AI Search Facts Most Marketers Haven’t Seen Yet
We tested 2,729 businesses across ChatGPT, Claude and Perplexity, with Gemini and Google AI Overviews for a subset. These findings challenge the most repeated assumptions about AI visibility—and every one is ready to share.
Roughly seven in ten businesses (69.5% of 2,729) were not mentioned by any AI system they were tested on. The larger study also found that off-page presence correlates with being mentioned by ChatGPT, Claude and Perplexity. Breadth across credible surfaces—not one platform or authority score—was the most reusable predictor. These are observational findings, not proof of causation.
Nearly seven in ten brands were not mentioned by any AI system they were tested on. Reddit, Quora and Wikipedia presence predict Claude mentions. Domain Authority stopped being significant once the full signal stack was measured. And when Perplexity cites a brand's URL, that brand is about six times more likely to be named.
Those are four findings from the two-phase Outrigger AI Visibility Index. We tested 2,729 businesses across ChatGPT, Claude and Perplexity, with Gemini and Google AI Overviews for a subset. Some figures from the v3 paper are under review; see the paper on Zenodo. The result is a more useful—and more complicated—picture of AI search than most tactical checklists suggest.
Below are 37 findings distilled for marketers, founders, agencies, and researchers. Each includes the action it suggests and the evidence that keeps the claim honest. Use the share button beside any finding to post that exact takeaway with a direct link back here.
The market reality
Before tactics, start with the size of the visibility gap.
Nearly seven in ten businesses are not ranking poorly in AI. They do not appear at all.
Make the first verified mention the first milestone for an invisible brand.
Roughly seven in ten businesses (69.5% of 2,729) were not mentioned by any AI system they were tested on. Gemini and Google AI Overviews were tested for a subset only.
Only three in ten businesses earned a mention from any AI system they were tested on.
Establish a baseline before investing in GEO; most brands are starting from zero.
30.5% of 2,729 businesses were mentioned by at least one system they were tested on (v3 paper; Gemini and Google AI Overviews for a subset only).
For most brands, the first AI-search goal is not ‘rank higher.’ It is ‘exist in the answer.’
Report zero-to-one-model movement separately from ranking or prominence gains.
Editorial implication of the 69.5% invisibility rate.
Which systems the correlations were measured on
Gemini and Google AI Overviews were scored for a subset of the sample only, so per-model statements below are limited to ChatGPT, Claude and Perplexity.
Among ChatGPT, Claude and Perplexity, Claude showed the largest off-page partial correlations.
Prioritise earned third-party presence when Claude matters to the buyer journey.
v3 Tables 2–4: Wikipedia × Claude +0.219, Quora × Claude +0.203, Reddit × Claude +0.190.
Per-model findings (ChatGPT, Claude, Perplexity)
These are the findings most likely to change where a team spends its next dollar.
Wikipedia presence had the largest single off-page association in the study—and it was with Claude.
Run a Wikipedia eligibility audit for qualifying brands; never manufacture eligibility.
Wikipedia × Claude partial r=+0.219; 95% CI [+0.172, +0.266].
Quora predicted Claude visibility more strongly than Reddit did.
Include credible Quora participation when technical or research-oriented buyers use Claude.
Quora × Claude +0.203 vs Reddit × Claude +0.190.
Reddit’s association with being mentioned was largest for Claude among the three systems with complete data.
Use earned Reddit presence for Claude-oriented audiences; do not generalise the result to other systems.
v3 Table 2: Reddit × Claude +0.190, ChatGPT +0.128, Perplexity +0.110.
Reddit still predicted ChatGPT visibility after nine controls.
Treat Reddit as one component of a ChatGPT plan, not the whole plan.
Reddit × ChatGPT partial r=+0.128; 95% CI [0.081, 0.174].
Reddit also predicted Perplexity visibility—but less strongly than it predicted Claude.
Combine community presence with retrieval-eligible pages for Perplexity.
Reddit × Perplexity partial r=+0.110; 95% CI [0.063, 0.156].
A complete LinkedIn company presence was one of Perplexity’s stronger measured off-page signals.
Make the company page complete, consistent, and active before chasing exotic GEO tactics.
LinkedIn × Perplexity partial r=+0.163; FDR-significant. Dataset file per-model-strict-isolation.json; the paper prints this row only as part of the "same pattern holds" group in §4.2.
YouTube presence was associated with being mentioned by ChatGPT, Claude and Perplexity.
Treat YouTube as an entity and topical-presence asset, not only a video channel.
Perplexity +0.153, Claude +0.182, ChatGPT +0.111; all FDR-significant. Dataset file per-model-strict-isolation.json; the paper prints has_youtube_channel only as part of the "same pattern holds" group in §4.2.
For local brands, BBB and Yelp were inexpensive signals with evidence across multiple models.
Claim, complete, and reconcile these profiles before funding higher-cost experiments.
BBB and Yelp were FDR-significant for ChatGPT, Perplexity, and Claude.
Breadth beats the silver bullet
The web rewards the brands that are corroborated across a network of credible places.
YouTube mentions were one of the strongest raw signals in the full sample.
Earn inclusion in third-party videos as well as publishing on the owned channel.
YouTube mention count raw r=+0.355 (v3 Table 7; n=2,729); 95% CI [0.324, 0.385] from dataset file bootstrap-cis.json.
AI visibility is a system, not a signal.
Sequence several low-friction presence fixes instead of betting the quarter on one channel.
v3 Conclusion: effect sizes are "small per individual signal (partial r ≈ 0.10–0.22) but consistent across signals"; v3 §5: "additive across signals".
Content and technical signals
Several popular checklists mattered—but less, or differently, than the industry tends to claim.
Raw page count looked useless until the extreme outliers were corrected.
Judge site depth on a log scale and by topical usefulness, not raw URL count.
Indexed pages raw r=−0.014; log r=+0.141; Spearman +0.133.
Organic search reach still travelled with AI visibility.
Keep strong SEO foundations; GEO is an added discovery layer, not an SEO replacement.
Log organic clicks r=+0.189; log organic keywords r=+0.168.
Backlink metrics changed sign after skew correction.
Do not quote raw Pearson results for heavily skewed link counts.
External links raw −0.059 → log +0.099; referring domains raw −0.068 → log +0.118.
Citability was small, but it survived the multivariate model.
Make high-intent pages easy to quote with clear claims, definitions, tables, evidence, and attribution.
Citability coefficient +0.123; 95% CI [+0.008, +0.244].
Retrieval and citation mechanics
Citation data is powerful, but only when the model and API limitations stay attached.
When Perplexity cited a brand’s URL, the brand was about six times as likely to be mentioned.
Build retrieval-eligible pages and earn inclusion in sources Perplexity already trusts.
v3 §4.3 reports a roughly sixfold within-response lift for Perplexity. Association, not causation.
Three of the five tested APIs did not expose source URLs.
Never publish a five-model citation-source chart from these API results.
ChatGPT, Claude, and Gemini returned empty source arrays by construction.
Ninety-seven percent of observed source URLs came from Perplexity.
Label source-category analyses as primarily a Perplexity study.
4,603 of 4,737 exposed citation URLs came from Perplexity (97.2%) and 134 from Google AI Overview (dataset file source-url-analysis.json, by_model; not printed in the paper).
Most ‘AI cites X% from Reddit’ claims are really Perplexity claims wearing an all-AI label.
Ask which model exposed the sources before repeating a citation statistic.
Direct implication of the source-URL ceiling.
For Perplexity, retrieval eligibility is the mechanism worth optimising.
Create clear comparison, category, evidence, and evergreen pages that can enter the source pool.
Editorial implication of the roughly sixfold citation-linked mention lift.
Being cited and being named travel together—but the study cannot prove which one causes the other.
Use citation-linked lift as prioritisation evidence, not a guaranteed outcome claim.
Within-response observational association; no random assignment.
Vertical and tactical implications
Benchmarks and channel choices get more useful when they reflect the category and buyer journey.
For local services, verified directory and review profiles are the cheapest evidence-backed first move.
Start with GBP consistency, BBB and Yelp where relevant, and vertical directories.
BBB and Yelp were FDR-significant across the three responsive models; prioritisation inference.
For B2B and SaaS, the useful footprint shifts toward LinkedIn, Crunchbase, YouTube, and category platforms.
Match the platform set to how buyers research the category and which model they use.
v3 §4.2: has_linkedin_company and has_crunchbase were positive and BH-FDR-significant for Claude, ChatGPT, and Perplexity.
The fastest useful audit is simple: which models mention you, and on how many trusted surfaces does your brand exist?
Put per-model visibility beside directory and platform breadth on the first screen.
Synthesis of the per-model tables (ChatGPT, Claude, Perplexity) and off-page breadth.
The caveats worth sharing
Research earns trust when the limits are as easy to find as the headline.
The study found predictors, not proof that a marketing tactic causes visibility.
Use ‘predicts’ and ‘is associated with’ until a controlled trial reports.
Both phases were observational; confounding and reverse causality remain possible.
The strictest per-model analysis used 1,693 complete cases, not all 2,729 businesses.
Put n=1,693 beside partial-correlation charts and explain the missing-data filter.
Businesses missing any control variable were excluded; the sub-sample is non-random.
Every model response was a one-shot sample.
Treat small changes as noise until repeated measurement establishes reliability.
No test-retest probing; run-to-run variance was not estimated.
Four hundred twenty-six businesses had no resolved first-party URL.
Show the sample size for every URL-dependent analysis instead of defaulting to 2,729.
15.6% URL-resolution gap in Phase 2.
These findings are a timestamp, not a law of nature.
Date every chart and rerun benchmarks as model behaviour changes.
Phase 2 data was collected April–May 2026.
Corrections are part of the record: v3 replaced the withdrawn v2 and demoted Domain Authority from the headline.
Make re-analysis part of the story: larger samples should be allowed to change the headline.
v3 §1: the v2 record (DOI 10.5281/zenodo.20048424) was withdrawn; v3 §4.4: Domain Authority’s multivariate coefficient interval crosses zero.
Open data turns a vendor study into an invitation to disagree.
Link the paper, dataset, caveats, and analysis outputs wherever the research is promoted.
Paper DOI 10.5281/zenodo.20076380; dataset DOI 10.5281/zenodo.20076406; CC BY.
The takeaway behind the takeaways
AI visibility is a portfolio problem.
The brands that win are not chasing one hack. They are legible across credible surfaces, measured model by model, and willing to update the playbook when better evidence arrives. Start by learning where you are absent. Then strengthen the footprint your buyer's AI system can actually find.
Method note: Phase 1 and Phase 2 were observational studies. Associations identify useful predictors, not guaranteed causal effects. Phase 2 was collected in April–May 2026, and each model response was sampled once. Model behaviour changes, so every benchmark should be treated as dated evidence.
Frequently Asked Questions
Where do these AI search statistics come from?
They come from the published v3 paper of the Outrigger AI Visibility Index (DOI 10.5281/zenodo.20076380) and its released dataset files. The study audited 2,729 businesses across ChatGPT, Claude and Perplexity, with Gemini and Google AI Overviews for a subset. Each fact's evidence line says whether the figure is printed in the paper or taken from a named dataset file. The paper and anonymised dataset are published on Zenodo under CC BY.
Do these findings prove that Reddit, directories, reviews, or citations cause AI visibility?
No. Both completed phases were observational. The results identify associations and useful predictors, but unmeasured confounding and reverse causality remain possible. A separate controlled study is needed to estimate causal impact.
Why are some findings labelled scoped finding?
Scoped findings are valid only for a named model, cohort, API limitation, or descriptive comparison, and several cite a released dataset file rather than a sentence printed in the paper. The label prevents a narrow result from being repeated as a universal rule. Findings from the original Phase 1 cohort (n=1,004) were removed on 21 October 2026: the Phase 1 dataset record was withdrawn on 7 May 2026 and the v3 paper does not reprint them.
Can I share or cite these findings?
Yes. Every fact has a one-click share link back to this article. For formal or research use, cite the open paper at DOI 10.5281/zenodo.20076380 and the dataset at DOI 10.5281/zenodo.20076406. Both are available under CC BY.
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