SnowSEO

Category: Artificial Intelligence

  • Ahrefs Keyword Explorer Review for AI Search Research

    Ahrefs Keyword Explorer Review for AI Search Research

    Quick Summary: Ahrefs Keyword Explorer is a solid first step for AI search research, but it only measures Google search demand, not AI visibility. It excels at finding topics and keywords from AI answers, yet it cannot track how often ChatGPT or Claude mention your brand. For that, you need a dedicated AI-visibility tool like SnowSEO or Ahrefs’ Brand Radar. Use Ahrefs to find prompts worth winning, then measure actual AI mentions separately.

    An AI answer can mention a tool or topic before it ever shows up in your keyword list. So the real question: can Ahrefs turn that signal into reliable search research? This Ahrefs Keyword Explorer review tests exactly that. We put it through a real AI-search workflow and share what holds up. Having tested dozens of keyword tools across client campaigns, we focus on evidence, not hype.

    Testing Keywords Explorer on an AI-Surfaced Topic

    From an AI Answer to a Searchable Seed

    I ran one test for this Ahrefs Keyword Explorer review. ChatGPT named “generative engine optimization” as a hot topic, so I plugged that phrase into Keywords Explorer as a seed. The tool returned volume, Keyword Difficulty, and keyword ideas like “geo vs seo” and “generative engine optimization tools.” You can also brainstorm seed keywords with AI prompts inside the tool itself, which helps when your AI answer is vague.

    Two limits showed up fast. AI prompts have almost no search volume history, so trends lag behind. And the tool cannot research keywords globally in full depth beyond what its index covers. For a newer AI topic, treat the numbers as a starting point, not proof.

    The screenshot below shows the dashboard I used for this test.

    Ahrefs Keywords Explorer
    Ahrefs Keywords Explorer

    Also Read: 7 Keyword Tools Combining Search Intent and GEO Data

    What Keywords Explorer Does Well for AI Search Research

    Demand, Intent, and Topic Consolidation

    Keywords Explorer’s real strength is demand data. It pulls from a database of roughly 28 billion keywords across more than 200 countries, so you can check volume, Keyword Difficulty, and traffic potential for almost any query. For AI search work, the useful metrics are Parent Topic, which groups related queries under one broader topic, and Traffic Potential, which shows what a top-ranking page earns from all its keywords combined. Ahrefs’ own docs show how a 500-search query can drive thousands of visits through traffic potential and parent topics.

    You also get intent labels (informational, commercial, transactional) and AI-powered SERP analysis. That matters because LLMs pull from pages that cover a whole topic, not one exact-match keyword. Ahrefs notes that LLMs increasingly use link graph data too, so Keyword Difficulty still helps you judge which pages AI engines can cite.

    Where it stops: this is classic search research. It won’t show how ChatGPT or Claude mention your brand. For that you need an AI-visibility layer, which SnowSEO and tools like Peec AI or Otterly.AI handle.

    Also Read: 10 Best Tools for Competitor SEO Analysis in 2024

    Where Keywords Explorer Falls Short for AI Search

    Keyword Demand Is Not AI Visibility

    Keywords Explorer tells you how many people search Google. It says nothing about how often ChatGPT mentions your brand. That’s a different game with different rules.

    Ahrefs itself admits AI can’t be tracked like traditional search. AI answers are probabilistic, not fixed. The same prompt returns different brands on every run, and there are no ranked positions to measure. Ahrefs explains this in detail: prompt volume data is locked away, so “hidden demand” is the core problem.

    Keywords Explorer also misses the metrics that matter in AI search:

    • Mention rate – how often AI names your brand per 1,000 relevant prompts
    • Citation rate – how often your pages get cited as a source
    • AI Share of Voice – your slice of category mentions versus competitors

    Even Ahrefs’ newer “AI adjusted volume” is an estimate derived from Google search volume, not real prompt data, because no AI platform publishes user queries. So treat volume numbers as a starting point for what to test, then measure actual visibility with a dedicated GEO tool. SnowSEO, for example, tracks mentions across ChatGPT and Claude alongside keyword research in one platform.

    Split dashboard comparing search volume and AI mentions
    Split dashboard comparing search volume and AI mentions

    Also Read: SnowSEO vs Ahrefs: Which SEO Tool Covers More Workflows?

    Is Ahrefs Keywords Explorer Worth Using for AI Search Research?

    Yes, but not on its own. Keywords Explorer is a search-demand tool. It shows volume, difficulty, and clicks for Google queries, and it helps you find the questions and topics that trigger AI Overviews. What it does not do is track your brand inside AI answers. For that, Ahrefs uses a separate product called Brand Radar, which measures mentions, citations, and share of voice across ChatGPT, Perplexity, Gemini, and AI Overviews.

    So the honest verdict: Keywords Explorer is worth using as step one of an AI-search workflow. It finds the prompts worth winning. Then you need a dedicated AI-visibility layer (Brand Radar, or an all-in-one platform like SnowSEO) to measure whether you actually win them.

    Marketer comparing keyword tool with AI answer panel
    Marketer comparing keyword tool with AI answer panel
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    Ahrefs tells you what people search. SnowSEO shows what AI platforms say about you – and helps you fix it. Start your free trial today.

    Frequently Asked Questions

    Q1: Is Ahrefs Keyword Explorer useful for researching topics surfaced by AI search?

    Yes, for keyword data. It shows search volume, difficulty, and related questions, which help you plan AI-search content. But it doesn’t track AI citations. Pair it with a GEO tool like SnowSEO for that.

    Q2: Can Ahrefs Keyword Explorer show if ChatGPT recommends my brand?

    No. It only covers Google and search data. For AI visibility, you need a dedicated tracker like SnowSEO, Otterly.AI, or Peec AI.

    Q3: What’s the best workflow combining Ahrefs with AI search tools?

    Use Ahrefs to build keyword and topic lists, then test those topics in AI platforms to see who gets cited. SnowSEO automates this monitoring and links it to content fixes.

    Q4: Is Keyword Explorer enough for a small business doing AI SEO?

    Not alone. It handles research well, but you still need citation tracking and content optimization. SnowSEO bundles both in one platform, which saves smaller teams juggling multiple subscriptions.

    Conclusion

    Keywords Explorer is a top-tier keyword research tool, not an AI-visibility tracker. Pair its keyword data with dedicated AI search tools, and your workflow covers both Google and AI answers like Ahrefs itself recommends.

  • Google AI Search Research Reveals New Keyword Opportunities

    Google AI Search Research Reveals New Keyword Opportunities

    Quick Summary: Google’s AI search uses query fan-out, splitting one typed query into many hidden sub-searches, so keyword opportunities now lie in longer, specific questions like comparisons and follow-ups. Pages answering several related sub-queries are 161% more likely to earn AI citations, so build question clusters from a seed query by grouping synonyms, follow-ups, and constraints. Track AI citations directionally via Search Console and tools like SnowSEO, but stick to standard SEO fundamentals since no special markup is needed.

    Google’s AI search research now points to new keyword opportunities. Both AI Overviews and AI Mode can use query fan-out, running several related searches before answering. So the opportunity isn’t just the phrase someone types first. It’s the questions Google generates around it. This analysis covers what Google has verified, the keyword opportunities that follow, and a simple method for building a question cluster from one target query.

    Query Fan-Out Expands One Search Into a Question Field

    What Google Has Confirmed

    Google confirms that AI Mode splits your question into subtopics and issues multiple related searches at once, then combines the results into one answer. That single sentence changes keyword research: one typed query can trigger a whole family of hidden subqueries, each one a separate shot at visibility. This is where new Google AI search keyword opportunities come from.

    Why the First Query Is Not the Whole Opportunity

    The words you type are not the words Google searches. Fan-out queries include comparisons, reformulations, and next-step questions. Pages that answer several subqueries get cited more often, which is why tools like SnowSEO now map Google AI search keyword opportunities by intent clusters, not single phrases.

    Also Read: SnowSEO – Your Last SEO Platform

    The Most Promising Keyword Opportunities Are Longer, More Specific Questions

    Google AI search keyword opportunities sit in the long tail now. Google’s own guidance confirms its AI features use query fan-out, splitting one broad prompt into many narrower searches Google’s AI optimization guide. Two query types stand out.

    Comparison and Constraint Queries

    • “X vs Y for [use case]”
    • “best [tool] under $[price]”
    • “[category] for [niche]”

    These work because AI answers need hard trade-offs. Generic listicles can’t cover them, so specific pages win the citation.

    Two analysts comparing constraint query data on a clean desk
    Two analysts comparing constraint query data on a clean desk

    Follow-Up and Task-Based Queries

    AI Mode users ask follow-ups like “how much does it cost?” or “is it worth it?” Each follow-up is a separate retrieval pass. Build FAQ sections that mirror these exact phrasings, and tools like SnowSEO can map the question clusters for you.

    Also Read: Keyword Research vs SEO Audits for Generative Search Success

    How to Turn One Target Query Into a Defensible Keyword Cluster

    Start with one seed query and expand it the way Google’s query fan-out does: synonyms, follow-ups, comparisons, constraints. Ask a model to list the sub-questions a complete answer would need, then check People Also Ask and forum threads for real phrasing.

    Build the Expansion Map

    Group the results by intent type: equivalent phrasings, follow-up questions, narrower specifications, and broader category terms. Each group becomes a node in your map. A seed like “best CRM for small teams” should yield 10 to 15 variants, from pricing questions to integration comparisons.

    A mind map expanding search query clusters
    A mind map expanding search query clusters

    Validate, Consolidate and Prioritize

    Merge near-duplicates into one canonical question, since AI matching covers adjacent phrasings anyway. Rank remaining nodes by business value and how directly you can answer them. Pages ranking for fan-out sub-queries are 161% more likely to earn citations, so prioritize coverage over volume.

    Also Read: Google AI Updates Change How Marketers Measure Search Visibility

    What the Shift Means for SEO Teams Now

    Measure Visibility Without Overclaiming

    Google confirms it reports AI Mode and AI Overview traffic in Search Console’s Performance report, so that’s your baseline. But it never shows which hidden sub-queries fan-out runs. Tools like Otterly.AI or AthenaHQ estimate AI citations from sample prompts. Treat those numbers as directional, not exact.

    The Strategic Bottom Line

    Google’s own guidance says no special markup is required for AI features; the usual SEO fundamentals still carry you, from crawlable text to internal links. The real change is scope. One page should clearly answer several related sub-questions, not chase a single keyword. Teams that map sub-intents and track citation share, rather than just rank, will spot the new opportunities first.

    Also Read: 7 Keyword Tools Combining Search Intent and GEO Data

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    Ready to find these long-tail wins yourself? SnowSEO surfaces them fast. Start your free keyword research today.

    Frequently Asked Questions

    Q1: What recent Google AI search developments reveal about future keyword opportunities?

    Google’s AI Mode uses query fan-out, breaking one search into many long-tail variations. That means specific, question-shaped and conversational phrases now drive visibility more than broad head terms.

    Q2: How do I find long-tail keywords AI search will surface?

    Mine “People Also Ask” data, support tickets, and sales calls for real phrasing. Then group questions by intent. Tools like SnowSEO can automate this clustering and track AI visibility.

    Q3: Should I still optimize for traditional Google rankings?

    Yes. Classic rankings still feed AI answers, so they matter. But add conversational phrases and clear, direct answers so AI Overviews and chatbots can quote your content accurately.

    Q4: How do I track whether AI platforms cite my content?

    Test prompts in ChatGPT and Google AI Mode, or use rank-tracking tools with AI visibility reports. Log which pages get cited, then update weak pages with sharper answers and structure.

    Conclusion

    Google’s query fan-out guide confirms what Digiday reported: one AI query now triggers many hidden sub-queries. Map those sub-queries, cover them deeply, and the long tail becomes your citation engine.

  • 7 Keyword Tools Combining Search Intent and GEO Data

    7 Keyword Tools Combining Search Intent and GEO Data

    Quick Summary: Keyword tools now track both classic search metrics and AI prompt demand from assistants like ChatGPT, since the two signals differ. SnowSEO leads the ranking of seven tools because it combines intent-driven keyword research with GEO tracking and content workflows in one platform. Semrush, Search Atlas, and Profound follow, each offering different strengths in prompt data, automation, or enterprise-scale AI conversation intelligence. Pick a tool that shows both keyword and AI signals together, matches your team’s workflow, and offers the right geographic and competitor depth.

    A keyword like best CRM for startups might show 5,000 Google searches a month. The same buyer might ask ChatGPT a longer, more specific question instead. Most keyword tools with AI search intent tracking now reveal both signals: classic volume and CPC, plus AI prompts, citations, and share of voice. We ranked seven options on intent data, GEO coverage, competitor context, and workflow fit. SnowSEO leads the list as the featured pick.

    Keyword and GEO Tool Comparison

    Tool Best for Intent data GEO or AI data Workflow strength
    SnowSEO Small teams, agencies, and businesses seeking one SEO and GEO workspace AI intent classification, semantic clusters, opportunity scoring, and content-gap context AI prompt tracking, mentions, citations, competitor visibility, and content gaps Keyword research to topic clusters, article generation, CMS publishing, and monitoring
    Semrush Enterprise SEO teams and agencies already using Semrush Keyword intent plus AI prompt intent across informational, commercial, transactional, navigational, and task queries AI topic demand, prompts, brand mentions, source domains, sentiment, and market-level visibility Strong suite integration for SEO research, content, monitoring, and reporting
    Search Atlas Agencies, multi-site brands, and teams seeking research-to-execution automation Keyword intent, funnel-aligned prompt families, buyer needs, and answer patterns Prompt volumes, LLM visibility, citation research, competitor-owned prompts, and source maps Keyword research to topical maps, content planning, optimization, and OTTO automation
    Profound Enterprise brands and agencies needing specialist AI conversation intelligence Automated intent, sub-intent, buyer-journey alignment, and intent-volume prioritization Large-scale prompt volumes, citations, co-citations, visibility, sentiment, and share of voice Prompt tracking, content briefs, page updates, agents, and reporting workflows

    What to know about keyword tools with AI search intent

    Keyword tools with AI search intent do two jobs at once. They show classic metrics like volume, difficulty, and CPC, plus how often AI assistants like ChatGPT cite brands for conversational prompts.

    Why now? Traffic is shifting from typed queries to AI answers, and prompt demand is modeled differently from Google search volume. So compare signals instead of treating them as one number.

    The seven tools below cover that range, from full platforms to GEO-focused trackers.

    1. SnowSEO

    SnowSEO puts keyword research and GEO visibility in one workspace. You research keywords, build clusters, generate content, publish it, and track both Google rankings and AI mentions without switching tools.

    SnowSEO
    SnowSEO

    Highlights

    • AI keyword suggestions with volume, CPC, difficulty, and trends
    • Intent classification and semantic clustering
    • AI visibility tracking across ChatGPT, Perplexity, and Claude
    • City-level keyword and prompt tracking
    • Topic clusters, content generation, and CMS publishing

    Pros

    • Combines traditional SEO and GEO workflows in one place
    • Links keyword research to content production and publishing

    Cons

    • Less granular AI conversation research than specialist tools
    • Verify plan and API limits for large data exports

    SnowSEO ranks first because it directly matches the combined brief: intent-driven keyword research plus GEO tracking, with the workflows to act on both.

    Last updated: September 17, 2026

    Also Read: 10 Best Tools for Competitor SEO Analysis in 2024

    2. Semrush

    Semrush pairs its mature keyword research suite with Prompt Research, a large AI prompt database that treats prompts as a second research layer. You get AI topic demand, intent breakdowns, brand mentions, and cited domains across major AI platforms and 40+ markets, per Semrush’s documentation.

    Semrush
    Semrush

    Highlights

    • 317M+ prompts with topic-level AI volume and intent analysis
    • Brand mentions, sentiment, and source-domain tracking
    • Prompt data flows into the same workflows as keyword and content research

    Specs

    • Best for: Enterprise SEO teams and agencies already using Semrush
    • Note: Full GEO features require the AI Visibility Toolkit ($99/mo per domain)

    Pros

    • Deep keyword and competitor research in one login
    • Broad prompt coverage and strong reporting

    Cons

    • Complex for small teams; AI data costs extra

    It ranks second because it offers the clearest bridge between keyword research and AI prompt research, though the combined setup is heavier than tools like SnowSEO, which bundle SEO and GEO in one platform.

    Last updated: September 17, 2026

    Also Read: SnowSEO vs Ahrefs: Which SEO Tool Covers More Workflows?

    3. Search Atlas

    Search Atlas pairs keyword intelligence with prompt-volume research, then links both to topical maps and automated SEO work. Agencies and multi-site brands use it to move from discovery straight to execution.

    Search Atlas
    Search Atlas

    Highlights

    • Keyword research with volume, difficulty, intent, CPC, and traffic opportunity
    • Prompt families organized by buyer need, funnel stage, and answer pattern
    • Mapping between AI prompts and traditional keywords
    • Topical maps, content briefs, and OTTO SEO automation

    Pros

    • Strong connection between keywords, prompts, topics, and content execution
    • Supports LLM visibility, citation research, and competitor-owned prompts

    Cons

    • Broad platform brings a steeper learning curve
    • Some AI visibility and automation features depend on plan level

    It ranks third because few tools cover research-to-implementation this completely, though lighter users may find it more platform than they need.

    Last updated: September 17, 2026

    Also Read: SnowSEO – Your Last SEO Platform

    4. Profound

    Profound is the specialist pick for measuring real AI conversation demand and intent at enterprise scale. It maps prompt volumes from large AI conversation datasets, not just classic keyword counts.

    Profound
    Profound

    Highlights

    • Prompt-volume research built on large AI conversation datasets
    • Automated intent and sub-intent classification
    • Segmentation by audience, demographic, platform, and region
    • Competitor citation and co-citation analysis
    • Prompt tracking with visibility, rank, share of voice, citations, and sentiment

    Pros

    • Unusually deep prompt-volume and intent analysis
    • Strong citation and competitor intelligence
    • Built for repeatable enterprise monitoring and action

    Cons

    • Not a full replacement for a conventional keyword database
    • Often overkill for small businesses covering a narrow topic set

    Profound ranks fourth because its AI prompt and citation layer goes deeper than all-in-one platforms like SnowSEO, but it works best alongside an existing SEO stack rather than as a single replacement.

    Last updated: September 17, 2026

    Honourable Mentions

    These tools each cover a useful slice of the keyword-to-GEO workflow, even if they’re less complete as combined research platforms.

    1. OtterlyAI – Combines AI prompt discovery with intent and funnel tagging, query fan-out, geographic coverage, and visibility tracking.

    2. Peec AI – Adds AI-suggested prompts, demand signals, competitor comparisons, source analysis, tags, exports, API, and Looker Studio connectivity.

    3. SE Ranking – Provides keyword volume, difficulty, CPC, intent, trends, competitor gaps, and local data across 188 geographic databases, but has a less central GEO research layer.

    How to choose the right keyword and GEO tool

    Match the tool to your data need first. Keyword volume and AI prompt demand are different numbers, so pick a platform that shows both together.

    • Intent granularity: it should label informational, commercial, transactional, navigational, and task-based queries
    • Geo filters: country, city, language, device, and AI platform coverage
    • Competitor depth: keyword gaps plus citation and co-citation data
    • Workflow fit: agencies need APIs and exports; small teams want briefs and publishing help
    • Closed loop: research, briefs, tracking, and reporting in one place

    That last point is where SnowSEO stands out. Most tools stop at research; SnowSEO carries the work through audits, content briefs, and prompt tracking, while GEO-only tools like Otterly.ai or Peec AI leave the keyword side thin.

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    Stop juggling separate tools for search intent and AI prompt data. SnowSEO combines both, plus tracking and content, in one dashboard. Start your free trial today.

    Frequently Asked Questions

    Q1: Best keyword research tools that integrate AI search intent and generative engine optimization?

    SnowSEO combines keyword data with AI prompt tracking. AthenaHQ and Otterly.ai focus mostly on GEO monitoring.

    Q2: Do I need a separate GEO tool?

    No. Platforms like SnowSEO handle keywords and AI visibility together, saving you juggling multiple subscriptions.

    Q3: How often should I track AI prompt rankings?

    Weekly works for most teams. Daily only matters during launches or major algorithm shifts.

  • How to Build Citation-Worthy Content for AI Search

    How to Build Citation-Worthy Content for AI Search

    Quick Summary: To get cited by AI search, create content with original proof, not just polished summaries. Lead with a clear answer, back it with verifiable data or firsthand tests, and keep the page technically clean so crawlers can index it. Measure visibility over time, but never promise citations, since Google offers no guaranteed formula.

    A page can rank well, then vanish when a buyer asks an AI tool for a recommendation. Often, the gap is not another keyword. It is a clear, checkable passage the system can retrieve and attribute.

    This guide shows how to create citation-worthy content for AI search with useful claims, original proof, clean structure, and sound technical setup. You will also learn how to measure citation-worthy content for AI search without making promises no one can prove.

    Step 1: Choose a Question You Can Answer Better Than a Summary

    Build a Question Map

    Pick questions where your team has proof, not just a polished recap. Google asks creators to add original information, analysis, or first-hand expertise in its people-first content guidance.

    • List buyer questions from sales calls and support tickets.
    • Group them by task, risk, and intent.
    • Flag questions competitors answer with vague advice.
    • Choose one your team can show, test, or explain clearly.

    Add a Distinctive Evidence Angle

    Give the answer something an AI summary cannot create alone.

    Evidence type Example
    First-hand test Results from a real content audit
    Original data Patterns from your own site data
    Expert judgment A clear decision rule and its limits

    Tip: Do not promise AI citations. Build useful proof, then measure visibility over time.

    Also Read: 10 Best Tools for Competitor SEO Analysis in 2024

    Step 2: Write Extractable Answers, Then Earn Trust With Depth

    Use a Repeatable Section Pattern

    Lead with a plain answer in the first two sentences. Then add the context, steps, and proof a reader needs to act.

    1. State the answer clearly.
    2. Explain when it applies.
    3. Add an example, source, or firsthand result.
    4. Link to deeper guidance where needed.

    Google advises clear structure and unique, useful content for generative search features in its AI search guidance.

    Four-step flowchart with pencil sketch style
    Four-step flowchart with pencil sketch style

    Write for quick extraction, not shallow reading. The short answer earns attention. The evidence earns trust.

    Make Claims Precise and Verifiable

    Replace broad promises with facts a reader can check. Name the source, date, sample, method, or limit.

    Weak claim Verifiable rewrite
    Our process improves visibility Our audit found 12 pages missing source links
    AI search cites experts AI systems may surface relevant indexed sources

    Google asks publishers to show original insight, clear sourcing, and demonstrable expertise in its people-first content guidance.

    Also Read: Google AI Updates Change How Marketers Measure Search Visibility

    Step 3: Make the Source Easy to Find, Understand, and Attribute

    Check Eligibility and Discoverability

    Your best evidence cannot help if crawlers cannot reach the page. Confirm it returns a 200 status, is not blocked by noindex or robots.txt, has a clear canonical URL, and appears in your sitemap.

    Google says key pages need links or sitemap entries to be found and crawled. Check the Page Indexing report after publishing.

    SEO manager checking indexing status on dual monitors
    SEO manager checking indexing status on dual monitors

    Indexing makes a page eligible for search. It does not promise an AI citation.

    Clarify the Page and Its Author

    State the answer near the top, then support it with clear headings, source links, and dated proof. Add a byline, author bio, reviewer details, and an About page.

    Google recommends clear sourcing and background about authors or publishers as trust signals in people-first content guidance.

    • Name who did the work.
    • Explain how you gathered the evidence.
    • Update dates when facts change.

    Also Read: SnowSEO – Your Last SEO Platform

    Step 4: Publish, Promote, and Measure Citation Potential

    Publish a clean, crawlable page with a clear author, date, sources, and internal links. Google advises content teams to show original research and first-hand expertise in its people-first content guidance.

    1. Share the page with relevant partners, customers, and industry communities.
    2. Track branded mentions, referring pages, AI answer appearances, clicks, and assisted leads.
    3. Review the page after new data or product changes.
    Signal What it suggests
    Earned mentions Others find the proof useful
    AI answer presence The page may be extractable
    Conversions Visibility supports business value

    Do not promise citations. Treat them as an outcome to test, not a ranking metric you control.

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    Turn stronger evidence into measurable AI search visibility. Use SnowSEO to audit content, find proof gaps, track rankings, and improve citation-ready pages.

    Frequently Asked Questions

    Q1: How can marketers create content that AI search engines are more likely to cite?

    Use clear answers, named sources, original data, and precise headings. Make key facts easy to extract.

    Q2: Does structured data guarantee AI citations?

    No. It can help systems understand a page, but proof and relevance matter more.

    Q3: What proof makes content more citation-worthy?

    First-party research, expert quotes, tested methods, and dated examples build trust.

    Conclusion

    Build content people can verify, use, and trust. Pair original proof with clear structure, sound technical SEO, and measured results. Google’s guidance confirms there is no guaranteed citation formula.