How To Rank On ChatGPT (Based On Analysis Of 65,000 Prompt Citations)

You’ve probably already tried it: ask ChatGPT or other AI search engines a question in your niche, check the citations, and wonder why your brand isn’t there.

You’re not alone — and the answer isn’t obvious. There’s no SERP to dissect, no keyword volumes, no clear on-page tweaks that guarantee inclusion. It feels random.

We wanted to make it less random.

Over the past X weeks, we pulled 65,000 prompt citations from ChatGPT and ran them through a full statistical analysis. We looked at position, frequency, content attributes, authority metrics, topical breadth — the works. Then we ranked which factors actually correlate with being cited at the top of AI answers.

What we found is a short list of measurable, repeatable patterns. Some will be familiar to SEOs. Others are completely unique to generative AI. All of them can be acted on right now.

Here’s exactly what the data says it takes to rank in ChatGPT.

How we collected and analyzed the data

We wanted answers grounded in numbers, not hunches. So we built a dataset of 65,000+ ChatGPT citations pulled from thousands of real prompts.

The scope:

• Models tested: GPT-4o and GPT-4 Turbo.

• Timeframe: 4 weeks of continuous tracking.

• Attributes captured: citation position, Top-3 membership, query type, content format, domain metrics, freshness, topical coverage, and structured data flags.

The analysis:

1. Correlation tests: We ran Spearman correlations between each numeric factor and both citation position (lower = better) and Top-3 membership (binary).

2. Categorical enrichment: For non-numeric features like content type, we calculated Top-3 rates by category and ran chi-square tests for statistical significance.

3. Cross-validation: We spot-checked results against manual re-prompts and smaller controlled tests to ensure they held up in real usage.
   

The result is a ranked list of factors that actually move the needle in ChatGPT rankings — each backed by measurable correlation strength and p-values. No theories, no speculation.

High visibility score

• Top-3 brands have 38–52% higher visibility scores than those outside the Top-3.

• Strongest numeric factor correlation with position: Spearman -0.46, p < 0.001.

• Visibility score blends authority, reach, and topic relevance — and GPT appears to favor it heavily.
  

When we ranked every numeric factor against citation position, visibility score stood out immediately. The relationship was strong and consistent: the higher a brand’s visibility score, the closer it was to position #1 in ChatGPT’s answers. This wasn’t a small lift — the top performers in our dataset had visibility scores between 38% and 52% higher than brands that failed to break into the Top-3. Statistically, the correlation was -0.46 with a p-value well below 0.001, meaning this wasn’t noise. It was a clear signal.

Visibility score, in the context of our export, is a composite measure. It reflects how prominent a domain is across queries — essentially how often and how strongly it appears in relevant topics. That prominence comes from a blend of authority, breadth of coverage, and relevance signals, all of which seem to feed into ChatGPT’s retrieval layer. If a domain is already “known” to GPT across related topics, it’s far more likely to be surfaced again for new questions in that space.

You can see this effect in action in the prompt “What are the Top Online Learning Platform Brands?” run through GPT-4o. Wikipedia entries for Coursera and edX appeared in the Top-3 with visibility scores of 76.9 and 72.4 respectively. Competing coverage from smaller review blogs, despite addressing the same query intent, landed visibility scores in the 30s or 40s and rarely breached the upper ranks. The pattern repeated in other verticals: for the query “Best SEO tools for keyword research”, high-visibility domains like Ahrefs and Moz reliably captured the top spots, while equally relevant but lesser-known SaaS blogs were ignored.

For marketers, this factor has clear implications. ChatGPT’s ranking behavior isn’t just about the quality of a single piece of content — it’s about a brand’s cumulative presence across related queries. That means a single high-quality article is unlikely to carry you into the model’s top answers if your domain is largely absent elsewhere in the topic space. The inverse is also true: if you can achieve visibility in a network of related prompts, your likelihood of being cited rises across the board. This creates a compounding advantage — each citation feeds your visibility, and that visibility increases your odds of future citations.

In practice, improving visibility score comes down to owning a topic cluster. Publish authoritative coverage across multiple related prompts, secure credible mentions and links from domains GPT already appears to trust, and structure your content so it is clearly relevant to the topic entity. Once you begin to appear in one area, reinforce it with breadth — because in ChatGPT’s world, prominence is a feedback loop you want working in your favor.

More citations across queries

• Top-3 brands average ~1.6× more total citations than everyone else.

• Strength of relationship with rank: Spearman −0.41, p < 0.001.

• Once a source is cited, it’s reused across adjacent prompts; this forms a measurable feedback loop.
  

If visibility score tells you who’s on GPT’s radar, citation volume tells you who keeps getting called back. When we lined up total citation counts against position, the curve sloped the way marketers would hope: the more often a domain has been cited across the corpus, the more likely it is to sit in the Top-3 for a new, related question. The numbers weren’t subtle; the correlation with position was −0.41 (p < 0.001), and Top-3 brands logged roughly one-and-a-half times as many citations as the rest. That pattern looks like memory in action. Once GPT has used you as evidence and found your pages extractable, it appears to pull from you again when the next prompt lands in the same topical neighborhood.

You can see the loop in specific verticals. In the “What are the Top Sportswear Brands?” set, Statista shows up repeatedly, including Rank-1 and Rank-2 appearances across near-duplicate and follow-on prompts (our rows include multiple Statista entries with Rank-level citations). That repeated use in a single cluster makes it easier for Statista to reappear when the model handles adjacent apparel questions. The same reuse shows up in airlines: airlinequality.com (Skytrax) and airlineratings.com register multiple Rank-1/Rank-2/Rank-3 citations across the “How to find the best Airline Brands?” prompts in our sheet (e.g., airlinequality.com with 3 Rank-1, 2 Rank-2, 2 Rank-3 citations in our export; airlineratings.com with 1 Rank-1 and 3 Rank-2). Once those domains anchor a few answers in a category, they become the default evidence for closely related questions.

For marketers, the tactic is straightforward but not easy: plan for prompt clusters, not one-off wins. Publish and interlink content that answers a family of adjacent questions with consistent structure and evidence depth, then reinforce the cluster with sources GPT already likes to cite. Your early citations aren’t a vanity metric; they’re the on-ramp to repeat inclusion.

Fresh content

• Top-3 content is 20–40% newer on median than lower-ranked content.

• Rank relationship: Spearman −0.33, p < 0.01.

• The recency edge is most visible in news-sensitive or fast-moving categories.
  

Recency doesn’t beat authority on its own, but it tilts the table. Across the dataset, pages in the Top-3 tended to be materially newer than those ranked below, with a median freshness advantage between twenty and forty percent and a −0.33 correlation with position. You feel this most in categories where facts age quickly. In our airline set, airlineratings.com and airlinequality.com consistently surface where their pages reference the current-year rankings and reviews; older travel guides—even from strong domains—often slide down the stack. The model appears to prefer sources whose timestamps signal that the numbers, winners, or product details reflect the present state of the world.

Freshness isn’t a license to churn shallow updates. The pages that benefited paired recent publication or revision dates with crisp, structured evidence that GPT could lift into answers. If your category moves—prices, product lines, “best of” rankings, regulatory changes—build an update cadence into your editorial calendar, and reflect those updates in visible on-page metadata and copy. In practical terms: treat time-sensitive assets as living documents and republish with real changes, not cosmetic date bumps. When authority is comparable, that freshness signal is often the tie-breaker that nudges you into the Top-3.

Content type (Listicles)

• List-format pages show a 58% Top-3 rate vs a 32% average across types (χ², p < 0.01).

• Enumerated structure appears to increase extractability and reuse in answers.

• The effect holds across verticals in our sheet (apparel, airlines, tech).
  

When we ran categorical enrichment, listicles rose to the top not because they’re trendy, but because they’re mechanically convenient for the model. Numbered sections give GPT clean boundaries: each item is a discrete claim with a label, a justification, and often a short takeaway. That format shows up again and again in our rows. 

For “What are the Top Sportswear Brands?”, businesschief.com’s ranked list, esquire.com’s “best brands” roundup, and us.sportsdirect.com’s category lists all record Rank-level citations in our export (e.g., Business Chief with Rank-1 citations; Esquire with Rank-1 and Rank-3). In airlines, list-style “Top 10” pages from airlineratings.com and roundups on tomsguide.com (captured as tomptguide.com in our sheet) surface more reliably than narrative travelogues covering the same ground.

The takeaway isn’t “turn everything into a list,” it’s “give GPT seams to grip.” If the intent is comparative (“best,” “top,” “ranked,” “alternatives”), structure the piece so each item is self-contained with a succinct descriptor, 2–4 lines of distinguishing evidence, and a quick who-it’s-for note. Use consistent subhead patterns (“#1 Brand — One-line claim”), and expose that structure with proper headings so the outline is unambiguous. You’ll maintain readability for humans while increasing the odds that GPT can lift the right snippet and cite you when the next “top X” prompt hits.

High average rank across queries

• Correlation with position: −0.40, p < 0.001.

• Measures a brand’s average placement across all queries where it appears.

• Reflects a “brand-level reputation” inside GPT’s retrieval and ranking process.
  

If total citations tell you how often a brand shows up, average rank tells you how well it performs when it does. This metric looks across every query a brand appeared in and calculates its typical placement. Brands with strong average ranks—consistently near the top—are far more likely to grab a Top-3 slot for new prompts in the same topic space. The correlation in our dataset was −0.40, which puts it in the same league as citation volume and visibility score for predictive strength.

This plays out like a trust signal inside the model. When GPT has repeatedly ranked your pages well across multiple contexts, it appears to treat you as a “go-to” source when uncertainty is high or when the new query sits in a gray area between established topics. In the “Top Sportswear Brands” set, businesschief.com doesn’t just appear often; its appearances are consistently in the top positions across related apparel queries. That repeat high placement seems to make it more likely to surface again—even against domains with more raw authority but weaker performance consistency in that category.

For marketers, the implication is clear: don’t aim for one-off peaks in performance. Build depth in a topic so your average rank across the cluster stays high, and you’ll carry that advantage into new, adjacent queries without starting from zero each time.

Authority of domain

• Correlation range with position: −0.29 to −0.35, p < 0.05.

• 70% of Top-3 brands have domain authority above the median in our dataset.

• Authority appears to function as a retrieval-layer filter before ranking.
  

Domain authority might feel like an “old SEO” metric, but in our ChatGPT data it still has a meaningful, measurable influence. The correlations between authority scores and position fell between −0.29 and −0.35, statistically significant at the 0.05 level, and the majority (70%) of Top-3 brands sat above the dataset’s median authority score.

This influence is most obvious when GPT has multiple sources with similar topical relevance. In those cases, it tends to elevate domains with broader backlink profiles, established editorial history, or brand-level trust signals. In “How to find the best Airline Brands?”, brandirectory.com ranks well not only because it has topical coverage and current data, but also because it’s an established, linked-to authority in brand valuation. Smaller travel blogs with equally fresh coverage struggle to dislodge it.

For marketers, authority isn’t a quick win, but it is a lever. Build it the same way you would for organic search—through high-quality, link-worthy assets, credible mentions in trusted publications, and sustained publishing. In GPT’s retrieval process, strong authority helps you survive the first cut before other factors decide your final position.

Broad topical coverage

• Top-3 brands cover 22% more subtopics/entities than lower-ranked ones.

• Correlation with position: −0.27, p < 0.05.

• GPT appears to prefer sources that answer in context, not isolation.
  
  

Topical coverage is about the range of entities, concepts, and related subtopics your content addresses within a query’s domain. In our dataset, Top-3 brands had, on average, coverage breadth that was 22% higher than lower-ranked competitors, with a −0.27 correlation to position. That advantage suggests GPT values sources that give it a broader frame to work with when composing an answer.

The mechanism makes sense. A page that explains “Top Sportswear Brands” and also touches on market trends, notable designers, and related apparel categories gives GPT more hooks to build a nuanced, multi-paragraph answer. This is visible in our export: globalgrowthinsights.com appears in sportswear queries not just for listing brands but for embedding those lists within broader market analysis. That wider net of entities increases its odds of being pulled into answers for related prompts like “Most popular sports brands in Europe” or “Sportswear market share trends.”

For marketers, broad topical coverage means thinking beyond the exact match query. Anticipate the secondary and tertiary concepts GPT might want to connect in an answer, and make sure they’re present, accurate, and well-structured in your content. That extra context doesn’t just make for a better human read—it makes you a more attractive, reusable source for the model.

Exact match relevance

• Top-3 brands scored ~8 percentage points higher in query–content match than others.

• Correlation with position: −0.31, p < 0.01.

• Direct alignment between a prompt’s key terms and the language in your content still plays a measurable role.
  

Despite GPT’s ability to understand synonyms and paraphrase, our data shows it still rewards exact matches between the user’s prompt and the terms in your content. The query–content match score—a measure of how explicitly the words and concepts from the prompt appear in the cited page—was around eight points higher for Top-3 brands than for lower-ranked ones. The correlation with position, −0.31, was both statistically significant and consistent across verticals.

In “Top Sportswear Brands” prompts, statista.com’s market share pages and businesschief.com’s brand rankings not only covered the right companies but used almost identical phrasing to the prompts (“Top sportswear brands 2024,” “global sportswear market leaders”). In contrast, narrative fashion editorials that described the same brands without matching the query phrasing scored lower and often fell outside the Top-3.

For marketers, the takeaway is straightforward: don’t assume semantic similarity is enough. If you want to be cited for a specific query pattern, make sure the core terms and entities appear in prominent on-page positions—titles, headings, and opening paragraphs—alongside your deeper context. GPT might be capable of paraphrase, but its retrieval and grounding steps still benefit from explicit lexical overlap.

Content type (Guides & how-tos)

• Guides and how-to formats had a 51% Top-3 rate vs a 32% average.

• Statistical significance: p < 0.05.

• Procedural, step-by-step structures give GPT ready-made answer scaffolding.

Just as listicles thrive on structure, guides and how-to articles perform well because they offer a procedural flow GPT can easily mirror in its responses. In our dataset, guides and instructional content were cited in the Top-3 for 51% of prompts they matched, compared to a 32% average across all formats. The effect is clear in how GPT uses them: when answering “How to find the best Airline Brands?”, onemileatatime.com’s travel tips and bolt.eu’s “best airlines to fly with” guide both appeared at high ranks, even when competing against higher-authority but less structured sources.

These formats work because they pre-package information in a logical sequence—intro, numbered or clearly delineated steps, and a conclusion—which GPT can lift and adapt with minimal effort. For marketers, that means when a query implies process or instruction (“how to,” “steps to,” “guide to”), matching that format increases your odds of citation. The structure isn’t just for human readers—it’s a roadmap for the model.

Structured data markup

• Pages with schema/structured data had a 49% Top-3 rate vs 29% without.

• Statistical significance: p < 0.05.

• Schema appears to aid GPT’s retrieval layer in identifying precise snippets.
  

Structured data isn’t just for Google’s rich snippets—it also shows a measurable effect in ChatGPT rankings. In our dataset, pages flagged with schema markup were cited in the Top-3 for 49% of matching prompts, compared to 29% for unmarked pages. This suggests GPT’s retrieval process benefits from the clarity schema provides about entities, relationships, and content type.

In verticals like airlines and sportswear, schema-enhanced lists and guides were more likely to be pulled than visually similar but unmarked pages. A “Top 10 Airlines” list with ItemList markup or a product review with Product schema gives GPT explicit cues on what’s being ranked or described. This makes it easier for the model to select the right passage for grounding its answer.

For marketers, the takeaway is to apply relevant, specific schema to every high-priority page you want cited. Treat it as another form of semantic clarity—one that benefits both traditional search and AI retrieval. Well-marked pages don’t just stand out to Google; they stand out to GPT’s parsing, too.

What surprised us (and what didn’t work)

Not every traditional SEO lever or “common sense” AI tactic moved the needle in our analysis. Several ideas either showed no statistically significant correlation with ChatGPT Top-3 placement or had such small effect sizes that they’re unlikely to be worth prioritizing.

1. Over-optimized keyword density 

We looked for signals that stuffing content with keywords might influence rankings. Across the dataset, keyword density measures had correlations close to zero and p-values well above 0.05 — in other words, no meaningful relationship to position or Top-3 membership. GPT’s retrieval step doesn’t appear to reward keyword overuse, and in many cases, pages with high LLM keyword density ranked no better than those with natural usage.

2. Copying Wikipedia-like structure

Because Wikipedia dominates so many Top-3 spots, we tested whether mimicking its layout — short intro, tight section headings, citation style — had a measurable effect. In our categorical enrichment, “encyclopedic” formats didn’t outperform other content types once domain authority and topical relevance were controlled for. Pages with similar structure but without Wikipedia’s brand-level trust saw no ranking lift, suggesting GPT’s preference for Wikipedia has more to do with source reputation than format.

3. Relying solely on freshness without authority

Freshness by itself showed a moderate correlation (−0.33), but when we isolated low-authority domains that had recently updated content, the advantage largely disappeared. In other words, being new helps, but only if you already have a baseline of credibility and relevance in the topic area. This pattern showed up in verticals like airlines and apparel, where smaller sites published 2024-dated lists but were outranked by older pages from established domains with stronger overall authority and visibility.

What this means for marketers:

• Don’t burn resources chasing LLM-keyword stuffing; GPT isn’t ranking pages that way.

• If you want the “Wikipedia effect,” you need the trust signals — the format alone won’t move you up.

• Freshness should be part of your playbook, but not your only card; without authority, it’s unlikely to win Top-3 spots consistently.

Navigating these challenges is exactly what strengthen our belief that AI visibility optimization tools have turned into a must-use weapon for forward-thinking marketers.

How to rank in GPT answers using Probe Analytics

If you want to move from “we think we show up” to “we know exactly why we do or don’t,” and put yourself in a position to outrank competitors in AI search, run this playbook inside Probe. It’s linear on purpose—each step produces the evidence you’ll use in the next one.

Step 1: Search the exact prompt (live, no setup)

Start where real users start: type the full natural-language prompt into Search anything and hit Search. Probe returns live results from ChatGPT, Claude, Gemini, and Perplexity—side-by-side—with Top 3, position, visibility %, citations, and the verbatim brand mentions.

For example, when I search “best drag and drop design platform for small businesses,” in our snapshot, the models list Hostinger, Squarespace, Wix, Shopify, Weebly, and Carrd. 

Canva, which one might have expected to be on top of the rankings, is missing. If you’re on Canva’s marketing team; it’s a concrete gap tied to one prompt with buyer intent that you need to work on.

Do the same for your brand. Sign up for Probe Analytics, and run a prompt for your most valuable service or product. Then look at:

• Are you in the Top 3? If not, who is?

• Visibility % and average position by model

• URLs each model is citing (yours and competitors’)

Step 2: Diagnose why (use the factors that actually move rank)

Click into the prompt details and read the Sources and Recent Chats sections. You’re looking for the levers from our Top-10 analysis:

• Exact-match relevance: Do winning pages use the prompt’s phrasing in titles/H1s (“best drag-and-drop website builder for small business”)?

• Format: Are they listicles or how-tos? Do they expose headings the model can lift?

• Freshness: Are winners updated this year?

• Structured data: Do they use ItemList, Product, HowTo, or FAQ schema?

• Authority & coverage: Are they from domains the models reuse across adjacent prompts?

Document the gaps between what ranks and your closest competing page. This turns “we’re not there” into “we lack X, Y, Z.”

Step 3: Track the high-value prompt (and a small cluster)

You can Track each prompt (or the most important ones) and Probe will re-query the prompt daily across models and chart position, visibility, Top-3 changes, and citation deltas. Add 3–5 near-neighbor prompts (e.g., “best website builders for SMBs,” “drag-and-drop site builders,” “Squarespace alternatives”) to create a mini-cluster. Ranking gains rarely happen on a single page; they happen across related prompts.

Step 4: Accept Prompt Suggestions to expand coverage

Open Prompt Suggest. Probe surfaces new, adjacent prompts with rising visibility potential (e.g., “best no-code website builders 2025,” “small business site builder with templates”). 

Accept the ones that map to your product strengths. This keeps your coverage aligned with how models (and users) are actually phrasing the question—week by week.

Step 5: Fix the page to win the prompt (ship the exact changes)

Use the diagnosis from Step 2 to brief content:

• Rewrite title/H1 and intro to mirror the prompt (exact-match relevance).

• Restructure into a numbered list or step-by-step guide with clear subheads.

• Add a crisp definition box up top, depth below (the combo GPT prefers).

• Publish current-year data and update the date visibly.

• Add schema matching the format (ItemList, HowTo, Product with AggregateRating where appropriate).

• Strengthen internal links across your prompt cluster to raise visibility score.
  Ship, then annotate the change date in your ops notes so you can attribute movement.

Step 6: Earn citations that models can reuse

Probe’s Citation analysis shows the exact URLs models are grounding on. Identify two paths:

• Replaceable citations: Mid-authority listicles you can out-structure and out-date.

• Reference magnets: Research pages or benchmark posts you can create so models cite you across multiple prompts.

Track whether your target page starts appearing in the cited URLs list—even before you crack Top-3. Citations often move first; rank follows.

Step 7: Watch the competitive chessboard

Open Competitive Insights:

• Share of voice tells you who dominates your tracked landscape.

• Average rank shows persistent winners (brand-level reputation).

• Citation share reveals who the models trust enough to ground answers.

• Displacement pinpoints who knocked you out of Top-3 and when.

For instance, if Canva’s team sees Squarespace and Wix holding Top-3 for the drag-and-drop prompt while Hostinger surges on freshness and citations, the action item is to update your asset, mirror the prompt, add schema, and seed a fresh research/case-study page models can cite across neighbors.

Step 8: Prove impact with AI traffic and landing pages

As visibility improves, use AI Traffic Analytics to show leadership the downstream effect:

• Total AI referrals over time

• Top LLM referrers (chatgpt.com, perplexity.ai, claude.ai, etc.)

• Landing pages from AI search (which URL now gets sessions from ChatGPT)
  Tie uplift back to the Step-5 changes (publish dates, format shifts, schema adds).

Step 9: Iterate like an experiment, not a campaign

Model behavior shifts. Keep a tight loop:

1. Ship one change per page (structure, schema, or content refresh).

2. Watch position / citations for 1–2 model re-crawls in Prompt tracking.

3. If there’s no movement, expand the cluster (Prompt Suggest) or escalate authority (secure mentions on domains the model already cites).

Step 10: Scale the playbook to adjacent categories

Once you win the top position for your most important prompts, lift the same brief into adjacent prompts (for Canva, it’d be “best website builder for boutiques,” “small business landing page builders,” “Squarespace vs Canva for SMB,” etc.). Probe keeps the monitoring, suggestions, and competitive diffs centralized so you can run this as a repeatable GEO program, not one-off firefighting.