Large language models are not just answering definitions anymore. People now ask them which vendor to hire, which hotel to book, which airline to fly, which insurance provider to trust, which ticketing platform to use, and which software company belongs on a shortlist.
That changes the visibility problem for marketers in a way that most GEO strategy has not caught up with yet.
In traditional SEO, the measurement model was simple: where do we rank? In AI search, that question is still relevant, but it is only a fraction of the picture. A brand can rank on page one of Google and still disappear from ChatGPT recommendations. Another brand can have weaker Google visibility but appear again and again because it has stronger entity signals, more consistent third-party references, more authentic community discussion, or clearer category authority across the wider web.
The gap between those two visibility systems is what this report quantifies — and the numbers are more useful than most AI visibility commentary published in the last year.
That is why this report matters.
OppAlerts.com just published what it describes as the largest LLM ranking factors analysis to date: 145 industries, 1,595 buyer personas, more than 105,000 ChatGPT prompts, and 29,562 unique domains scored against 13 external signals. The dataset draws from Common Crawl (1.15 billion web pages), Reddit (5 billion+ posts and comments), 15,697 Google search queries, Wikimedia (300M+ entity records), and a 4 billion+ backlink graph.
The goal was not to claim ChatGPT had been cracked. The useful question was narrower and harder: which observable external signals correlate with LLM recommendation visibility, and do those signals change by industry and buyer persona?
The answers are clarifying, uncomfortable in places, and more strategically useful than most GEO content published in the last 12 months.
This article goes through the findings in depth — the data, the mechanisms behind the correlations, where the evidence points clearly, where it does not, and what it means for building an AI visibility strategy that can actually hold up.
Executive Summary: The Data Says There Is No Single Factor — and That Is the Point
TL;DR:
- OppAlerts analyzed 105,000+ ChatGPT prompts across 145 industries and 1,595 buyer personas — the largest LLM recommendation analysis published to date.
- Search engine appearances were the strongest global external signal, but explained only 5.8% of variance in LLM recommendation scores. The other 80–85% is inside the model.
- Backlink count correlated with recommendations, but the mechanism is different than traditional SEO — it is about citation breadth and category fit, not raw PageRank.
- Wikidata was the dominant signal in multiple industries (Hotels: R² 42.3%, ERP software: 42.9%, Furniture: 49.9%) — yet almost no B2B brands have invested in it.
- Reddit was highly predictive in categories where people ask for real opinions: Enterprise AI platforms (R² 27.9%), live entertainment (25.3%), cosmetic surgery clinics (10.3%).
- Persona context is not a modifier. It is the product. The same industry, the same model, a different buyer — materially different recommendations.
- A single aggregate “AI rank” is a weak measurement unit. Brands need persona-level LLM visibility tracking.
- The most actionable finding: 80–85% of LLM recommendation behavior cannot be explained by any external signal. That means LLM visibility must be tested directly, not inferred from SEO dashboards.
The thing most AI visibility commentary gets wrong is treating this as a ranking problem with a familiar shape. It is not.
In SEO, you could build a model: improve authority, improve content, improve technical signals, rankings improve, traffic improves. The causal chain was legible, even if imperfect. Teams could look at a Semrush dashboard, identify gaps, fix them, and track movement.
LLM recommendations do not work that way.
The best single external predictor in this dataset — search engine appearances — explains 5.8% of variance. All 13 measured external signals combined explain well under 20%. The remaining 80–85% is inside the model: training data distributions, fine-tuning objectives, reinforcement learning from human feedback, long-term brand familiarity, user-context interpretation, and answer-format preferences that cannot be audited from the outside.
That is not a reason to ignore external signals. It is a reason to stop pretending that optimizing external signals is a complete strategy.
The brands that are winning in AI search are not the ones chasing one tactic or one factor. They are the ones building enough credible, consistent evidence across enough layers of the web that the model’s internal representation of their brand is strong, accurate, and durable. That takes longer. It is harder to attribute. It is also the only thing that holds up over time.
| Signal | Spearman rho | R² | Tier |
|---|---|---|---|
| Search Engine Appearances | +0.241 | 5.8% | STRONG |
| Best Search Engine Rank | +0.238 | 5.7% | STRONG |
| SE Outbound Links | +0.230 | 5.3% | STRONG |
| Backlink Count | +0.204 | 4.2% | STRONG |
| BL Authority | +0.200 | 4.0% | STRONG |
| BL Authority (Exp) | +0.199 | 3.9% | CONFIRMED |
| PageRank | +0.194 | 3.7% | CONFIRMED |
| Harmonic Centrality | +0.169 | 2.8% | CONFIRMED |
| Common Crawl | +0.123 | 1.5% | CONFIRMED |
| Wikidata | +0.120 | 1.4% | CONFIRMED |
| Reddit Comments | +0.111 | 1.2% | CONFIRMED |
| Reddit Posts | +0.096 | 0.9% | EMERGING |
| Wikipedia Citations | +0.077 | 0.6% | EMERGING |
| Homepage Keywords | +0.072 | 0.5% | EMERGING |
The better question is not: “What is the LLM ranking factor?”
It is: for this industry, for this buyer persona, what evidence layers separate the brands that get recommended from the ones that do not?
The Dataset: Why Scale Matters Here
TL;DR:
- 145 industries, 1,595 buyer personas, 105,000+ prompts, 29,562 unique domains.
- Signals compared across Google SERPs, Common Crawl, Reddit, Wikimedia, backlinks, and homepage content.
- Rank-weighted scoring: a #1 recommendation counted more than a #10.
- Spearman correlation used throughout because recommendation scores are heavily skewed distributions.
Most AI visibility commentary is built on small samples. A researcher runs 50 prompts, notices patterns, writes a framework. That work is useful for generating hypotheses. It is not useful for strategy.
The OppAlerts dataset is different in a way that matters. It treats LLM recommendations as a market visibility problem across thousands of industry-persona combinations, not as an individual brand audit. It does not ask whether one brand appeared in one prompt. It compares recommendation behavior across 145 industries, 1,595 buyer personas, repeated prompt runs with rank weighting, and 13 external signal categories pulled from one of the largest public web datasets available.
| Input | Scale |
|---|---|
| Common Crawl HTML | 1.15B random pages, March 2026 WARC snapshot |
| 5B+ submissions and comments, Jan 2025–Mar 2026 | |
| Google searches | 15,697 queries, top 100 organic results per query |
| Google result URLs | 1.5M+ individual results, 833,458 unique URLs |
| Search result domains | 250,899 unique sites |
| Wikimedia | 300M+ entities, pages, and external-link records |
| Backlink graph | 4B+ backlinks from Common Crawl web graph data |
The methodology is also honest about its limits. Spearman rank correlation was used throughout because recommendation scores and authority metrics are heavily skewed — Pearson would overfit to outliers. R² tells you how much variance each signal explains. Lift tells you how much more common a signal is among the top 10% most-recommended domains. These are the right tools for this problem.
One prompt is an anecdote. Ten prompts are still fragile. At this scale, across this many industries and personas, the patterns become strategically usable.
Finding #1: Search Is Part of the AI Evidence Layer — but It Explains Less Than 6%
TL;DR:
- Search Engine Appearances was the strongest global external signal: rho +0.241, R² 5.8%.
- Top-10 recommended domains had 2x as many Google SERP appearances as average domains.
- Only 37% of LLM-recommended domains even appeared in the tracked Google results.
- The hotel example makes the gap concrete: Tripadvisor is the #1 search domain. Marriott is the #1 LLM domain. They are not the same brand.
- SEO is still foundational — but it is an input into AI visibility, not a proxy for it.
Search engine appearances were the strongest measured global signal. That is not surprising. Google search results are one of the most organized public maps of the web, and ChatGPT draws from that map — through training data, retrieval integrations, browsing tools, and indirect authority signals baked into how the model understands the web.
But 5.8% R² is the number that should change how your team talks about SEO and AI visibility in the same conversation.
It means that even the strongest measurable external signal only explains about 1 in 17 data points. Teams that assume their Google rankings translate into AI recommendations will be right sometimes. They will be wrong most of the time — and they will not know when.
The hotel example from the report makes this gap impossible to ignore.
Tripadvisor was the top search domain for Hotels & Resorts with 223 SERP appearances. Marriott was the top LLM-recommended domain, recommended in 49.1% of all hotel query runs. These are not small differences in rank position. They are different brands at the top of two different visibility systems.
Why? Because the signals that put Tripadvisor at the top of Google — massive link volume, broad keyword coverage, high-frequency crawl, thousands of location pages — are not the same signals that put Marriott at the top of ChatGPT recommendations. Marriott wins on Wikidata entity clarity, backlink authority in the right categories, Reddit discussion volume, and long-term brand familiarity absorbed into model weights during training. The domain signals matrix in the report makes this visible: Marriott carries Wikidata coverage and strong backlink authority simultaneously, while Tripadvisor’s advantage is concentrated in search appearances alone.
The practical consequence for strategy is not “do less SEO.” SEO is still the infrastructure layer. Crawlability, content depth, internal linking, backlinks, and topical authority all matter because they feed into the evidence the model can find, index, and rely on.
The practical consequence is: rankings are no longer the final reporting metric. A team that reports SERP position and assumes AI visibility is covered is leaving a measurement gap that competitors who test LLM visibility directly will exploit.
The right response is to run both systems in parallel: traditional SEO for search rankings, and persona-segmented prompt testing for LLM recommendation visibility. They measure different things. They require different optimization. They can both be won, but not with the same playbook.
Finding #2: Backlinks Still Matter — but the Mechanism Has Changed
TL;DR:
- Raw backlink count: rho +0.204, R² 4.2%, lift 2.84x among top-recommended domains.
- Top-10 recommended domains had a median of 343 backlinks vs. 121 for the average domain.
- Google.com had the highest backlink authority in the hotel dataset (PR 100, 16.9M backlinks) — and did not top the hotel LLM recommendation list.
- The mechanism is not PageRank. It is citation breadth — how many parts of the web connect your brand to the right category context.
- The link-building brief has to change: it is about reinforcing specific brand-to-category relationships, not accumulating generic authority.
The 2.84x lift figure is the one that matters most in this section, and it is more important than the correlation number.
Lift means the top 10% most-recommended domains have nearly three times as many backlinks as the average domain in the dataset. That is a structural gap, not a marginal one. The most recommended brands have not built slightly more authority. They have built a fundamentally different scale of web citation.
But this is where the mechanism diverges from traditional SEO in a way that changes the strategic brief.
The report’s hotel backlink data is the clearest illustration. Google.com had PageRank 100 and 16.9 million backlinks — the highest authority in the entire hotel dataset. Google is not a hotel recommendation. Marriott had PageRank 78 and 89,400 backlinks. Marriott is the #1 recommended hotel brand.
The difference is not authority. It is category-relevant citation breadth.
Marriott’s backlink profile is built from hotel-relevant sources: travel guides, booking flow pages, credit card rewards sites, airport partner pages, tourism boards, hotel review publications, loyalty program directories, and comparison platforms. Every one of those links reinforces the same relationship: Marriott = hotels = trusted accommodation at the category level where the query lives.
Google’s backlinks come from everywhere and reinforce no specific category association.
In traditional SEO, a link from a high-DA site was a link. In AI visibility, a link from a high-DA site that also creates a clear brand-to-category-to-use-case relationship is doing something fundamentally more valuable. It is feeding the model’s understanding of what your brand is, where it belongs, and when it is appropriate to recommend.
That changes how the link-building brief should be written.
Instead of: “We need links from DA 60+ sites.”
It should be: “We need links from sites that cover our category, mention our competitors, describe our buyer’s problem, and exist inside the content ecosystem where buying decisions in our market get made.”
The specific citation relationships that compound into AI visibility look like this for a B2B SaaS brand:
- Brand to category — A data engineering platform mentioned in a “best modern data stack tools” comparison
- Brand to buyer problem — The same platform cited in content about data pipeline reliability for fintech teams
- Brand to use case — A case study referenced by an analytics publication covering real-time reporting infrastructure
- Brand to comparison set — A G2 or analyst mention placing the brand alongside Fivetran, dbt, and Airbyte
- Brand to founder or expert — A podcast transcript or byline connecting the founder’s name to the category
- Brand to outcome — A trade publication citing a customer’s ROI metric from using the platform
Each of those links teaches the model something specific about the brand. Generic high-DA links do not.
The implication for budgeting: link acquisition that does not reinforce a specific brand-category-buyer relationship is less valuable for AI visibility than it used to be for search ranking. The question is no longer “can we get a link from that domain?” It is “does a link from that domain teach the model something useful about what we do and who we do it for?”
Finding #3: 80–85% of LLM Recommendation Behavior Cannot Be Explained by Any External Signal
TL;DR:
- The best single external predictor explained 5.8% of variance. All 13 combined signals explain well under 20%.
- The remaining 80–85% is inside the model: training data, fine-tuning, RLHF, and long-term brand familiarity.
- This is the most important constraint for setting realistic expectations about what GEO tactics can control.
- The correct response is dual-track: test LLM visibility directly, and build evidence layers that improve your odds over time.
This finding should be pinned to the wall of every agency pitching GEO services and every internal team presenting an AI visibility plan to leadership.
Eighty to eighty-five percent of LLM recommendation behavior — across 145 industries, 29,562 domains, and 13 external signals — cannot be explained by any observable external metric.
Not by search rankings. Not by backlinks. Not by Reddit volume. Not by Wikidata records, Common Crawl co-occurrence, or homepage keyword density. Not by any combination of these.
What explains it? The model’s internal state: how the brand appeared across billions of training documents before the model was built, what the fine-tuning process reinforced as trustworthy sources, what RLHF raters selected as helpful answers across thousands of judgment calls, and what the model has learned to associate with specific buyer contexts from years of conversational interaction.
That internal state is not directly auditable. It cannot be reverse-engineered from an Ahrefs report.
This creates a real tension for GEO strategy. External signals are improvable. They are measurable. They give teams levers to pull and dashboards to report. So there is natural pressure to overweight them — to claim that “fixing your entity clarity” or “building 50 backlinks in category-relevant publications” will move your AI recommendation score measurably.
Sometimes it will. The correlations are real. But they explain a fraction of the system.
The honest strategic response is to run two workflows in parallel:
Workflow 1: Direct LLM visibility testing. Build a prompt library segmented by buyer persona. Run prompts repeatedly across ChatGPT, Perplexity, and Gemini. Track which brands appear, at what position, and in what percentage of runs. Measure movement over time. This is the only way to know your actual recommendation visibility — not inferred from proxy metrics, but measured directly.
Workflow 2: Evidence-layer building. Work on every external signal that is measurable and improvable: search rankings, backlink breadth and category fit, entity clarity, third-party citations, review platform presence, community discussion, structured data, and earned media. These are the levers that influence the model’s evidence environment, even if the causal chain runs through training data rather than real-time retrieval.
Neither workflow replaces the other. Without measurement, you are optimizing blind. Without evidence-building, you are only watching the problem.
The biggest mistake in GEO right now is teams claiming they have “optimized for AI visibility” without ever running a prompt audit to see what the model actually recommends. That is equivalent to doing SEO without ever checking whether the pages rank.
Finding #4: Signal Hierarchies Are Industry-Specific — and That Breaks Every Generic GEO Checklist
TL;DR:
- The top signal in Hotels & Resorts (Wikidata, R² 42.3%) would be nearly irrelevant advice for Airlines (top signal: Backlink Authority, R² 24.5%).
- Auto insurance is dominated by backlink harmonic centrality (R² 40.6%) — a metric that correlates with long-term web authority accumulation in mature markets.
- Enterprise AI Platforms and Live Entertainment are driven by Reddit posts (R² 27.9% and 25.3%) — categories where buyers seek unfiltered real-world experience before committing.
- For B2B tech companies: the buyer journey spans enough evidence layers that no single signal shortcut exists.
| Industry | Top Signal | rho | R² |
|---|---|---|---|
| Furniture stores | Wikidata | +0.706 | 49.9% |
| ERP software | Wikidata | +0.655 | 42.9% |
| Hotels & resorts | Wikidata | +0.650 | 42.3% |
| Auto insurance | Backlink harmonic centrality | +0.638 | 40.6% |
| Enterprise AI platforms | Reddit posts | +0.528 | 27.9% |
| Live entertainment & ticketing | Reddit posts | +0.503 | 25.3% |
| Airlines | Backlink authority (exp) | +0.495 | 24.5% |
The phrase “LLM ranking factors” is useful as a category label. It becomes dangerous when it implies there is a universal list.
These numbers show why. The top signal in Hotels (Wikidata, R² 42.3%) would be practically irrelevant advice for Airlines, where Wikidata appears nowhere near the top. The top signal in Auto Insurance (backlink harmonic centrality, R² 40.6%) would be misleading guidance for Enterprise AI Platforms, where Reddit posts explain nearly three times more variance than any link metric.
These are not marginal differences. They are structurally different markets with structurally different evidence hierarchies.
Why does Wikidata dominate in Hotels, ERP software, and Furniture?
These are categories with large established brands, long brand histories, and relatively stable market structures. Marriott, SAP, IKEA — these companies have been written about in encyclopedias, referenced in structured databases, and linked from authority sources for decades. AI systems trained on the web have absorbed those structured references as foundational brand representations. When a buyer asks ChatGPT to recommend a hotel chain, the model’s internal representation of Marriott is sharp, consistent, and well-sourced. The entity is clear. That clarity translates into recommendations.
Why does Reddit dominate in Enterprise AI Platforms and Live Entertainment?
These are categories where official brand content is least trusted and peer experience is most valued. A VP of Engineering evaluating an enterprise AI platform does not fully trust vendor content. They look for r/MachineLearning threads, Hacker News discussions, and real practitioner accounts of what worked and what failed in production. Ticketing buyers look for Reddit threads about whether a platform hides fees, cancels orders without notice, or makes refunds impossible.
The model has learned from billions of these conversations. Brands that have been discussed honestly and at scale in those communities have stronger model representations in the categories where real-world peer trust matters.
Why does backlink harmonic centrality dominate in Auto Insurance?
Auto insurance is a mature, commoditized, heavily-advertised market dominated by State Farm, Progressive, Geico, and Allstate. These brands have accumulated massive backlink footprints over decades: insurance directories, comparison sites, financial publications, news coverage, regulatory filings, affiliate networks, and consumer reporting platforms. Harmonic centrality measures how well-connected a domain is across the full web graph — and in a mature market with dominant national brands, that centrality has been building for 20+ years. The model’s recommendations reflect that accumulated market structure.
The practical implication for B2B tech: your industry-specific signal hierarchy probably looks different from all of the above. The B2B software buyer journey spans search rankings, analyst-style comparisons, review platforms (G2, Capterra), founder and executive credibility, GitHub or documentation quality, community discussion, case studies, and integration partner pages. That is too many evidence layers for any single signal to dominate.
That is actually the right answer for strategy: build all of them. But knowing which ones are weakest for your specific category — versus your competitors — is the work.
Finding #5: Wikipedia and Wikidata Are the Most Underinvested Signals in B2B GEO
TL;DR:
- Global Wikidata correlation is modest (rho +0.120, R² 1.4%) because coverage is sparse globally — only about 5% of domains in the study had records.
- But where records exist, the effect is enormous: Hotels R² 42.3%, ERP R² 42.9%, Furniture R² 49.9%.
- The mechanism: Wikidata is part of the foundational reference layer LLMs are trained on. A brand with a clear entity record gives the model a durable, structured representation.
- Most B2B SaaS and services brands have no Wikidata entity. That is a gap that takes a few hours to close.
- Wikipedia is not the right goal for most brands. Entity clarity across the whole web is.
LLMs do not only process pages. They process relationships: between companies, products, people, categories, publications, locations, and concepts. Wikidata is one of the primary sources where those relationships are encoded in a machine-readable, structured form.
When a company has a clean Wikidata entity record — with consistent naming, industry classification, founding date, country, key personnel, sameAs links to official profiles, and knowsAbout declarations — the model has a precise, durable anchor for everything else it has learned about that brand from web crawls, training data, and retrieval.
When a company does not have that record, the model’s internal representation is fuzzier. It may have learned about the brand from web content, but it has no authoritative structured entity to anchor that learning.
The global R² of 1.4% for Wikidata is low for a specific reason: coverage is sparse. The study tracked 1,619 domains with Wikidata records out of 29,562 total — roughly 5.5% of the dataset. In most industries, the signal barely exists to measure. But in the industries where structured reference coverage exists — software, hotels, consumer brands with long histories — the relationship is one of the strongest in the entire dataset.
| Industry | Wikidata rho | R² | n (domains with records) |
|---|---|---|---|
| Furniture stores | +0.706 | 49.9% | 12 |
| ERP software | +0.655 | 42.9% | 12 |
| Hotels & resorts | +0.650 | 42.3% | 37 |
| Accounting software | +0.515 | 26.5% | 22 |
Note the small n values. The reason these correlations are so strong is partly that only the most established brands in each category have Wikidata records at all — so the comparison is between brands with structured entity clarity and brands with none. The effect size is real, but the sample in each industry is narrow. Hotels (n=37) is the most statistically robust; Furniture and ERP (n=12 each) should be read as directional.
Notice the small n values. The reason ERP software shows Wikidata at R² 42.9% is that only 12 ERP domains had Wikidata records — and those 12 dominated the recommendation set. That is how lopsided the advantage is when structured entity clarity exists in a category where most competitors have none.
The Wikipedia instinct is wrong here, and it leads most teams astray. Wikipedia requires demonstrated notability through independent coverage, editorial review, and the risk that a weak entry gets nominated for deletion and creates a reputational incident. Forcing a Wikipedia page for a brand that does not meet notability thresholds is not a GEO tactic. It is a liability.
But Wikidata is different. Wikidata is an open, structured knowledge base that accepts entities far below Wikipedia’s notability threshold. Any brand with verifiable external references can have a Wikidata record. Creating one is a few hours of work. Maintaining it is minimal. And it feeds directly into the reference layer that LLMs rely on for entity classification.
For most B2B SaaS and services brands, the practical entity clarity work looks like this:
On Wikidata: Create or verify your entity record. Include consistent naming, industry classification, founding information, location, key personnel, and sameAs links to your LinkedIn company page, Crunchbase profile, Twitter/X handle, and official website. Add knowsAbout properties that connect the entity to your category and use cases.
On-site schema: Implement clean Organisation schema with the same sameAs references. Add Article schema with full author attribution. Add FAQ schema where appropriate (noting that Google’s March 2026 core update narrowed rich result eligibility, but the schema still feeds AI extraction pipelines).
Across directories: Ensure consistent naming across LinkedIn, Crunchbase, Clutch, G2, DesignRush, GoodFirms, AngelList, BuiltWith, and any industry-specific databases. Inconsistency across these sources creates conflicting signals that weaken the model’s entity representation.
For people: Build author pages that connect key executives and founders to specific areas of expertise. The person-to-brand-to-category relationship matters for high-trust recommendation scenarios, especially for executive buyer personas.
The goal is not “get a Wikipedia page.” The goal is to make the brand easier for machines to classify, verify, and connect to the right recommendation context. Entity clarity compounds over time in a way that most content marketing does not.
Finding #6: Reddit Is an AI Visibility Signal — in the Categories Where It Counts Most
TL;DR:
- Reddit is not the top global signal, but in specific categories it drives more recommendation variance than any backlink metric.
- Enterprise AI Platforms: Reddit posts rho +0.528, R² 27.9%.
- Live Entertainment & Ticketing: Reddit posts rho +0.503, R² 25.3%; Reddit comments rho +0.429, R² 18.4%.
- Cosmetic Surgery Clinics: Reddit comments rho +0.321, R² 10.3%.
- The mechanism: these are categories where buyers use Reddit to vet options before high-stakes or high-friction decisions. The model has learned from that vetting behavior at scale.
- The playbook is not Reddit SEO. It is building the kind of brand that generates authentic community discussion.
The category list tells you everything about why this signal behaves the way it does.
Enterprise AI platforms. Ticketing. Cosmetic surgery clinics. These are not impulse purchases. They are high-stakes, high-friction decisions where buyers have been burned before, do not fully trust marketing content, and actively seek peer testimony from people who have no stake in the outcome.
For enterprise AI tools, the Reddit vetting conversation looks like: which platforms actually work at scale? Which ones have hidden costs? Which ones have support teams that respond when something breaks in production? Which ones are the company’s AI wrappers on top of GPT-4, and which ones have genuine proprietary model development? These are questions that polished vendor content cannot credibly answer. So practitioners go to r/MachineLearning, r/datascience, Hacker News, and internal Slack communities where they trust the answers are real.
For ticketing, the conversation is: does this platform hide fees? Does it fail when everyone tries to buy at once? Does it honor refunds? Has it been involved in scalping controversies? These are trust and reliability questions that only community testimony can answer at scale.
The model has consumed billions of these conversations. Brands that have been discussed honestly, at scale, in these communities — with real user experience, real complaints, and real advocacy — have stronger internal model representations in their categories. Brands with thin or manufactured community presence do not.
This is where the GEO Reddit playbook gets dangerous.
The wrong version: “Reddit is a ranking factor. Let’s post in relevant subreddits.”
That path leads to account bans, community backlash, and a reputational incident that is harder to recover from than having no Reddit presence at all. Subreddit moderators are sophisticated about identifying coordinated brand campaigns. The consequences are permanent and public.
The right version is slower and more valuable: build a brand that generates authentic community discussion, then monitor that discussion as a strategic signal.
For B2B tech companies, that means:
Monitoring category subreddits (r/devops, r/dataengineering, r/SaaS, r/analytics, r/MachineLearning) for questions that reveal what buyers actually want to know about your category — not to respond to them all, but to understand what the model is learning about buyer concerns in your market.
Answering genuinely in communities where you have real expertise, with founder or practitioner accounts that disclose affiliation and contribute substantively over time. One real answer to a specific technical question builds more community trust than 50 promotional posts.
Turning Reddit objections into content. The complaints buyers raise about your category in community discussions are the exact questions the model will surface in recommendations. If buyers consistently ask about data security, implementation timelines, or support responsiveness in your category, your owned content should answer those questions so clearly and specifically that the model has no better answer to pull from.
Encouraging honest reviews on the platforms buyers trust: G2, Capterra, Trustpilot, GitHub issues, and App Store reviews for relevant products. These are adjacent to Reddit in function — they are the community-sourced evidence that buyers seek when they do not trust brand content.
The AI visibility question is not only “what do we say about ourselves?” It is “what does the market say when we are not in the room?” For the categories where Reddit matters, the model has an opinion on that question. The only way to improve it is to be the kind of brand worth talking about honestly.
Finding #7: Persona Context Is Not a Modifier — It Is the Product
TL;DR:
- The same industry, the same model, a different buyer — materially different recommendations.
- In airlines: delta.com is the top industry-wide domain, but no single airline is #1 across all 11 personas. A student abroad flyer gets studentuniverse.com. A senior comfort traveler gets delta.com. A miles-maximizing loyalist gets united.com.
- For B2B SaaS: a CFO, a CTO, an operations lead, and a startup founder evaluating the same category will get different recommendations from ChatGPT.
- Measuring LLM visibility as a single score is measuring noise. Persona-level tracking is the unit that matters.
This is the finding that most changes what the day-to-day work of GEO strategy should look like.
In classic SEO, personalization was a variation. You could track “best ERP software” and get a meaningful approximation of visibility across the market, with some personalization at the edges. The keyword was the unit. You tracked it, reported on it, and optimized toward it.
With LLMs, user context is the center of the recommendation logic — not a modifier applied after the fact. The model does not find a universal best answer and then adjust it slightly for the person asking. It interprets the buyer’s context first, and the recommendation emerges from that interpretation.
| Airline Persona | #1 Recommended Domain |
|---|---|
| Miles-maximizing loyalist | united.com |
| Visiting relatives | southwest.com |
| Adventure route seeker | alaskaair.com |
| Student abroad flyer | studentuniverse.com |
| Senior comfort traveler | delta.com |
Delta is the top airline brand by aggregate score across the full dataset. But Delta is not the top recommendation for every persona. A student traveling abroad gets a completely different brand — one that does not even appear near the top of the aggregate list.
For a B2B SaaS company, this effect is likely at least as large. The model does not have one answer to “what is the best data engineering platform?” It has different answers depending on whether the buyer is a startup CTO who needs something that can be running in a day, an enterprise procurement lead who needs SOC 2 compliance and dedicated support, a data team lead who needs dbt compatibility and strong community documentation, or a CFO who needs transparent pricing and verifiable ROI case studies.
Each of those personas produces a different competitive set. Each requires a different content and evidence-building strategy to win.
The measurement implication is fundamental: a single aggregate “AI visibility score” for your brand is not a useful reporting metric. It averages across contexts that produce genuinely different results, and that average tells you almost nothing about whether you are winning the buyer segments that actually drive pipeline.
Persona coverage — how many of your addressable buyer segments your brand appears in when they ask ChatGPT for recommendations — is a more meaningful unit. So is position within a persona: being the first recommendation for your highest-value buyer segment is worth more than appearing seventh across a broad set.
Building a prompt library segmented by persona is not a one-time exercise. It is an ongoing measurement program, the same way a rank tracker is an ongoing program. The prompts need to mirror real buying language for each segment, run repeatedly for statistical stability, and tracked over time so movement is visible.
Finding #8: High-Value Buyer Personas Have Compressed Recommendation Sets — and High Barriers to Entry
TL;DR:
- The executive hotel traveler persona produced a recommendation set of 4–5 dominant brands. Fairmont scored 0.4 vs. Marriott’s 100.
- For that persona, Reddit comments showed rho +0.915, R² 83.8% — meaning community discussion among executive-level buyers explained most of the recommendation variance.
- The implication for B2B: enterprise executive personas likely produce similarly compressed recommendation sets in software categories.
- To enter a compressed set, you need evidence that matches the buyer’s trust threshold — not more content volume.
Not all personas produce wide, accessible recommendation lists.
The executive business traveler persona in the hotel dataset showed recommendation scores that dropped from a competitive cluster of 79–100 (Marriott, Hyatt, Hilton, Four Seasons) to 0.4 (Fairmont) without a gradual middle. Everything below the top four was effectively invisible to that persona. And for that persona specifically, Reddit comments explained 83.8% of recommendation variance — meaning the community discussion among executive-level hotel users was doing almost all the explanatory work.
That is a small sample (n = 10) and should not be treated as a universal rule. But the pattern it illustrates is real and likely applies across high-stakes B2B categories.
Consider: a CFO evaluating enterprise revenue attribution platforms is not going to receive a list of 20 options. The model’s understanding of what a CFO needs in that context — governance, audit trails, financial-grade accuracy, implementation risk management, enterprise support — is specific enough that the recommendation set compresses toward 3–5 brands that have demonstrated those properties across enough third-party evidence.
Everything below that threshold is invisible to the CFO persona. Not ranked lower. Invisible.
The strategic implication is about what kind of evidence moves you into a compressed set, versus what evidence just adds volume.
Most content strategies are built around volume: publish more educational content, rank for more keywords, generate more organic traffic. That strategy can work well for reaching early-stage buyers doing broad research. It does not work for high-trust executive personas, because the model is not counting your blog posts. It is assessing whether the wider web’s evidence about your brand matches the trust threshold a CFO would apply.
The evidence that tends to move a brand into high-trust recommendation sets looks different from standard content marketing:
- Financial-grade case studies with specific outcomes, named customers, and measurable ROI — not vague “we improved efficiency” summaries
- Implementation and risk transparency — realistic timelines, known challenges, mitigation approaches — not sales-optimized promises
- Analyst and third-party validation — Gartner, Forrester, IDC coverage, industry award recognition, or credible benchmark inclusion
- Executive thought leadership with genuine positions on category-defining debates, not generic “thought leadership” blog posts
- Security and compliance documentation that is public, specific, and auditable
- Community trust — how practitioners who have used the platform talk about it in unmoderated environments
None of that is SEO. All of it feeds the model’s internal evidence about what kind of buyer your brand is credible for.
High-trust personas are the most valuable buyer segments and the hardest to enter in AI recommendations. The brands already in those compressed sets have multi-year head starts in evidence accumulation. The path in is not faster content production. It is building the kind of durable, multi-layer credibility that makes the model confident recommending you when the stakes are high.
What This Means for GEO, AEO, and SEO Strategy in 2026
TL;DR:
- SEO is the infrastructure layer. Without it, you cannot enter the AI evidence pipeline.
- AEO is the extraction layer. Content that answers clearly and cites sources gives AI systems something to pull from.
- GEO is the recommendation layer. It requires evidence across the entire web, not just your own site.
- LLM recommendation optimization adds a fourth layer: persona-specific evidence that makes the model confident recommending you for the right buyer context.
- These are not competing frameworks. They are sequential dependencies.
The biggest strategic mistake in this space is treating GEO as a separate discipline that replaces SEO, or as a new set of tactics grafted onto existing content production.
A better model is that SEO, AEO, GEO, and LLM recommendation optimization are four sequential layers, each dependent on the one below it.
Traditional SEO asks: can Google crawl, understand, and rank this page? Rankings, technical health, internal linking, and backlinks feed the foundational indexing that AI retrieval systems draw from. Without this layer working, nothing above it is reachable.
AEO asks: can an AI system extract a clear answer from this page? Answer-first structure, question-format headers, statistics with attribution, expert quotes, structured data, and FAQ sections shape whether content is extractable as a citable source. The Princeton KDD research showed that adding statistics to content can increase AI visibility by up to 40%, citing authoritative sources produces a similar lift, and expert quotations with attribution add 28%. These are content-level interventions, but they only matter if the page is indexed and trusted at the SEO layer first.
GEO asks: can the brand become a trusted source or recommendation inside generated answers? This layer extends beyond owned content into third-party mentions, earned media, review platforms, community discussion, entity databases, and the wider web’s representation of the brand. The OppAlerts data makes clear that most of the variance in LLM recommendations is explained by signals outside the brand’s own website.
LLM recommendation optimization asks: does the model understand that this brand is a good fit for this specific buyer context at this stage of evaluation? This is the persona layer. It requires evidence built specifically around the trust signals each buyer segment relies on, tested and tracked against persona-specific prompts over time.
Most teams are working seriously on layer one, partially on layer two, and barely at all on layers three and four. That gap is where AI visibility competitors are forming.
A Practical 90-Day Roadmap for LLM Recommendation Visibility
TL;DR:
- Start with measurement, not tactics. You cannot optimize what you have not tested.
- Define your 3–5 highest-value buyer personas before building any prompt library.
- Map your evidence layers against competitors, not just your own site in isolation.
- Build the roadmap around the highest-value gaps, not a generic GEO checklist.
Step 1: Define Buyer Personas as Measurement Units (Week 1–2)
Do not start with keywords or content. Start with market segments.
For a B2B SaaS company, the persona set might include:
- CFO evaluating cost control, ROI, and financial governance
- CTO evaluating architecture, security, and integration risk
- Head of Marketing or RevOps evaluating pipeline attribution and adoption
- Startup founder evaluating speed, flexibility, and self-serve
- Enterprise procurement lead running a formal vendor comparison
- Practitioner or technical lead evaluating implementation complexity and documentation quality
Each persona needs its own prompt set. Each should be tested independently. The aggregate view comes later.
Step 2: Build and Run the Prompt Library (Week 2–3)
Create repeatable prompts that mirror real buying language for each persona:
- “What are the best [category] platforms for [persona context]?”
- “Which [category] vendors are most credible for [use case or constraint]?”
- “Compare [brand] to alternatives for [specific buyer requirement]”
- “What should a [persona] look for in [category] software?”
- “Which [category] companies have the strongest [trust signal: security / compliance / enterprise support / etc.]?”
Run each prompt a minimum of five times. Record which brands appear, at what position, and in what percentage of runs. Rank-weight the results — position one counts more than position five. This is your baseline.
Step 3: Map AI Visibility Against Search Visibility (Week 3–4)
Compare your LLM recommendation visibility with your Google search visibility across the same query set. Look for the four patterns:
| Pattern | Meaning |
|---|---|
| Strong in Google, strong in LLMs | Existing visibility is translating. Defend and compound it. |
| Strong in Google, weak in LLMs | Evidence extraction or entity trust may be weak. Investigate third-party signal gaps. |
| Weak in Google, strong in LLMs | Competitor has strong entity or community signals independent of search. Understand why. |
| Weak in both | Foundational SEO and authority work are prerequisites before GEO investment makes sense. |
Many teams discover here that their SEO competitors and their AI competitors are not the same brands.
Step 4: Run a Competitor Evidence Audit (Week 4–5)
For each brand appearing in LLM recommendations that you are not beating, review:
- Google rankings for your shared target queries
- Backlink profile depth and category relevance
- Wikipedia and Wikidata entity coverage
- Review platform presence (G2, Capterra, Trustpilot)
- Inclusion in comparison and alternative-to pages
- Reddit, Hacker News, and community discussion volume
- High-authority editorial coverage and press mentions
- Author and executive visibility in category publications
- Case study depth and specificity
The goal is to identify what the model may be seeing that your current reporting does not capture.
Step 5: Build the Evidence Roadmap (Week 5–6 onwards)
Turn the gaps into a 90-day priority list. The priority order should be:
- Fix the SEO foundation if it is weak — crawlability, indexing, internal linking, content clarity on commercial pages
- Create or clean up entity records — Wikidata, directory profiles, schema markup
- Publish persona-specific commercial content — comparison pages, alternative-to pages, use-case pages, executive proof content
- Launch or accelerate Digital PR — earned media placements in category-relevant publications, original research, expert commentary
- Strengthen review platform presence — G2, Capterra, Clutch, or whichever platforms your buyers trust
- Monitor and participate authentically in community — not SEO campaigns, but genuine engagement where your buyers ask real questions
The output is not a pile of GEO tasks. It is a market-specific evidence-building plan built around the buyer segments that actually drive pipeline.
Three Mistakes to Avoid in LLM Visibility Strategy
Mistake #1: Using One Prompt as a Ranking Report
LLM outputs vary. The same prompt with slightly different wording can produce different results. One test run is an anecdote. A single observation that ChatGPT recommended a competitor once is not a crisis. A consistent pattern across 20 prompt runs for a specific persona over 60 days is meaningful.
Run prompt clusters. Run them repeatedly. Measure movement, not moments.
Mistake #2: Optimizing Only Your Own Website
Your website is your version of the story. Third-party evidence is what the model uses to decide whether to believe it.
If your brand has weak third-party mentions, thin review presence, inconsistent directory profiles, no community discussion, and no earned media coverage, publishing more content on your own domain will not close the AI visibility gap. The model’s representation of your brand is built from the whole web, and your own site is a small fraction of that input.
Mistake #3: Reporting Aggregate AI Visibility as a Single Score
A single “AI visibility score” hides more than it reveals. You may be visible to startup founders and invisible to enterprise executives. You may appear in generic category prompts and disappear in high-intent migration, security, or compliance prompts.
Persona coverage is the unit. Track it explicitly, by segment, over time.
Final Takeaway
The OppAlerts LLM Ranking Factors Report is the most rigorous public analysis of AI recommendation signals published to date. Its core finding is not what most GEO frameworks want to hear: there is no single factor, no universal checklist, and no shortcut that explains more than a fraction of LLM recommendation behavior from the outside.
What the data does give is a framework for asking the right questions.
For this industry: what signals actually correlate with recommendations, and how different are they from global averages?
For this competitor: what evidence layers do they have that we do not?
For this buyer persona: what does the model understand about who this buyer is and what they need — and does it understand our brand as a credible answer?
Those questions are harder to answer than “how do I rank in ChatGPT.” But they are the questions that lead to strategy instead of tactics, and to AI visibility that compounds instead of collapses.
The brands that build enough credible evidence across the web for the model to understand clearly what they do, who they are best for, and why they are trustworthy for specific buyer contexts — those are the brands that will hold AI recommendation positions as the technology matures.
The window for building that foundation ahead of competitors is still open.
FAQ
Does improving Google rankings automatically improve ChatGPT recommendations?
Not automatically, and not proportionally. The OppAlerts data shows a positive correlation between search engine appearances and LLM recommendation scores (rho +0.241), but that correlation explains only 5.8% of variance. The hotel example in the report makes the gap concrete: Tripadvisor is the #1 search domain with 223 SERP appearances; Marriott is the #1 LLM-recommended domain at 49.1% of query runs. Two different brands at the top of two different visibility systems. SEO is a foundational prerequisite — pages that cannot be found or indexed by search engines are unlikely to enter AI retrieval pipelines — but ranking improvements do not translate directly into AI recommendation improvements. Both need to be tracked and optimized separately.
Is Wikidata worth investing time in for a B2B SaaS company?
Yes, and it is one of the lowest-effort high-leverage actions available. Creating or improving a Wikidata entity record for your company, with accurate naming, industry classification, sameAs references to your official profiles, and knowsAbout properties covering your category, takes a few hours. The ERP software data (Wikidata rho +0.655, R² 42.9%) suggests that for software categories, entity clarity can be a dominant signal. Most B2B SaaS companies have no Wikidata record at all.
How do I measure LLM recommendation visibility by persona?
Build a prompt library with 5–10 prompts per buyer persona, written in the natural language that persona would use when asking for vendor recommendations. Run each prompt a minimum of five times across ChatGPT, Perplexity, and Gemini. Record which brands appear and at what position. Rank-weight the results. Repeat the audit on a monthly or bi-weekly cadence and track movement over time. Set up GA4 AI referral tracking to correlate prompt visibility with actual referral traffic. This is the minimum measurement infrastructure for GEO strategy.
Are Reddit and community signals worth investing in for B2B tech?
In categories where buyers actively seek peer testimony — enterprise AI platforms, developer tools, security software, data infrastructure — yes, substantially. The Enterprise AI Platform data (Reddit posts R² 27.9%) suggests community discussion explains more recommendation variance than any backlink metric in that category. The investment is not posting in subreddits. It is building a brand that practitioners discuss honestly, monitoring those discussions for strategic intelligence, and ensuring your content answers the real objections buyers raise in community environments.
What is the difference between SEO, AEO, and GEO?
SEO asks whether search engines can find, understand, and rank your pages. AEO asks whether AI systems can extract clear, citable answers from your content. GEO asks whether your brand appears as a trusted recommendation inside AI-generated answers. LLM recommendation optimization adds a fourth layer: whether the model understands your brand as a credible fit for specific buyer contexts and personas. These are sequential dependencies, not competing frameworks. You need the layers below before the layers above can work.
References
Primary Source
Related Analysis from The Digital Bloom