space. Scrunch's denominator is every answer that cited a sampled page, across seven engines and whatever prompts produced those citations. Many of those answers recommend no one. Scrunch reports that in 63.9% of answers citing a self-promotional listicle, the engine used the page "as raw material for a balanced overview" rather than adopting its number-one pick. A competitor-recommended rate computed over that wider base will be lower. The timing differs too: Scrunch's window ended June 30, and Ray's own two waves show the effect moving against publishers after that.

The ratio is what holds up. In Scrunch's data, competitors were recommended instead of the author in 24.3% of answers, against a roughly 7% author recommendation rate: about 3.5 to 1. In Ray's September data, 70% against 17%: about 4 to 1. Measured either way, a cited self-promotional listicle is followed by a competitor recommendation several times more often than by one for its publisher.

There is a body of research suggesting a vendor's own page can move an LLM's product ranking. The two studies most often cited are real. Neither describes the tactic marketers are actually using.

Both test adversarial text written to hijack the model, inserted into a small, controlled set of candidate pages. A "Top 10 [category] tools (we're #1)" post is ordinary persuasive copy competing with many other retrieved sources. Scrunch found 43.4% of answers citing a self-promotional listicle also cited a competitor's own website. These papers establish that LLM rankings can be manipulated. They are not evidence that a self-ranking listicle will move a production engine. Their technique is also prompt injection, which is a different thing from content marketing, with different risks.

Google's position points the same way. Its guide to AI features says plainly that "seeking inauthentic 'mentions' across the web isn't as helpful as it might seem."

Ray's methodology is lightly documented. The June study describes its query set, dates, and counts. The September update reports percentages without saying whether it used the same 100 queries or how pages were classified. It covers one engine and one vertical, B2B software.

Scrunch is a single vendor's study of a single window. Its causal estimate covers about 6% of the eligible population. Its authors say the mention lift has not yet passed their falsification test and that the numbers are "a snapshot of that window, not a constant." Scrunch sells AI visibility software, as we do.

Averages hide your page. Both studies pool hundreds of brands. A listicle from a category leader whose name the model already associates with the category can behave very differently from one by an unknown challenger. Ray notes that lesser-known self-promoters occasionally make it into the answer, though "this appears to be increasingly rare."

Neither study measures clicks or revenue. A citation can still send traffic. What these numbers say is that it rarely sends an endorsement.

The question to ask about a self-promotional listicle is not "does it get cited?" Both datasets say it probably does. The question is whether answers that cite it recommend you more often than they recommend someone else.

If the delta stays negative, the page is giving your competitors evidence. Rewrite it into a comparison a neutral reader would trust, with stated criteria and honest trade-offs, or stop producing more of them. If it is positive, keep it and keep measuring, because Ray's two waves show the effect shrinking.

The broader point is the one our review of where AI citations come from flagged as an open question: most citations point to domains the brand does not own, and the major format studies don't check whose domain a cited listicle sits on. This is the first evidence on that question. It suggests the recommendation is shaped mostly by what other sources say about you, and very little by what you say about yourself.

Elmo is an open-source, self-hosted AI visibility platform that runs your prompt sets across ChatGPT, Claude, Gemini, Perplexity, and Google's AI surfaces. It records the cited URLs, the brands named, and the competitors recommended next to you on every run. Because you run it yourself and the data is yours to query, the net recommendation delta for any page is a query over your own results.

For the fundamentals, start with AI citations and AI share of voice, then how to track your brand in AI search. For the vocabulary, see the AI search glossary.