Research

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Explore interactive examples of our AI-moderated customer research. Each report includes executive summaries, quantified findings, and verbatim evidence.

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Researcher using User Intuition AI-moderated research platform
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Intelligence Report Live
80% Overwhelm
Trust
87%
AI Usage
92%
WTP $30+
75%
AI Insight

80% of consumers report feeling overwhelmed daily or weekly. 87% are open to giving AI deep access...

User Intuition
Benchmark
80%
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Original Research

First-party data from thousands of real customer conversations

A public library of original studies from interviews conducted on the User Intuition platform. Every report is first-party data — real consumers, real buyers — with full methodology, sample composition, and verbatim evidence published alongside the headline findings. Designed to be cited, audited, and pressure-tested.

How are these studies conducted?

Each study uses the same AI-moderated interview infrastructure customers run on the platform: voice or chat conversations 20-45 minutes long, laddered 5-7 levels deep into the why behind each response. Participants are recruited from User Intuition's 4M+ vetted panel, screened to study-specific criteria, and incentivized at market rates.

Why publish the full methodology?

Because business decisions get made on these numbers. Sample composition, laddering depth, and screener criteria shape what a study can and cannot claim. Every report includes the recruitment frame, the interview guide structure, and the analytical method — so a CMO, a research lead, or an analyst can audit whether the conclusion follows from the evidence.

How should I attribute findings if I cite them?

Headline statistics carry their sample size inline so they can be quoted cleanly ('N=10,247', 'N=117 voice interviews'). When referencing a finding, link to the underlying report page — it carries the full provenance — and cite as 'User Intuition Research, [Report Title], [Year].'

Cross-Study Findings

What the research library shows

Each number below comes from one of the published reports. Click through to see sample, methodology, and verbatim evidence.

Stated vs. actual loss-reason gap
44-pt

Across 10,247 post-decision buyer interviews, price was cited in 62.3% of lost deals but drove only 18.1% of decisions. The remaining 44 points trace to four real drivers, exposed in the win-loss study.

Of consumers feel overwhelmed daily or weekly
80%

From 117 AI-moderated voice interviews on agentic AI demand. The OpenClaw study quantifies the overwhelm crisis behind the viral hype.

Real participants vs. LLM 'participants'
117 vs. 90

The Synthetic Mirage study ran the identical interview guide with 117 humans and 90 LLM-generated personas across Claude, GPT-5.3, and Gemini. Frontier models could imitate the surface of qualitative research. They could not reproduce its findings.

Churned-founder deep dive (Lovable)
N=4

Sometimes the right N is small. Four churned founders of a fast-growing AI builder explain where the prototype-to-production gap broke their workflow — and what would bring them back.

Library

Browse the full research

Each report includes executive summary, methodology, sample composition, and verbatim quotes from participants.

Why Price Is Almost Never the Real Reason You Lost the Deal

B2B Win-Loss Research

10,247 post-decision buyer interviews reveal price is cited in 62.3% of lost deals but drives only 18.1% of decisions. Original research on the five real drivers behind 'lost on price.'

Sample: N=10,247

The Synthetic Mirage in Market Research

Original Research

We ran the same mental-load study with 117 real humans and 90 LLM-generated 'participants' across Claude, GPT, and Gemini. Frontier models could imitate qualitative research. They could not reproduce its variance.

Sample: N=117 real + 90 synthetic

Lovable: What Churned Founders Say About the Path from Prototype to Production

Churn Analysis

Four churned founders explain what drew them to Lovable, where the experience broke down on the path from prototype to production, and what would bring them back.

Sample: N=4

OpenClaw: Why It Went Viral and What Consumers Actually Want

Consumer Research

OpenClaw crossed 232K GitHub stars in weeks. 117 AI-moderated voice interviews reveal the overwhelm crisis behind the hype, what consumers actually want from agentic AI, and what they'd pay for it.

Sample: N=117
Methodology & Trust

How we conduct — and publish — every study

Transparency is what makes original research citable. Every report carries its full provenance so a quality buyer can audit the work, not just consume the conclusion.

How studies are conducted

  • Recruited from User Intuition's 4M+ vetted panel, screened to study-specific criteria
  • AI-moderated voice or chat interviews, 20-45 minutes per participant
  • Laddered 5-7 levels deep into the why behind each response
  • Participants incentivized at market rates — never coerced, never under-compensated
  • Sample sizes from N=4 deep churn dives to N=10,247 buyer interviews
  • Multilingual capability across 50+ languages where the study calls for it

What's published in every report

  • Recruitment frame and screener criteria
  • Interview guide structure and laddering pattern
  • Sample composition (geography, role, behavior, brand usage)
  • Headline statistics with sample size cited inline
  • Verbatim participant quotes as primary evidence
  • Methodology notes covering analytical approach and known limitations

Methodology validated across 30,000+ AI-moderated interviews on the User Intuition platform.

FAQ

Common questions

We publish original research where the finding is non-obvious, the sample is sufficient to support the claim, and the methodology holds up to outside scrutiny. Some studies are commissioned for our own product roadmap; others are run because we noticed a gap in public data. Either way, we don't publish until the headline finding is something we'd defend in front of a research review board.
Yes. Most studies in this library started as customer-funded research before being released publicly. If you want a study run with the same methodology — your own sample, your own interview guide, your own analysis — you can launch one self-serve from $200 per study, or work with us on a commissioned program for larger scope. The platform infrastructure is the same; only the audience and questions change.
Each report carries a publication date and, where applicable, a fielding window. AI-moderated research has a structural advantage on freshness — the entire field-to-publish loop is typically 7-21 days, versus 8-16 weeks for traditional qual programs. If you need to verify currency, the report page lists the exact dates fielded and any subsequent updates.
Not in the academic sense. These are industry research studies, not journal articles. Each report is reviewed internally before publication — sample composition checked, headline statistics audited against the underlying data, verbatim evidence verified — but they are not blind-reviewed by external academics. If you need that level of methodological rigor, the underlying data and interview transcripts are the primary source; we'll share them under NDA for credible academic or due-diligence requests.
Yes — that's why we publish them. Headline statistics are stated with sample sizes inline so they can be cited cleanly. If you're using a finding in a deck, an article, a book, or an analyst report, link back to the report page (it carries the full provenance) and cite as 'User Intuition Research, [Report Title], [Year]'. We track inbound citations and have flagged the report library for AI search engines as citable original research.
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