The Research-Backed GEO Checklist: 30 Actions That Actually Improve AI Search Visibility

Introduction: A Checklist Built on Evidence, Not Speculation

There is no shortage of GEO checklists. Type “how to optimise for AI search” into any search engine and you will find dozens of posts listing tactics — some well-reasoned, some copied from each other, most citing no evidence for why any specific action would improve AI search visibility.

This checklist is different. Every item on it maps to a specific research finding, with a study source and, where available, a Normalised Importance Score (NIS) that indicates how strongly that factor predicts AI search visibility relative to others.

The research backbone is Kargaev’s (2026) cross-paradigm synthesis, which draws on the Aggarwal et al. (2024) GEO benchmark (10,000 queries, nine AI systems), the Ahrefs (2025) AI brand visibility study (75,000 brands), the Backlinko (2020) ranking factors corpus (11.8 million search results), the Semrush (2024) ranking factors study, and contextual evidence from seoClarity (2025), BrightEdge (2025a; 2025b), SparkToro (2026), Authoritas (2025), and academic work by Gao et al. (2023) and Wallat et al. (2025).

The checklist is organised into three tiers, each reflecting a different phase of GEO implementation:

• Foundation Tier — ranking eligibility: the organic infrastructure prerequisites
• Amplification Tier — citation eligibility: brand entity and content signals
• Optimisation Tier — measurement and iteration

Within each tier, items are prioritised by research evidence strength. Start from the top of each tier. The research tells you what matters most.

Quick Answer A research-backed GEO checklist has three tiers: Foundation (ranking eligibility — indexation, Core Web Vitals, authority), Amplification (brand entity at NIS 0.918, evidence-rich content at NIS 0.747), and Optimisation (monthly AI citation tracking). Start with any Foundation gaps before touching Amplification signals.

How Do You Use This GEO Checklist?

Before working through the items, run a baseline assessment. The free AIO Clicks scan at aioclicks.com/free-analysis covers both traditional SEO health and AI search visibility simultaneously — giving you a starting position map that makes the checklist more targeted.

For each checklist item, rate your current status: ✅ Done and verified | ⚠️ Partially done or needs review | ❌ Not started.

After completing the audit, prioritise any Foundation Tier ❌ items before touching Amplification Tier items. The organic foundation effect documented by seoClarity (2025) and synthesised by Kargaev (2026) makes Foundation Tier items the prerequisite for everything that follows. Building Amplification Tier signals on a weak Foundation Tier produces SparkToro’s (2026) volatility outcome — occasional AI citations that are unstable and non-compounding.

What Is in the Foundation Tier of the GEO Checklist?

These ten items establish the organic infrastructure that the research shows is the prerequisite for AI search visibility. AI systems draw from the indexed, organically-visible web. Pages that fail these criteria are not in the candidate pool from which GEO actions would otherwise benefit.

Research basis: seoClarity (2025) — AI Overviews overwhelmingly include URLs already performing well in organic search. SparkToro (2026) — lower-authority domains show substantially higher AI citation volatility.

F1. All important pages are indexed and appearing in Google Search Console

• Why it matters: Non-indexed pages are not in the candidate pool for AI retrieval. The organic foundation effect makes indexation the prerequisite for all GEO signals.
• How to check: Search Console → Coverage → verify no important pages in Excluded or Error status.
• How to fix: Correct robots.txt blocks, noindex tags, crawl errors, and canonical misconfigurations.

F2. No crawl blocks on important pages for Googlebot or AI crawlers

• Why it matters: Pages blocked from crawling cannot be indexed, which means they cannot contribute to the organic foundation that GEO builds on.
• How to check: robots.txt validator in Search Console; test individual pages with URL Inspection tool.
• How to fix: Review and update robots.txt; ensure Googlebot is not blocked from CSS, JavaScript, or content-critical resources.

F3. Core Web Vitals passing on all key pages

• Why it matters: Google uses Core Web Vitals as a ranking signal; pages that fail are suppressed in organic rankings, which reduces their organic prominence and thus their GEO candidate pool position.
• How to check: Google Search Console Core Web Vitals report; PageSpeed Insights for individual pages.
• How to fix: Address LCP (loading), CLS (visual stability), and INP (interactivity) issues identified in the diagnostic tools.

F4. Mobile-friendly rendering confirmed

• Why it matters: Google’s mobile-first indexing uses the mobile version of your page for ranking signals. A desktop-only optimised page underperforms in organic rankings, reducing candidate pool position.
• How to check: Google’s Mobile-Friendly Test; Search Console Mobile Usability report.
• How to fix: Ensure responsive design, adequate tap target sizes, and no mobile-specific content blocks.

F5. HTTPS active across all pages

• Why it matters: HTTPS is a confirmed ranking signal (NIS 0.015 in the Semrush corpus — a baseline requirement, not a differentiator). Its absence is a clear organic foundation failure.
• How to check: Browser address bar; SSL certificate validity; HTTPS redirect configuration.
• How to fix: Install valid SSL certificate; configure 301 redirects from HTTP to HTTPS across all pages.

F6. XML sitemap complete and submitted to Google Search Console

• Why it matters: A complete, accurate sitemap ensures all important pages are in Google’s crawl queue and reduces the risk of indexation gaps in the organic foundation.
• How to check: Search Console → Sitemaps → verify submission and no errors.
• How to fix: Generate a complete sitemap using your CMS or a sitemap tool; submit and monitor for errors.

F7. Internal linking structure connects all important pages

• Why it matters: Internal links distribute authority across the domain and help Google’s crawlers discover and understand the relationship between pages. Orphaned pages with no internal links are at risk of poor indexation.
• How to check: Screaming Frog or Ahrefs site audit for orphaned pages and shallow link depth.
• How to fix: Add contextually relevant internal links from high-authority pages to important lower-linked pages.

F8. Domain authority baseline established and being actively built

• Why it matters: Backlinks score NIS 1.000 in the Backlinko corpus (Backlinko, 2020) as the strongest SEO-side authority signal. Domain authority shapes the organic prominence that determines candidate pool position. SparkToro (2026) found that lower-authority domains show higher AI citation volatility.
• How to check: Ahrefs DR, Semrush Authority Score, or Moz DA as composite proxies.
• How to fix: Active link acquisition through digital PR, editorial outreach, and legitimate directory listings.

F9. No significant duplicate content across the domain

• Why it matters: Duplicate content dilutes the domain’s overall quality signal in Google’s quality assessment, suppressing the organic rankings that feed the GEO candidate pool.
• How to check: Screaming Frog for near-duplicate pages; canonical tag audit.
• How to fix: Implement canonical tags on near-duplicate pages; consolidate or remove thin or duplicated content.

F10. E-E-A-T signals present across the domain

• Why it matters: Kargaev (2026) identifies content quality and E-E-A-T as persistent across both SEO and GEO paradigms. Domains with weak E-E-A-T signals face suppression in both traditional rankings and AI retrieval.
• How to check: Author attribution on content pages; About page with team credentials; transparent business information; no thin content patterns.
• How to fix: Add named author attribution to all content; create or improve About and Team pages; remove or substantially improve thin content.

What Is in the Brand Entity Amplification Tier?

These eight items build the brand entity signals that Ahrefs (2025) identified as the strongest measured GEO factor — Brand Entity Mentions at NIS 0.918. They convert organic foundation presence into named recommendation capability.

Research basis: Ahrefs (2025) — brand entity mentions NIS 0.918, brand search volume NIS 0.547, domain rating NIS 0.397. Kargaev (2026) — authority broadens from link-graph toward entity salience. Authoritas (2025) — entity-linked signals among the most influential for AI inclusion.

E1. Organisation schema implemented and validated on homepage

• Why it matters: Organisation schema is the foundational technical declaration of brand entity — it tells AI systems your business name, type, location, services, social profiles, and contact details in machine-readable format, reducing the inference burden on AI attribution.
• How to check: Google’s Rich Results Test on your homepage URL; Search Console’s Schema Markup report.
• How to fix: Implement schema.org/Organization markup with all available fields populated; validate and monitor for errors.

E2. Google Business Profile fully completed, verified, and regularly updated

• Why it matters: Google Business Profile is the primary knowledge graph signal for Google-based AI systems including Gemini and AI Overviews. Complete profiles with photos, services, posts, and review responses signal active, credible business presence.
• How to check: Search your business name in Google and inspect the Knowledge Panel; Google Business Profile dashboard for completeness score.
• How to fix: Fill all available fields; add photos; list all services; set accurate opening hours; respond to all reviews; post at least monthly.

E3. NAP consistency audited and corrected across all directories

• Why it matters: Inconsistent Name, Address, Phone data across directories introduces entity ambiguity — AI systems encounter conflicting identity signals that reduce citation confidence.
• How to check: Manual audit of top ten directories (Google, Yelp, LinkedIn, industry-specific); Brand24 or Mention for broader mention monitoring.
• How to fix: Standardise exact business name, address format, and phone number across all listings; update stale listings.

E4. Wikidata entry established (where notability criteria are met)

• Why it matters: Wikidata is the primary openly-editable knowledge graph and a key cross-reference source for AI systems verifying entity identity from sources independent of the business’s own website.
• How to check: Search your business name on wikidata.org.
• How to fix: Create a Wikidata entry with accurate, linked data; include official website, social profiles, location, industry category, and founding date.

E5. LinkedIn company page complete and actively maintained

• Why it matters: LinkedIn is among the sources AI systems treat as authoritative for professional entity verification. A complete, accurate LinkedIn profile is a cross-reference confirmation of business identity.
• How to check: Visit your LinkedIn company page; assess completeness of all sections.
• How to fix: Fill all profile sections; add product/service descriptions; post regularly; ensure employee connections to company page are accurate.

E6. Consistent social profile presence across relevant platforms

• Why it matters: Each social profile with accurate, consistent business information adds a verification data point to the cross-referenced entity profile AI systems build. Inconsistent or absent profiles create verification gaps.
• How to check: Review all active social profiles for name consistency, bio accuracy, and profile completeness.
• How to fix: Audit all social presences; correct any identity inconsistencies; complete any partially filled profiles.

E7. Editorial mentions in at least five authoritative external sources

• Why it matters: Brand Entity Mentions is the NIS 0.918 signal — the dominant GEO factor. Editorial mentions in publications that AI systems already treat as authoritative provide the cross-web validation that enables named AI recommendations.
• How to check: Search for your business name in Google News and Google Web Search; check Ahrefs for referring domains and editorial contexts; run a ChatGPT citation test.
• How to fix: Targeted digital PR campaign focused on publications that appear in AI-generated responses for your category; guest content; expert commentary placements.

E8. Branded search volume growing month over month

• Why it matters: Brand Search Volume scores NIS 0.547 in the Ahrefs (2025) study — the second-strongest GEO authority signal. Growing branded search volume reflects the real-world brand recognition that AI systems interpret as entity salience.
• How to check: Google Search Console — filter queries containing your brand name; track monthly trend.
• How to fix: Brand-building activity across channels — editorial coverage, social presence, product quality, word of mouth. Branded search volume is a lagging indicator of overall brand entity strength.

What Is in the Content Citation Amplification Tier?

These eight items build the content-level citation eligibility signals identified in the Aggarwal et al. (2024) GEO benchmark and the Kargaev (2026) synthesis.

Research basis: Aggarwal et al. (2024) — Statistics Addition NIS 0.747, Fluency Optimization NIS 0.684, Cite Sources NIS 0.671. Gao et al. (2023) — citation-capable generation requires attributable sources. Wallat et al. (2025) — faithfulness requires clear attribution chains.

C1. Key pages contain at least eight attributed statistics with specific numbers and sources

• Why it matters: Statistics Addition scores NIS 0.747 — the strongest single content intervention in the GEO benchmark. Specific, attributed quantitative claims are the most directly citable content for AI synthesis.
• How to check: Count specific cited statistics on each important page; note pages with fewer than five.
• How to fix: Add quantitative data — percentages, absolute numbers, comparative measurements — with explicit attribution to studies, reports, or original research.

C2. Each section opens with a direct, complete answer (inverted pyramid structure)

• Why it matters: Fluency Optimization scores NIS 0.684 — AI systems extract content most reliably from sections that lead with direct answers. Buried conclusions reduce citation eligibility.
• How to check: Read the opening sentence of each H2 section — does it directly answer the implied question? Or does it ease in with context and background?
• How to fix: Restructure section openings to lead with the answer; move context and evidence to supporting sentences.

C3. Formal citations to authoritative external sources present throughout

• Why it matters: Cite Sources scores NIS 0.671. Wallat et al. (2025) found AI systems distinguish faithful (properly attributed) from merely plausible answers — content with clear citations is more synthesis-compatible.
• How to check: Count formal citations per page; note whether they reference studies with dates, authors, and institutions.
• How to fix: Add citations to all statistical claims; include author, year, and publication for each; use academic, large-scale industry, or institutional sources.

C4. FAQ section present on all important pages with real buyer questions

• Why it matters: FAQ structure maps directly onto conversational AI query patterns, making FAQ content the most naturally extractable format for AI responses. FAQPage schema extends this advantage to structured data systems.
• How to check: Audit key pages for FAQ sections; verify questions reflect real buyer language rather than invented queries.
• How to fix: Add FAQ sections; source questions from Search Console queries, customer service logs, and People Also Ask data; answer directly and concisely.

C5. FAQPage schema implemented on all FAQ-containing pages

• Why it matters: Schema markup is the bridge between content and AI readability. FAQPage schema makes question-answer pairs explicitly machine-readable, improving extraction reliability for AI responses and eligibility for Google rich results.
• How to check: Schema Markup Validator on FAQ pages; Google Search Console Rich Results report.
• How to fix: Implement FAQPage schema; validate implementation; monitor for errors in Search Console.

C6. Article schema on blog posts and guides (with author, date, publisher)

• Why it matters: Article schema establishes the authorship and publication attribution that E-E-A-T requires and that AI systems use when attributing content to named sources.
• How to check: Schema Markup Validator on content pages; inspect for author, datePublished, and publisher fields.
• How to fix: Add Article or BlogPosting schema to all long-form content; include named author with Person schema; include datePublished and dateModified.

C7. Named author attribution with verifiable credentials on all content

• Why it matters: Authoritas (2025) found that entity-linked signals and explicit expertise markers are among the most influential factors for AI inclusion. Anonymous content lacks the attribution chain AI systems prefer for named sourcing.
• How to check: Check whether author names, professional roles, and credentials are visible on all published content.
• How to fix: Add author bylines with brief credentials; link to author profile pages; ensure author pages include professional background and verifiable expertise signals.

C8. Headings structured as questions or direct descriptive statements

• Why it matters: Question-mirroring headings improve the semantic alignment between content and AI query patterns, making it easier for retrieval systems to match content to relevant queries and extract it accurately.
• How to check: Review H2 and H3 headings on key pages — are they descriptive and specific, or vague and stylistic?
• How to fix: Rewrite vague headings (“Our Approach”, “What We Offer”) to descriptive or question-format headings that directly address buyer concerns.

How Do You Measure and Iterate on GEO Performance?

These four items establish the measurement infrastructure that turns the GEO checklist from a one-time project into an ongoing programme.

Research basis: SparkToro (2026) — citation volatility is high for lower-authority domains; consistent measurement is essential for understanding whether GEO signals are producing stable outcomes. Kargaev (2026) — GEO is still a young field; iterative evidence-based adjustment is more reliable than set-and-forget implementation.

M1. Monthly manual AI citation testing across key query sets

• Why it matters: Manual testing is the most direct measurement of GEO outcomes. It reveals whether specific pages are being cited, how your brand is being described, and which competitors are appearing in the same responses.
• How to do it: Define a set of fifteen to twenty queries your target buyers would use in ChatGPT, Perplexity, and Gemini. Test monthly. Document appearances, descriptions, and competitor mentions.
• What to track: Presence/absence by query, description accuracy, competitor share of voice, trend month over month.

M2. AI visibility tracking tool implemented for systematic measurement

• Why it matters: Manual testing cannot scale to the full range of queries relevant to a business. Systematic tracking provides share-of-voice data, sentiment analysis, and competitor benchmarking at scale.
• Tools: Otterly.ai and Peec AI for dedicated AI visibility monitoring; Semrush AI Visibility Toolkit for integration with traditional SEO metrics. For businesses wanting specialist AI visibility monitoring combined with active GEO strategy, AIO Clicks provides both through its AI Search & GEO service.
• What to track: Citation frequency, share of voice, sentiment, trending query topics where you are gaining or losing AI visibility.

M3. AI-referred traffic segment set up in Google Analytics

• Why it matters: AI-referred traffic is the commercial signal that links GEO actions to business outcomes. Tracking traffic from ChatGPT, Perplexity, and Gemini referral sources shows the revenue impact of improving GEO signals.
• How to set up: In GA4, create a segment filtering sessions by source containing “chat.openai.com”, “perplexity.ai”, “gemini.google.com”, and equivalent AI platform domains.
• What to track: Sessions, conversion rate, revenue (where applicable), month-over-month trend.

M4. Quarterly GEO checklist review and gap re-audit

• Why it matters: GEO is an evolving field. AI platform behaviour changes, competitor GEO investment grows, and new signals emerge. A quarterly re-audit of the full GEO checklist maintains alignment between implementation and current evidence.
• How to do it: Re-score all checklist items quarterly; prioritise any new ❌ or ⚠️ items; update content with fresh statistics and citations; assess whether brand entity signals have improved.
• What to look for: Emergence of new ❌ items from previously passing categories; shifts in AI citation consistency (SparkToro’s volatility signal); gaps revealed by manual prompt testing that were not visible three months earlier.

What Is Not on the GEO Checklist — and Why?

A GEO checklist built on evidence should be explicit about what it excludes. Several tactics frequently appear in practitioner GEO guidance that do not have meaningful support in the retained evidence base.

Content length targets above the evidential baseline. Content length scores NIS 0.043 in the Semrush corpus — near-null. There is no evidence in the retained GEO corpus that content above a quality threshold produces additional AI citation benefit from length alone. Word count targets that are not tied to evidential density are not evidence-based GEO strategy.

Social media engagement metrics as GEO signals. No retained quantitative study directly links social engagement to AI citation frequency. Social presence contributes indirectly to brand entity signals, but social media follower counts, share rates, or engagement metrics are not GEO checklist items.

HTTPS optimisation beyond compliance. NIS 0.015 in the Semrush corpus — a baseline requirement with no competitive differentiation value above that baseline.

Page speed optimisation beyond Core Web Vitals thresholds. Page speed scores NIS 0.000 in the within-first-page Backlinko distribution. Core Web Vitals compliance (F3 in Foundation Tier) is required; optimisation beyond those thresholds produces no evidence-based GEO benefit.

AI-specific keyword strategies. There is no evidence in the retained corpus that optimising for AI search requires different keyword targeting from traditional SEO. The organic foundation effect means that traditional keyword-aligned content is the appropriate starting point; GEO adds evidential and entity signals on top, not alternative keyword strategies.

How Do You Prioritise the GEO Checklist When Resources Are Limited?

The GEO checklist contains thirty items. Most businesses cannot address all thirty simultaneously. The research-based prioritisation logic is straightforward.

Days one to thirty — Foundation Tier triage: Address any ❌ Foundation Tier items before anything else. A domain with indexation errors, blocked crawls, or failing Core Web Vitals cannot benefit from Amplification Tier investment. The organic foundation must be functional.

Days thirty to ninety — Brand Entity foundation: Complete E1 (Organisation schema) and E2 (Google Business Profile) first — these are the fastest and highest-impact brand entity improvements. Then address E3 (NAP consistency audit) and E5 (LinkedIn). These four items collectively establish the technical and knowledge graph brand entity foundation that the research shows matters most.

Days ninety to one hundred eighty — Content citation eligibility: Work through C1 to C4 on your ten highest-traffic pages. Adding statistics, restructuring sections with inverted pyramid, adding citations, and installing FAQ sections on existing high-performing pages converts ranking eligibility into citation eligibility without requiring new content production.

Ongoing — Amplification and optimisation: Brand entity items E7 (editorial mentions) and C items requiring sustained effort (new content to the AI-ready standard, schema implementation across the full domain) are ongoing programme items. Optimisation Tier items M1 to M4 should be running from the start, but they become most valuable after Foundation and core Amplification work is complete.

How Does AIO Clicks Execute the GEO Checklist?

Who Is AIO Clicks?

AIO Clicks is a premium digital visibility agency headquartered in Haaksbergen, Netherlands, serving businesses across the EU. Founded by entrepreneurs who have operated real businesses, the team evaluates GEO implementation the way a business owner does: which actions produce the most commercial return, in what order, measured in leads and revenue rather than abstract visibility scores.

The GEO checklist above is the operational expression of the research that AIO Clicks has built its AI Search & GEO methodology around. Every item maps to a study. Every priority is research-justified. The goal is not to implement a checklist for its own sake — it is to systematically build the ranking eligibility and citation eligibility that produce compounding AI search visibility and the commercial outcomes that follow.

Services Mapped to GEO Checklist Tiers

Foundation Tier — Google Rankings & SEO: Technical SEO audit and implementation addressing F1 through F10. This is the infrastructure layer — crawlability, indexation, Core Web Vitals, authority building, E-E-A-T foundations.

Brand Entity Amplification — Brand Entity Optimization: Structured data implementation (E1), knowledge graph establishment (E2, E4), NAP consistency audit (E3), and digital PR for editorial mentions (E7). Directly maps to the Ahrefs (2025) Brand Entity Mentions finding at NIS 0.918.

Content Amplification — AEO and GEO Content Strategy: Content restructuring and production to the AI-ready standard — statistics, citations, FAQ architecture, schema markup, author attribution. Directly maps to the Aggarwal et al. (2024) GEO benchmark findings.

Optimisation Tier — AI Search & GEO Monitoring: Ongoing AI citation tracking, Share of Voice measurement, prompt testing cadence, and quarterly GEO re-audit.

Start by finding out which tier needs the most immediate attention. Run the free scan at aioclicks.com/free-analysis — Foundation, Brand Entity, and Content layers assessed simultaneously in 60 seconds.

Frequently Asked Questions About the GEO Checklist

What Is the Key Takeaway From the GEO Checklist?

The thirty items in this GEO checklist are derived from the best available evidence on what improves AI search visibility. They are not static. The GEO research field is still developing — Kargaev (2026) explicitly notes the need for longitudinal measurement, stronger causal evidence, and engine-specific analysis that the current evidence base cannot yet support.

What the checklist provides is a research-grounded starting point — one that prioritises actions by evidence strength rather than practitioner intuition. Brand Entity Mentions at NIS 0.918 earns a prominent position. Statistics Addition at NIS 0.747 earns a high priority in the content tier. HTTPS beyond compliance at NIS 0.015 does not make the list as a competitive investment. This is what evidence-based GEO looks like.

The businesses that approach GEO systematically — Foundation Tier first, Brand Entity Amplification second, Content Amplification third, ongoing measurement throughout — are building the compounding AI search visibility that SparkToro (2026) found only at the top of the authority and entity distribution. The gap between first movers and later entrants widens with each passing month.

Use the checklist. Start with the free scan at aioclicks.com/free-analysis to identify where your Foundation, Brand Entity, and Content tiers currently stand — then work through the highest-priority ❌ items systematically.

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References

Aggarwal, P., Maatouk, A., Maillard, Q., Gagnon, L., Pal, C., & Boussioux, L. (2024). GEO: Generative engine optimization. Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’24). https://doi.org/10.1145/3637528.3671900

Ahrefs. (2025). Top brand visibility factors in ChatGPT, AI Mode, and AI Overviews. https://ahrefs.com/blog/ai-brand-visibility-correlations/

Authoritas. (2025). Can you fake expertise in AI search? We tested 9 models to find out. https://www.authoritas.com/blog/can-you-fake-it-til-you-make-it-in-the-age-of-ai-search

Backlinko. (2020). We analyzed 11.8 million Google search results. https://backlinko.com/search-engine-ranking

BrightEdge. (2025a). One year into Google AI Overviews, BrightEdge data reveals Google search usage increases by 49%. https://www.brightedge.com/news/press-releases/one-year-google-ai-overviews-brightedge-data-reveals-google-search

BrightEdge. (2025b). AI search visits surging in 2025, but organic search remains the cornerstone of digital growth. https://www.brightedge.com/resources/research-reports/ai-search-visits-in-surging-2025

Gao, T., Yen, H. W., Yu, J., & Chen, D. (2023). Enabling large language models to generate text with citations. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023). https://doi.org/10.18653/v1/2023.emnlp-main.398

Kargaev, D. (2026). The SEO-to-GEO gap: Quantifying ranking factor divergence between traditional and generative search. SSRN. https://doi.org/10.2139/ssrn.6476021

Semrush. (2024). Ranking factors study 2024. https://seventy2digital.com/wp-content/uploads/2024/01/2024-Google-Ranking-Factors-Study-By-Semrush-English.pdf

seoClarity. (2025). Impact of Google’s AI Overviews: SEO research study. https://www.seoclarity.net/research/ai-overviews-impact

SparkToro. (2026). AIs are highly inconsistent when recommending brands or products; marketers should take care when tracking AI visibility. https://sparktoro.com/blog/new-research-ais-are-highly-inconsistent-when-recommending-brands-or-products-marketers

Wallat, J., Heuss, M., de Rijke, M., & Anand, A. (2025). Correctness is not faithfulness in retrieval augmented generation attributions. https://doi.org/10.1145/3731120.3744592

Published by AIO Clicks — Digital Visibility Specialists | Haaksbergen, Netherlands | aioclicks.com