CGO Media Statistics Library™
The CGO Media Statistics Library™ brings together quantitative research covering AI search, ChatGPT usage, Google AI Overviews, GEO, search behaviour, citations, local search, technical SEO, ecommerce, enterprise search and wider digital performance. It provides the measurable evidence layer within the broader CGO Media research and knowledge ecosystem.
Last reviewed: August 2026
Building a Quantitative Evidence Library for Modern Search
Modern search research increasingly requires more than strategic interpretation.
As search engines, artificial intelligence systems and user behaviour evolve, organisations also need reliable quantitative evidence that helps explain how quickly those changes are occurring, where adoption is developing and which areas of digital visibility are becoming strategically important.
The CGO Media Statistics Library™ provides a dedicated home for this evidence.
It brings together numerical research covering search behaviour, AI adoption, generative search visibility, citations, technical performance, commercial search activity and other measurable aspects of digital discovery.
The purpose of the Library is not simply to collect numbers.
It is to organise statistics into a structured research system where data can support deeper analysis, Research Observations, long-form research papers and CGO Media strategic frameworks.
Statistics Library Principle
Quantitative evidence becomes more valuable when it is organised, contextualised and connected with wider research rather than presented as isolated statistics.
What Is the CGO Media Statistics Library?
The CGO Media Statistics Library™ is the central directory for quantitative research published across the CGO Media knowledge ecosystem.
It contains data-led resources examining measurable developments across search engines, artificial intelligence platforms, search behaviour and digital performance.
These resources may include:
- Adoption rates.
- Usage statistics.
- Search behaviour data.
- Market-share indicators.
- AI citation data.
- Traffic trends.
- Conversion indicators.
- Technical performance statistics.
- Industry-specific SEO data.
- Local and ecommerce search statistics.
The Statistics Library is distinct from the CGO Media Research Observations Library.
Research Observations primarily analyse emerging developments.
Statistics resources primarily organise and interpret measurable evidence.
Statistics Library Definition
The CGO Media Statistics Library™ is a structured collection of quantitative research assets that document measurable developments across search, artificial intelligence, digital discovery and online performance.
The Role of Statistics in the CGO Media Research System
Statistics provide the measurable evidence layer within the wider CGO Media research architecture.
Research papers can explain why a development matters.
Research Observations can examine how a specific change may be emerging.
Frameworks can provide strategic structures for interpreting and applying those developments.
Statistics help establish the measurable scale, frequency or direction of change.
This makes quantitative evidence important across the entire research system.
Measure Adoption
Statistics can help identify how quickly technologies and behaviours are being adopted.
Examples may include:
- AI search usage.
- ChatGPT adoption.
- Generative search usage.
- Google AI Overview exposure.
- Voice and conversational search behaviour.
Measure Behaviour
Search behaviour is not static.
Users may change:
- How they formulate queries.
- Which platforms they use.
- Whether they click traditional results.
- How they interact with generated answers.
- How they compare products, services and organisations.
Statistical research can help document these behavioural changes.
Measure Visibility
Modern visibility increasingly extends beyond rankings.
Quantitative research may examine:
- AI citation frequency.
- AI search traffic.
- Search-result exposure.
- Brand visibility.
- AI recommendation presence.
Measure Performance
Statistics also help organisations evaluate whether search visibility contributes to measurable outcomes.
Relevant areas can include:
- Traffic.
- Conversions.
- Revenue.
- Return on investment.
- Lead generation.
- Engagement.
Statistics provide the quantitative foundation that allows search developments to be measured rather than discussed only in conceptual terms.
Research Papers, Observations, Statistics and Frameworks
The CGO Media knowledge ecosystem contains several different research asset types.
Each performs a separate role.
CGO Media’s knowledge assets form a connected research system in which papers provide depth, observations examine emerging developments, statistics provide quantitative evidence and frameworks convert knowledge into practical strategic application.
One Research Ecosystem
Statistics can provide evidence for a Research Observation.
Research Observations can identify questions requiring deeper investigation.
Research Papers can integrate those findings into wider strategic analysis.
Frameworks can then convert accumulated evidence and research into structured methodologies.
Statistics Knowledge Flow
The Statistics Library forms the quantitative layer within the wider CGO Media research architecture.
A useful statistics resource does more than publish individual numbers.
It connects numerical evidence with context, interpretation and wider research.
Statistics Knowledge Flow
Data Source → Measurement → Statistical Finding → Context → Research Connection → Strategic Insight
Stage 1 — Data Source
Quantitative research begins with evidence.
Potential sources can include:
- Published industry datasets.
- Government statistics.
- Search-engine data.
- Technology platform reporting.
- Academic research.
- Survey data.
- Market research.
- CGO Media analysis.
Stage 2 — Measurement
Relevant variables are identified and measured.
These may include:
- Usage.
- Adoption.
- Traffic.
- Search behaviour.
- Visibility.
- Conversion.
- Market share.
- Performance.
Stage 3 — Statistical Finding
The numerical evidence is organised into understandable findings.
This may include:
- Percentages.
- Growth rates.
- Comparisons.
- Usage frequencies.
- Market indicators.
- Performance benchmarks.
Stage 4 — Context
A statistic without context can easily be misunderstood.
The Library therefore aims to explain:
- What the statistic measures.
- Where the evidence originated.
- When the data was collected.
- What limitations may apply.
- Why the finding may matter.
Stage 5 — Research Connection
Statistics can then be connected with related:
- Research Observations.
- Research papers.
- CGO Media frameworks.
- Industry analysis.
Stage 6 — Strategic Insight
The final purpose is not simply to present a number.
It is to help organisations understand whether measurable evidence indicates a meaningful change in search behaviour, technology adoption or digital performance.
The value of a statistic increases when readers can understand its source, context, limitations and relationship with wider research.
Core Statistics Research Areas
The CGO Media Statistics Library covers a broad range of search, artificial intelligence and digital performance topics.
These can be organised into several connected research categories.
AI Search and Generative Discovery
This category examines quantitative developments across AI-driven search environments.
Research areas include:
- AI search adoption.
- AI search market share.
- Generative search usage.
- AI search traffic.
- AI search behaviour.
- AI citation visibility.
ChatGPT and Conversational Search
Conversational interfaces are changing how users search for information.
Statistics in this area can examine:
- ChatGPT usage.
- Conversational search adoption.
- User behaviour.
- Search replacement behaviour.
- AI-assisted research.
Google AI Overviews
Google AI Overviews represent an important development in the evolution of traditional search results.
Statistics can help document:
- AI Overview prevalence.
- Query coverage.
- Search-result changes.
- Click behaviour.
- Source visibility.
GEO and AI Visibility
Generative Engine Optimisation introduces new measurement questions.
Relevant statistics may include:
- AI citation rates.
- Source visibility.
- Brand mentions.
- Recommendation appearances.
- AI referral traffic.
SEO and Search Performance
Traditional search performance remains an important part of the wider ecosystem.
The Statistics Library can include data covering:
- SEO ROI.
- Technical SEO.
- Local SEO.
- Ecommerce SEO.
- Enterprise SEO.
- Small-business SEO.
- Website performance.
Search Behaviour
Search behaviour statistics help explain how users interact with modern discovery systems.
Areas may include:
- Search platform choice.
- Query behaviour.
- AI adoption.
- Mobile search.
- Voice search.
- Conversational search.
- Search-result interaction.
Statistics Research Principle
The CGO Media Statistics Library is designed to document measurable change across the complete search ecosystem rather than treating SEO, AI search, user behaviour and commercial performance as separate disciplines.
Statistics Library Directory
The CGO Media Statistics Library™ organises quantitative research into clear subject areas so readers can move quickly between different dimensions of modern search, artificial intelligence and digital performance.
The Library is intended to function as a research directory rather than a loose collection of standalone statistics pages.
Each statistical resource contributes to one or more wider research themes, including AI adoption, search behaviour, citation visibility, commercial performance, technical SEO and industry-specific search activity.
The current Statistics Library can be organised into several major categories:
- AI search statistics.
- ChatGPT and conversational search statistics.
- Google AI Overview statistics.
- GEO statistics.
- AI citation statistics.
- Search behaviour statistics.
- Technical SEO statistics.
- Local SEO statistics.
- Ecommerce SEO statistics.
- Enterprise SEO statistics.
The Statistics Library provides the quantitative evidence layer that supports wider CGO Media research into how search behaviour, AI adoption, visibility and digital performance are changing.
Statistics Research Categories
The CGO Media Statistics Library is organised around complementary categories covering AI search, conversational AI, Google AI Overviews, GEO, citations, search behaviour and SEO performance.
AI Search Statistics
AI search is emerging as a major new component of the digital discovery ecosystem.
Users increasingly interact with generative systems not only to retrieve information, but also to compare options, conduct research, discover products and services and receive direct recommendations.
Quantitative research is important because the scale and speed of this transition can easily be overstated or underestimated.
AI search statistics can help document:
- AI search adoption.
- Frequency of use.
- Demographic patterns.
- Search substitution behaviour.
- AI search market share.
- Referral traffic.
- Commercial usage.
- Growth trends.
AI Search Statistics UK 2026
The AI Search Statistics UK 2026 resource examines measurable developments in the adoption and use of artificial intelligence for information discovery in the United Kingdom.
It provides quantitative context for wider research into how generative search is changing traditional search behaviour.
AI Search Market Share Statistics 2026
AI search market-share research can help establish the relative scale of emerging generative discovery systems compared with conventional search engines and other digital platforms.
Market-share figures should always be interpreted carefully because different datasets may measure:
- Website visits.
- Application usage.
- Queries.
- Monthly active users.
- Referral traffic.
Different measurement methods can produce different results.
AI Search Traffic Statistics UK 2026
AI search traffic statistics examine whether generative discovery platforms are beginning to contribute measurable referral traffic to websites.
This is important because visibility inside an AI response does not always result in a conventional website click.
Traffic research can therefore help distinguish between:
- AI visibility.
- Source citation.
- Referral traffic.
- Assisted discovery.
- Downstream conversion.
AI Search Behaviour Statistics UK 2026
AI search behaviour statistics examine how users interact with generative discovery systems and whether those behaviours differ from traditional web search.
Research can include:
- Question length.
- Conversational queries.
- Follow-up questions.
- Research behaviour.
- Recommendation queries.
- Commercial intent.
AI Search Measurement Principle
AI search adoption should be evaluated through multiple indicators rather than one headline number, because usage, traffic, market share and behavioural change measure different aspects of the transition.
ChatGPT Usage Statistics
ChatGPT has become one of the most visible examples of conversational artificial intelligence.
Its importance extends beyond general AI adoption.
Users can use ChatGPT for:
- Research.
- Information discovery.
- Product comparison.
- Travel planning.
- Professional advice.
- Writing and analysis.
- Commercial research.
This makes ChatGPT usage relevant to wider questions about the future of search.
ChatGPT Usage Statistics UK 2026
The ChatGPT Usage Statistics UK 2026 resource examines quantitative evidence concerning ChatGPT adoption and use in the United Kingdom.
The research can provide context for questions such as:
- How widely is ChatGPT used?
- How frequently do users interact with it?
- Which age groups are adopting generative AI?
- How is usage changing?
- What activities are users performing?
Why ChatGPT Statistics Matter for Search Research
Traditional search measurement typically focuses on search-engine market share.
Conversational AI introduces a different form of information discovery.
A user may receive an answer without visiting a conventional search-results page.
This means ChatGPT usage statistics can provide an additional indicator of how information-seeking behaviour is evolving.
The growth of conversational AI does not necessarily mean conventional search disappears. It indicates that users increasingly have multiple discovery interfaces available for different information needs.
Google AI Overview Statistics
Google AI Overviews represent a significant change in how generative artificial intelligence is incorporated into mainstream search.
Instead of requiring users to leave the search-result environment immediately, AI Overviews can synthesise information directly within the results page.
This creates important measurement questions.
Statistics can help examine:
- How frequently AI Overviews appear.
- Which types of query trigger them.
- How their prevalence changes over time.
- How citation patterns develop.
- How click behaviour may change.
Google AI Overview Statistics UK 2026
The Google AI Overview Statistics UK 2026 resource brings together quantitative evidence about Google AI Overviews and their role within the evolving search-results environment.
The resource can support wider analysis of:
- Search-result changes.
- Organic visibility.
- Source citations.
- Click-through behaviour.
- Generative search adoption.
AI Overview Research Principle
Google AI Overview visibility should be analysed as part of the wider search-results ecosystem rather than as a complete replacement for traditional organic search.
GEO Statistics
Generative Engine Optimisation, commonly referred to as GEO, focuses on improving visibility across generative search and artificial intelligence environments.
Because GEO is still developing as a discipline, quantitative evidence is especially important.
Statistics can help distinguish between genuine changes in digital discovery and assumptions based primarily on industry speculation.
Relevant GEO measurements can include:
- Generative search adoption.
- AI citation frequency.
- Brand mentions.
- AI referral traffic.
- Recommendation visibility.
- Source selection patterns.
GEO Statistics UK 2026
The GEO Statistics UK 2026 resource provides quantitative context for the development of generative search optimisation.
It connects emerging AI search behaviour with measurable questions surrounding discoverability, citations and recommendation visibility.
GEO as a Measurement Discipline
GEO measurement should extend beyond whether an organisation appears in one AI-generated answer.
A broader approach may examine:
- Frequency of appearance.
- Source citation.
- Brand mention share.
- Recommendation presence.
- Query-category visibility.
- Referral traffic.
Effective GEO measurement requires repeated observation across multiple prompts, systems and discovery contexts rather than relying on isolated AI responses.
AI Citation Statistics
Citation visibility has become one of the most important measurable dimensions of generative search.
AI systems may retrieve information from multiple sources and, depending on the interface, reference or cite those sources within generated answers.
Citation statistics can therefore help investigate:
- Which sources appear most frequently.
- What types of content are cited.
- How citation patterns vary by query.
- Whether citations correlate with traditional rankings.
- How citation visibility changes over time.
AI Citation Statistics 2026
The AI Citation Statistics 2026 resource examines quantitative evidence concerning citation behaviour across generative search environments.
It supports wider CGO Media research into source authority and AI citation visibility.
Citations as a New Visibility Metric
Traditional SEO measurement frequently focuses on ranking position.
Generative search introduces additional visibility measures.
A source can potentially be:
- Retrieved.
- Used.
- Referenced.
- Cited.
- Recommended.
Each represents a different type of interaction with the source.
Citation Measurement Flow
Source Discovery → Retrieval → Use → Citation → Visibility
Search Behaviour Statistics
Understanding search behaviour is essential for interpreting changes in search technology.
Technology adoption alone does not explain how users behave.
Search behaviour research examines how people formulate questions, choose platforms, evaluate information and move between different discovery environments.
Search Behaviour Statistics UK 2026
The Search Behaviour Statistics UK 2026 resource examines measurable changes in how UK users search for and evaluate information.
Research areas can include:
- Search-engine usage.
- AI search adoption.
- Mobile search behaviour.
- Voice search.
- Conversational search.
- Commercial research.
- Local discovery.
Voice and Conversational Search Statistics UK 2026
Voice and conversational search statistics provide additional evidence about the move from short keyword queries towards more natural language interaction.
This research can help document:
- Voice-assistant usage.
- Conversational query behaviour.
- Mobile interaction.
- Local search use.
- AI-assisted discovery.
Behaviour Research Principle
The evolution of search should be measured through user behaviour as well as platform technology.
A new search interface becomes strategically important when users begin incorporating it into real information and decision-making journeys.
Technical SEO Statistics
AI-driven search does not remove the importance of technical accessibility.
Search engines and generative systems still depend on accessible, understandable and retrievable information.
Technical SEO statistics can therefore provide evidence relating to:
- Website speed.
- Mobile performance.
- Crawlability.
- Indexation.
- Core Web Vitals.
- Structured data.
- Technical errors.
Technical SEO Statistics UK 2026
The Technical SEO Statistics UK 2026 resource examines quantitative evidence relating to the technical foundations of search visibility.
Website Speed Statistics UK 2026
Website performance influences user experience and can affect how effectively digital resources are accessed.
The Website Speed Statistics UK 2026 resource provides evidence relating to performance, loading behaviour and wider digital experience.
Local SEO Statistics
Local search remains one of the most commercially important forms of digital discovery.
Users frequently search for organisations, services and products within a geographic context.
Local SEO statistics can examine:
- Local search behaviour.
- Mobile local search.
- Google Business Profile interaction.
- Reviews.
- Location-based conversion.
- AI-assisted local discovery.
Local SEO Statistics UK 2026
The Local SEO Statistics UK 2026 resource provides quantitative evidence about local search behaviour and local digital visibility.
Ecommerce SEO Statistics
Ecommerce search combines informational, commercial and transactional behaviour.
Users may move between traditional search, marketplaces, social platforms and artificial intelligence systems before making a purchase.
Ecommerce SEO statistics can examine:
- Product discovery.
- Organic ecommerce traffic.
- Mobile shopping behaviour.
- Search conversion.
- AI-assisted product research.
- Commercial search trends.
Ecommerce SEO Statistics UK 2026
The Ecommerce SEO Statistics UK 2026 resource provides quantitative evidence relating to ecommerce visibility and search-driven commercial behaviour.
Enterprise SEO Statistics
Enterprise search environments involve larger websites, complex organisational structures and extensive content ecosystems.
Enterprise SEO statistics can help quantify:
- Technical complexity.
- Content scale.
- International search.
- Governance challenges.
- Search performance.
- AI-search readiness.
Enterprise SEO Statistics UK 2026
The Enterprise SEO Statistics UK 2026 resource examines quantitative evidence relating to large-scale search programmes and enterprise digital visibility.
From Individual Statistics to Research Evidence
The value of the Statistics Library comes from connecting individual data resources.
AI search adoption can be compared with search behaviour.
Search behaviour can be connected with citation and traffic data.
Technical, local, ecommerce and enterprise statistics can then show how broader changes affect different areas of digital visibility.
Additional Statistics Resources
The CGO Media Statistics Library™ extends beyond the core AI search, citation and behaviour categories.
The wider statistics programme also includes resources examining SEO performance, marketing effectiveness, small-business visibility and the commercial impact of search.
These additional statistics help connect AI-search research with the broader digital environment in which organisations operate.
SEO ROI Statistics UK 2026
The SEO ROI Statistics UK 2026 resource examines quantitative evidence relating to the commercial performance of search-engine optimisation.
Relevant research areas can include:
- Organic traffic contribution.
- Lead generation.
- Customer acquisition.
- Conversion rates.
- Revenue contribution.
- Long-term search value.
UK SEO Statistics 2026
UK SEO Statistics 2026 provides a broader quantitative view of search-engine optimisation within the United Kingdom.
The resource can help contextualise:
- SEO adoption.
- Search investment.
- Organic visibility.
- Business dependence on search.
- Agency and in-house activity.
UK AI SEO, GEO & AEO Pricing Study 2026
Research Category: AI Search, GEO, AEO & Search Economics
CGO Media’s UK AI SEO, GEO & AEO Pricing Study 2026 examines how AI-search optimisation services are currently priced, packaged and delivered across the UK market.
The research analyses publicly available provider pricing, service descriptions and published industry evidence to identify the emerging cost structure of AI SEO, Generative Engine Optimisation and Answer Engine Optimisation.
The study distinguishes between measurement, audits, implementation, authority development and enterprise governance, and examines why apparently similar AI-search services can range from hundreds to more than £10,000 per month.
The paper also introduces several CGO Media research models, including the AI Search Investment Spectrum, AI Search Investment Model™, AI Search Cost Drivers Model and Integrated Search Authority Model.
Small Business SEO Statistics UK 2026
Small businesses often depend heavily on search visibility but operate with more limited resources than enterprise organisations.
Statistics in this area can examine:
- SEO adoption.
- Local search dependence.
- Digital marketing investment.
- Website performance.
- Lead generation.
- Search-driven customer acquisition.
Digital Marketing Statistics UK 2026
Search operates within a much wider digital marketing ecosystem.
Digital Marketing Statistics UK 2026 can provide context for:
- Digital advertising.
- Content marketing.
- Social media.
- Email marketing.
- Search marketing.
- Marketing technology.
Content Marketing Statistics UK 2026
Content remains a central component of both traditional search and AI-driven discovery.
Content marketing statistics can examine:
- Content investment.
- Publishing frequency.
- Organic traffic contribution.
- Lead generation.
- Authority development.
- Content performance.
The Statistics Library is designed to connect AI search research with the wider commercial and digital marketing environment rather than treating generative search as an isolated discipline.
Connecting Statistics with Research Observations
Statistics and Research Observations perform different functions within the CGO Media knowledge system.
Statistics provide measurable evidence.
Research Observations provide focused interpretation.
The strongest analysis can emerge when the two are connected.
A statistic may show that AI search adoption is increasing.
A Research Observation can then examine what that increase may mean for search visibility, authority or conversion.
Similarly, citation statistics may show that certain types of source appear repeatedly.
A citation-focused Research Observation can then analyse the strategic significance of those patterns.
Evidence and Interpretation
The relationship can be expressed simply:
Statistics-to-Observation Flow
Quantitative Evidence → Identified Pattern → Research Observation → Strategic Interpretation
This helps prevent statistics from being interpreted without sufficient context.
CGO Media statistics provide quantitative evidence that can be connected to Research Observations, allowing measurable search behaviour, citation, adoption, traffic and ROI signals to inform broader analytical interpretation.
Evidence Layer Principle
Statistics should support interpretation, not replace it.
A numerical trend becomes strategically useful when its context, limitations and wider implications are understood.
Connecting Statistics with Research Papers
Long-form Research Papers provide the broadest analytical layer within the CGO Media research programme.
Statistics can strengthen those papers by providing measurable evidence for major themes.
For example:
- AI search adoption statistics can support research into the evolution of search.
- Google AI Overview statistics can support research into changing organic search interfaces.
- Technical SEO statistics can support research into the future of technical optimisation.
- Enterprise SEO statistics can support large-scale search research.
- Local SEO statistics can support research into AI-assisted local discovery.
This creates a stronger relationship between measurable evidence and long-form analysis.
From Data to Deeper Research
A statistical finding may raise additional questions.
If those questions are strategically important, they can contribute to:
- New Research Observations.
- New Research Papers.
- Additional data collection.
- Framework revisions.
- Measurement methodologies.
Statistics can identify where change is occurring. Research Papers can investigate why that change matters and what its long-term consequences may be.
Connecting Statistics with CGO Media Frameworks
Frameworks provide the strategic structure used to interpret evidence.
Statistics can support framework development by showing whether particular dimensions of search, authority or digital performance are becoming more significant.
They can also provide measurement indicators for framework implementation.
AI Search Readiness Framework
AI search statistics can help organisations understand the wider environment in which AI-search readiness is becoming important.
Relevant measurements may include:
- AI adoption.
- AI search behaviour.
- AI traffic.
- Citation visibility.
AI Citation Framework
AI Citation Statistics can provide the quantitative layer supporting the CGO Media AI Citation Framework™.
Statistical evidence can help examine:
- Citation frequency.
- Source diversity.
- Domain visibility.
- Content-type patterns.
Search Visibility Framework
Search visibility statistics can support a broader visibility model incorporating:
- Organic rankings.
- AI appearances.
- Citations.
- Referral traffic.
- Brand visibility.
Search Visibility Score Methodology
Quantitative data is particularly important for measurement methodologies.
Different indicators can potentially be combined to create a broader picture of digital visibility.
These indicators may include:
- Ranking presence.
- Search-result visibility.
- AI citation frequency.
- Brand mentions.
- Referral traffic.
Statistics-to-Framework Flow
Measurement → Evidence → Pattern → Framework Interpretation → Strategic Application
Methodology and Data Interpretation
Statistics should be interpreted carefully.
A numerical finding is only as useful as the methodology behind it.
Different sources may measure similar concepts in very different ways.
For example, one dataset may measure monthly active users while another measures website visits.
Both can be valid, but they should not automatically be treated as equivalent.
Statistical Integrity Principle
CGO Media statistics should distinguish clearly between the original data source, the measurement being reported and CGO Media interpretation of what that evidence may mean.
Source Quality
Where possible, quantitative research should prioritise reliable and transparent sources.
These can include:
- Government datasets.
- Official platform reporting.
- Academic studies.
- Recognised market-research organisations.
- Published industry datasets.
- Primary survey data.
Measurement Definition
Readers should be able to understand exactly what a statistic measures.
This includes distinctions between:
- Users and visits.
- Searches and sessions.
- Market share and traffic share.
- Citations and mentions.
- Visibility and clicks.
- Correlation and causation.
Time Period
Search and artificial intelligence markets can change rapidly.
The date of a dataset therefore matters.
Statistics should be interpreted in relation to:
- Collection date.
- Publication date.
- Geographic scope.
- Platform version.
Geographic Scope
UK statistics should not automatically be generalised to every market.
Different countries can have different:
- Platform adoption rates.
- Search behaviour.
- Regulatory environments.
- Commercial patterns.
Sample Size and Methodology
Survey statistics should be interpreted according to the size and composition of the sample.
Relevant factors can include:
- Number of respondents.
- Age distribution.
- Geographic distribution.
- Sampling technique.
- Question wording.
Correlation and Causation
Statistical relationships do not automatically prove causation.
For example, a relationship between website performance and conversion may indicate an association without proving that one factor alone created the outcome.
This distinction is important when statistics are used to support strategic decisions.
Quantitative evidence should be interpreted within its source, measurement definition, timeframe, geographic scope and sample characteristics before broader conclusions are drawn.
Research Usage and Citation
CGO Media statistics resources are designed to support research, journalism, strategic analysis and wider discussion of search and artificial intelligence.
Researchers, journalists, publishers and organisations may reference statistics with clear attribution to the original source.
Where a CGO Media page reports a statistic originating from another organisation, the original source should remain identifiable.
Where CGO Media produces its own analysis or interpretation, that contribution should be attributed separately.
Recommended Citation Principle
Cite the original statistical source where applicable, and cite CGO Media when referencing CGO Media analysis, compilation, interpretation or proprietary research.
Citing the CGO Media Statistics Library
The Statistics Library itself may be referenced when discussing the wider CGO Media quantitative research programme.
According to the
CGO Media Statistics Library™
quantitative search research should connect measurable evidence with methodology, context and wider strategic analysis.
APA Citation
CGO Media. (2026). CGO Media Statistics Library. CGO Media. https://cgomedia.com/cgo-media-statistics-library/
CGO Media Research Ecosystem
The Statistics Library forms the quantitative evidence layer within a wider CGO Media research system.
The complete ecosystem includes research papers, Research Observations, statistics, strategic frameworks and knowledge architecture resources.
CGO Media Research Library
The Research Library contains the main long-form CGO Media AI Search Research Series.
These papers provide deeper investigation of major developments across search and AI discovery.
CGO Media Research Architecture
The Research Architecture organises the main research programme into connected analytical layers.
CGO Media Research Observations Library
The Research Observations Library provides focused analytical studies examining specific developments across AI search, authority, citations, recommendations, visibility, conversion and ROI.
CGO Media Framework Library
The Framework Library contains strategic models and methodologies that translate research findings into structured approaches for implementation.
CGO Media Knowledge Architecture Map™
The Knowledge Architecture Map explains how research, frameworks, services, expertise and supporting knowledge assets connect within the wider CGO Media ecosystem.
One Connected Evidence System
Research Papers provide depth.
Research Observations provide focused interpretation.
Statistics provide quantitative evidence.
Frameworks provide strategic structure.
Knowledge Architecture connects the complete system.
Frequently Asked Questions
What is the CGO Media Statistics Library?
The CGO Media Statistics Library™ is a structured collection of quantitative research covering search, artificial intelligence, digital discovery, SEO and online performance.
How is the Statistics Library different from the Research Observations Library?
The Statistics Library focuses primarily on quantitative evidence.
The Research Observations Library focuses primarily on analytical interpretation of specific developments and emerging patterns.
What subjects are covered in the Statistics Library?
The Library covers areas including AI search, ChatGPT, Google AI Overviews, GEO, AI citations, search behaviour, technical SEO, local SEO, ecommerce SEO, enterprise SEO and wider digital marketing.
Can journalists cite CGO Media statistics pages?
Yes.
Journalists, researchers and publishers may cite CGO Media statistics resources with appropriate attribution.
Where a statistic originates from an external dataset, the original source should also remain identifiable.
Are all statistics produced directly by CGO Media?
No.
Some resources may compile and analyse statistics from third-party research, official datasets and published studies.
Where CGO Media produces proprietary research or analysis, that contribution should be identified clearly.
How current are the statistics?
Statistics pages should indicate the relevant year, source publication date and available data period.
Resources can be reviewed as newer evidence becomes available.
Why does methodology matter?
Different datasets can measure different things.
Understanding methodology helps readers determine whether statistics are comparable and what conclusions can reasonably be drawn.
Will the Statistics Library continue to expand?
Yes.
The Library is designed as an evolving resource that can incorporate new quantitative research as search, artificial intelligence and digital behaviour continue to change.
Research Governance
CGO Media aims to present quantitative evidence in a way that distinguishes clearly between:
- Primary data.
- Third-party statistics.
- Published research.
- CGO Media compilation.
- CGO Media interpretation.
- CGO Media proprietary research.
This distinction helps preserve transparency around where evidence originates and how it is being used.
Statistics Governance Principle
Quantitative research should remain transparent about source, methodology, time period, geographic scope and interpretation.
Explore the CGO Media Research System
The Statistics Library forms part of a wider research ecosystem examining search, artificial intelligence, authority, visibility and digital discovery.
Continue exploring the main CGO Media research resources:
CGO Media Research Library
CGO Media Research Architecture
CGO Media Research Observations Library™
CGO Media Framework Library
CGO Media Knowledge Architecture Map™
CGO Media
The CGO Media Statistics Library™ provides the quantitative evidence layer within the wider CGO Media research ecosystem, connecting measurable developments with analytical research, strategic frameworks and the future evolution of search.
