Market research is being reshaped by two forces at once: a wave of AI-driven tooling that is compressing timelines and costs, and a data environment that is harder to trust than it was five years ago. For business leaders who commission or rely on research, 2026 is a year to recalibrate rather than assume the old playbook still applies. The market research statistics 2026 data show an industry that is larger, faster, and more automated than ever, but also one wrestling with fraud, synthetic respondents, and a shrinking pool of reliable third-party data.
This article pulls together verified market research industry statistics from professional associations, market analysts, and primary research organizations, and translates them into practical implications. Historical figures, current estimates, and forward-looking projections are labeled throughout so readers can tell what is measured, what is estimated, and what is still uncertain. Businesses that use industry-specific market research reports to guide strategy will find the underlying shifts in how that data gets produced just as important as the numbers themselves.
Market research in 2026: key statistics at a glance
- The global insights industry (market research, research software, and reporting combined) surpassed US$150 billion in 2024 and was projected to exceed US$160 billion by the end of 2025, according to ESOMAR’s Global Market Research 2025 report.
- Within that total, the core market research sector generated an estimated US$56 billion in 2024, growing 4.8% year over year — a slower pace than the research software segment.
- Research software was the fastest-growing segment of the insights industry, expanding 11.5% in 2024, more than double the growth rate of traditional market research services.
- A narrower definition — market research services alone, as tracked by The Business Research Company — was valued at $93.37 billion in 2025 and is projected to reach $96.77 billion in 2026, a 3.6% year-over-year increase, with a forecast of $116.02 billion by 2030.
- The Asia Pacific region had the fastest-growing insights industry of any world region in 2023, expanding 9.5% versus 8.0% globally, driven largely by research software adoption.
- 67% of research suppliers now embed generative AI directly into client deliverables, according to Greenbook’s 2025 GRIT Insights Practice Report.
- 62% of market researchers reported using synthetic data (AI-generated respondent data) in the six months before a 2025 Qualtrics study.
- 40% of researchers cite data quality as their top professional challenge, per the same 2025 GRIT report.
- An Insights Association analysis presented in January 2026 estimated that roughly 40% of nonprobability online survey interviews in 2025 were likely fraudulent, equivalent to an estimated 2 billion fraudulent interviews industry-wide.
- 59% of data leaders reported an organizational AI skills gap in early 2026, despite 82% of organizations offering some form of AI training, according to DataCamp’s 2026 State of Data & AI Literacy Report.
- Chrome confirmed in April 2025 that it will not force the deprecation of third-party cookies, leaving them on by default with an opt-out — a reversal that complicates, but does not eliminate, the industry’s shift toward first-party and zero-party data strategies.
These figures set the stage for the trends explored below: an industry that is growing steadily by revenue, being reorganized around AI and automation, and increasingly defined by the tension between speed and trust.
The size and trajectory of the global market research industry
Depending on how “market research” is defined, estimates of industry size vary considerably — a point worth understanding before citing any single number. ESOMAR’s Global Market Research 2025 report, widely regarded as the sector’s most comprehensive benchmarking study, uses a three-part framework covering market research services, research software, and reporting/analytics. Under that broader “insights industry” definition, global turnover surpassed US$150 billion in 2024 and was expected to surpass US$160 billion by the end of 2025. The market research sector itself accounted for about US$56 billion of that total, research software for US$62 billion, and reporting for US$35 billion.
By sector, market research showed the characteristics of a mature, consolidated industry, expanding 4.8% in 2024, while research software continued to lead growth at 11.5%, and reporting advanced at 8%. This divergence matters for strategy: the fastest-growing part of the insights economy is not fieldwork or data collection, but the software layer that automates and accelerates it.
A narrower, services-only definition tells a similar growth story at a different scale. The Business Research Company’s 2026 market analysis estimated the market research services market at $93.37 billion in 2025, growing to $96.77 billion in 2026 at a compound annual growth rate of 3.6%, with continued growth to $116.02 billion by 2030 at a 4.6% CAGR. Regardless of which definition is used, the direction is consistent: steady, mid-single-digit growth, with software and automation contributing a disproportionate share of new revenue.
Regional growth also varies meaningfully. Asia Pacific had the fastest-growing insights industry of any region in 2023, at 9.5% growth compared with 8.0% globally, an expansion driven primarily by the research software sector, which grew 19.5% in absolute terms in the region. For organizations evaluating where to expand research capacity, this regional divergence is a useful signal — a topic covered in more depth in Kentley Insights’ global market size and growth reports for service industries, which track market expansion by country and sector.
Notably, market research itself sits inside a broader universe of professional and knowledge services that Kentley Insights tracks directly, including the research and intellectual property services sector, which provides additional context on how research-adjacent industries are sized and segmented globally.
Generative AI and automation are now standard infrastructure
Adoption of AI tools in market research has moved past the pilot stage. Greenbook’s 2025 GRIT Insights Practice Report found that the industry has converged on three tasks where agentic AI is already embedded: analyzing data, updating reports, and preparing and integrating data. Separately, industry commentary on the same report noted that a staggering 67% of suppliers now bake generative AI directly into client deliverables, automating tasks from survey design to cross-tab analysis.
This is consistent with broader enterprise AI adoption trends. Across industries generally, McKinsey’s 2025 survey found 78% of companies using AI in at least one business function, a context that helps explain why research buyers now expect AI-assisted turnaround times as a baseline rather than a differentiator.
Importantly, most industry sources are careful to frame AI as augmentation rather than replacement. Generative AI is streamlining research processes by automating repetitive tasks and assisting researchers in making better decisions, rather than replacing the researchers who interpret and act on the findings. That distinction is echoed by practitioners writing about the same GRIT data, who describe the shift as freeing analysts to spend less time cleaning data and more time on interpretation and strategic recommendations, rather than eliminating the analyst role outright.
Why human judgment still anchors the research function
Despite the pace of automation, the data does not support a narrative of researchers being displaced. Forsta’s 2026 analysis of the profession puts it plainly: researchers who thrive in 2026 will not be defined by how fast they execute technical tasks, but by their interpersonal skills and the impact of their data communication, since clients increasingly look to agencies for deeper insight interpretation rather than faster production alone.
This shift changes what “skill” means in the profession. Historically, much of a researcher’s value was tied to execution — writing questionnaires, managing fieldwork, cleaning data, building charts, and manually producing reports. As AI absorbs more of that execution work, the differentiating skill set moves toward judgment: knowing which questions are worth asking, recognizing when data looks wrong, and translating findings into decisions a business leader can act on. Organizations evaluating strategic performance benchmarks should treat this shift as a reason to invest in interpretive capability, not just in tooling.
Synthetic data and simulated respondents are moving from experiment to practice
One of the more consequential shifts in 2026 is the rise of synthetic data: AI-generated responses intended to simulate how real consumers or professionals would answer a survey. Adoption has grown quickly. A 2025 Qualtrics study found that 62% of market researchers had used synthetic data in the six months before the survey, and Greenbook’s own reporting on the 2025 GRIT data noted that synthetic data crossed from a niche topic to top-three industry buzz in a single wave.
The appeal is speed and cost. Early adopters cited in industry coverage of the GRIT report have used synthetic respondents to compress research timelines, in some cases reportedly cutting weeks of fieldwork down to days. But the practice remains genuinely unsettled from a methodological standpoint. Synthetic data is trained on prior human response patterns, which means it can reproduce historical biases and may struggle to anticipate genuinely novel consumer behavior. Most credible industry voices frame synthetic data as a complement to primary research for hypothesis generation and pre-testing — not a wholesale substitute for validated human data, particularly for high-stakes decisions.
First-party and zero-party data strategies are becoming the default
As third-party data sources become less reliable, more organizations are building direct data relationships with their own customers. First-party data (collected through direct interactions, such as purchases or account activity) and zero-party data (information customers proactively share, such as preferences or survey responses) are increasingly treated as the more durable foundation for research and targeting.
First-party data collection now requires a systematic and consensual approach — email signups, purchase history, on-site behavior, loyalty programs, and progressive profiling form the foundation of cookieless strategies, with every collection touchpoint requiring clear consent disclosure under frameworks like GDPR, CCPA, and CPRA. This is less a research methodology shift than an operational one: companies that already run structured customer feedback programs, membership schemes, or transactional data capture are better positioned to build research on top of assets they already own, rather than depending on external panels or ad-tech identifiers.
Privacy regulation and the uneven decline of third-party identifiers
The long-anticipated death of the third-party cookie has not arrived on schedule, and the regulatory picture in 2026 is more complicated than a straightforward phase-out. In April 2025, Google confirmed it will not ship a separate third-party cookie deprecation mechanism in Chrome and will not force deprecation; third-party cookies remain on by default in Chrome, with an opt-out available in settings. Safari, Firefox, and Brave, by contrast, continue to block third-party cookies by default, which means the browser landscape itself is now split rather than converging.
Enforcement, meanwhile, has intensified even as the technical deprecation stalled. French regulator CNIL issued a €325 million fine against Google and a €150 million fine against Shein in 2025, both tied to cookies set without valid consent and non-functional reject controls. In the United States, the privacy landscape remains fragmented, with California’s CCPA/CPRA, Virginia, Colorado, Connecticut, and Utah each maintaining active state privacy laws and no federal standard yet in place, according to IAPP tracking cited by Searchlab’s 2026 privacy statistics roundup.
For research teams, the practical takeaway is that consent management, transparency, and defensible data governance now matter as much as the specific technical mechanism (cookies or otherwise) used to collect data. Treating privacy compliance as a fixed, one-time technical fix is increasingly risky given how unsettled both the regulatory and platform environments remain.
Real-time, agile, and always-on research
Business cycles have compressed, and research timelines have compressed with them. Where a full quantitative study once took months, agile and always-on research programs are increasingly expected to deliver usable findings in days. This shift is closely tied to the AI adoption trends described above: automated survey scripting, AI-assisted analysis, and synthetic pre-testing all shorten the distance between a research question and an answer.
This speed comes with trade-offs. Faster research cycles generally mean smaller sample sizes, more automated quality control, and less time for methodological review — all of which raise the stakes on data quality practices discussed later in this article. Organizations operating in categories exposed to rapid cost pressure, which Kentley Insights tracks through resources like its highest-inflation industries analysis, have particular reason to value faster research cycles, since pricing and demand conditions in those categories can shift meaningfully within a single quarter.
Mobile-first, social listening, and passive-data research
Consumer attention has continued its long shift toward mobile devices and social platforms, and research methodology has followed. Passive and behavioral data — collected through app usage, browsing patterns, and social engagement, rather than direct questioning — is increasingly used alongside traditional surveys to validate or supplement self-reported findings. Social listening tools, which analyze public conversation across social platforms, have become a standard input for tracking brand sentiment and emerging trends in near real time, particularly for consumer categories where stated preferences and revealed behavior often diverge.
The practical implication for research design is methodological triangulation: pairing what people say (surveys, interviews) with what they do (behavioral and passive data) produces a more reliable picture than either source alone, especially as self-reported survey data faces the authenticity challenges described below.
Qualitative research at scale
Qualitative research — traditionally limited by the time-intensive nature of interviews and focus groups — is being reshaped by AI-assisted analysis. According to Stratega Research’s review of 2026 market research trends, Greenbook’s 2025 GRIT Report found that high-performing insight suppliers now automate an average of 5.1 project functions using AI, with transcription, theme clustering, and summarization among the tasks most commonly handled by AI tools. Work that once took researchers days — reading transcripts, identifying themes, and pulling representative quotes — increasingly happens in hours.
This has made it economically feasible to run qualitative research at a scale that was previously impractical: dozens of interviews or virtual focus groups analyzed with the same rigor once reserved for a handful. The tradeoff is that AI-assisted synthesis is only as good as the underlying interview quality and the analyst’s ability to catch when an automated summary has flattened an important nuance — reinforcing, again, why human interpretive skill remains central even as production work is automated.
Data quality, survey fraud, and respondent authenticity
Data quality is arguably the industry’s most urgent operational problem in 2026, and the numbers are stark. An Insights Association webinar presented in late January 2026 estimated that 40% of nonprobability online survey interviews in 2025 were likely fraudulent — roughly 2 billion fraudulent interviews industry-wide. The presentation noted that the majority of this fraud remains human, click-farm-based rather than AI-generated, though AI-driven fraud is regarded as still in its early stages.
This aligns with the concerns raised in Greenbook’s GRIT research, where 40% of researchers cited data quality as their top barrier, a problem fueled in part by AI-generated “synthetic respondents” contaminating legitimate sample pools. Academic reviews of the issue point in a similar direction: a literature review compiled by NORC found that survey fraud has dramatically undermined data quality, with the share of usable responses in some studies dropping from roughly 75% to as low as 10% in recent years, though estimates vary widely depending on panel composition and detection methodology.
The response from the industry has been a shift toward layered verification — combining behavioral analysis, biometric or device-based checks, and human review — rather than relying on any single fraud-detection method. For business leaders commissioning research, this means data quality assurances should be treated as a specific, answerable question during vendor selection, not an assumed baseline.
Demand for industry-specific and localized market intelligence
Generic market overviews are losing ground to research and data that reflect the specific dynamics of a given industry, region, or business segment. This trend shows up clearly in regional insights-industry data: Asia Pacific’s insights industry grew faster than the global average in 2023, driven substantially by research software adoption, a pattern that reflects how differently research infrastructure is developing market by market. Businesses evaluating opportunities in a specific vertical increasingly need data segmented at the industry level rather than broad macroeconomic indicators, a demand that has fueled steady interest in resources like Kentley Insights’ fastest-growing service industries analysis, which breaks down growth trends by individual sector rather than by economy-wide averages.
This localization trend also extends to methodology. Research conducted for a single global campaign increasingly needs country-specific adaptation — different consent norms, different preferred research channels, and different levels of trust in AI-mediated data collection — rather than a one-size-fits-all global instrument.
Integrating market research with competitive intelligence and strategic planning
Market research is increasingly treated as one input within a broader intelligence function that also includes competitive intelligence, customer experience data, and financial benchmarking, rather than a standalone deliverable. This integration reflects a practical reality: a market research report that describes customer sentiment in isolation is less useful to a strategy team than one connected to competitive positioning and internal performance data.
For companies building this kind of integrated view, understanding sector-level profitability alongside customer research is a natural pairing — a use case reflected in how organizations use Kentley Insights’ most profitable industries data alongside customer and market research to prioritize where strategic investment will have the greatest impact. Research functions that can connect findings directly to strategic and financial planning processes are better positioned to influence resourcing decisions than those that operate as a separate, siloed function.
What these trends mean for business leaders
Taken together, these trends point to a few practical conclusions for organizations that commission or rely on market research in 2026:
- Speed is now a baseline expectation, not a differentiator. AI-assisted research workflows have compressed typical turnaround times, which means slow research is a competitive disadvantage even when it remains methodologically sound.
- Data provenance requires active scrutiny. With survey fraud estimates as high as 40% for nonprobability samples and synthetic data use growing quickly, leaders should ask vendors directly how respondents are verified and whether synthetic or AI-augmented data was used at any stage.
- First-party data is a strategic asset, not just a compliance requirement. Organizations with structured customer data programs are better insulated from the uncertainty around third-party identifiers than those still dependent on external panels or ad-tech data.
- Human interpretation remains the differentiator. As execution tasks are increasingly automated, the value of a research function is shifting toward judgment, contextual interpretation, and the ability to connect findings to strategic decisions.
- Regional and industry-specific context matters more than global averages. Growth rates, adoption patterns, and regulatory conditions vary significantly by region and sector, which argues against relying on single global figures for planning purposes.
- How to build a stronger market research strategy in 2026
Organizations looking to strengthen their research function this year should consider a few concrete steps. First, audit current research vendors and internal teams on data quality practices specifically — not as a general due-diligence checkbox, but with direct questions about fraud detection, panel sourcing, and synthetic data disclosure. Second, invest in first-party and zero-party data infrastructure now, since it will only become more valuable as third-party alternatives remain unstable. Third, treat AI tools as an augmentation layer for execution tasks (transcription, initial analysis, drafting) while preserving dedicated time and budget for human interpretation and strategic synthesis. Fourth, favor research and market data providers who are transparent about methodology and sourcing over those offering the lowest price or fastest turnaround with limited documentation.
Finally, pair primary research with structured secondary data on industry size, growth, and benchmarks. Teams starting this process can review Kentley Insights’ free market research reports as a starting point for understanding how industry-level data is structured before committing budget to a full research program.
Conclusion
The market research statistics 2026 data tells a consistent story: an industry growing steadily by revenue, being reorganized by AI and automation at a pace few other professional services sectors have matched, and simultaneously confronting a genuine crisis of data authenticity. None of these forces cancel each other out. Generative AI adoption and the rise of synthetic data are real productivity gains, but they raise the bar for verification and human oversight rather than lowering it. Privacy regulation continues to evolve unevenly rather than converging on a single global standard, and demand for industry-specific, regionally grounded intelligence continues to outpace demand for generic market overviews. Business leaders who treat 2026 as a year to strengthen data governance, invest in interpretive skill, and demand transparency from research partners will be better positioned than those who simply adopt faster tools without addressing the underlying trust problem.
Sources and methodology
Statistics in this article were drawn from named, publicly available sources, including: ESOMAR’s Global Market Research 2025 report and related Research World commentary (the primary industry association benchmark for insights-industry sizing); market sizing data from The Business Research Company; Greenbook’s 2025 GRIT Insights Practice Report and related industry commentary; Qualtrics-sourced synthetic data adoption figures as reported by Greenbook; NORC at the University of Chicago’s analysis of Insights Association survey-fraud findings; DataCamp’s 2026 State of Data & AI Literacy Report (conducted with YouGov); McKinsey’s 2025 State of AI survey; and privacy and consent data compiled by Searchlab, citing IAPP and Google’s Privacy Sandbox documentation. Each statistic is attributed to its source with a reporting year and geographic scope in the fact-check table below. Where 2026-specific data was not yet available at the time of writing, the most recent verified figure is used and its reporting year is stated explicitly. Figures describing forecasts (such as 2030 market-size projections) are clearly distinguished from historical or current-year figures throughout the article. This article does not include statistics from sources without a traceable original publication or named methodology.
Frequently asked questions
How big is the global market research industry in 2026?
Estimates vary by definition. ESOMAR’s broader “insights industry” measure (market research, research software, and reporting combined) surpassed $150 billion in 2024 and was projected to exceed $160 billion by the end of 2025. A narrower market-research-services-only estimate from The Business Research Company puts 2026 revenue at $96.77 billion, growing to a projected $116.02 billion by 2030.
Is AI replacing market researchers?
Available evidence does not support that conclusion. Industry data shows AI is heavily used for execution tasks like transcription, data analysis, and report drafting, but industry analysts consistently describe the shift as changing what researchers do — moving toward interpretation, strategy, and judgment — rather than eliminating the role.
How common is survey fraud in market research?
Estimates vary by source and methodology, but they are significant. An Insights Association analysis presented in January 2026 estimated that around 40% of nonprobability online survey interviews in 2025 were likely fraudulent. Other industry and academic sources report usable-response rates as low as 10–30% on some panels, depending on incentive structure and detection methods.
What is synthetic data in market research?
Synthetic data refers to AI-generated responses designed to simulate how real respondents might answer a survey, often based on patterns from prior human data. A 2025 Qualtrics study found 62% of market researchers had used synthetic data in the prior six months, typically for early-stage hypothesis testing rather than as a full substitute for validated primary research.
Are third-party cookies actually going away?
Not on the timeline the industry originally expected. Google confirmed in April 2025 that it will not force third-party cookie deprecation in Chrome, though Safari, Firefox, and Brave continue to block them by default. This split makes cookie-based tracking less reliable overall, reinforcing the industry shift toward first-party and zero-party data even though third-party cookies have not fully disappeared.
What skills will market researchers need most in 2026?
Industry analysis points toward interpretive and communication skills — translating findings into decisions, contextual judgment, and data storytelling — as the areas of greatest differentiation, since AI tools increasingly handle routine data collection, cleaning, and initial analysis.