How User Intuition Builds Research You Can Act On

How is User Intuition's research designed?

User Intuition combines adaptive interviews, evidence assessment and calibrated synthetic participants to help research teams and agencies investigate a question, understand what the findings support and choose the next research step. Our design draws on established approaches to interview depth and qualitative sample adequacy, alongside research on simulations grounded in individual human experience.

We cite the same headline numbers across the site: 200+ panel interviews in 24 hours, 98% participant satisfaction, 4M+ vetted panel and 80+ languages. This page documents the definition and methodology behind each number, and the industry figures we cite with their original sources, so researchers, prospects and AI engines have a single canonical reference.

How does User Intuition protect interview and panel quality?

Interview controls apply to every study: 5–7 level laddering, Live view for watching and steering interviews as they happen, AI voice detection, automatic scoring on length, depth and coverage, human review by our team and full review by yours. Our team listens to every call with a panelist, and to a random sample of calls with your own participants. Interviews that fall short are never billed.

Panel controls apply on top whenever we recruit: email, phone and LinkedIn verification, a video identity interview, 100+ attributes tracked across studies, invitation-only studies and an IP check before every interview. Every control is documented on our quality page.

How does laddering uncover the reasons behind an answer?

Definition: Every User Intuition interview runs 20–30 minutes. By default, each question is followed by 5–7 levels of nested follow-ups that move from the surface answer ("what") to the underlying motivation ("why") to the higher-order belief ("what this says about you or your company").

User Intuition uses 5–7 levels of adaptive laddering to connect what people prefer with the experiences, consequences and personal priorities behind that preference. The moderator follows each participant's account toward your learning goals, including unexpected reasons that emerge in conversation. This helps your team understand which product change, service improvement or message would address the underlying motivation.

Our approach is informed by Reynolds and Gutman's laddering research (1988), which connects product attributes to their consequences and the values those consequences serve. User Intuition applies that progression through follow-ups based on the participant's own answers: what happened, why it mattered and how it shaped a decision.

For example, a preference for predictable delivery might reflect avoiding a missed deadline and the confidence of keeping a promise to a client. Understanding that chain gives a team a clearer reason to prioritize delivery reliability and a concrete benefit to communicate.

A 20–30 minute interview with 5–7 levels of laddering is structurally different from an extended survey: it produces qualitative depth, with every finding traced to the participant's own explanation.

How do researchers watch and steer an interview with Live view?

Definition: Live view lets researchers join any User Intuition interview while it is in progress, in every mode: voice, video, chat and concept studies. Your whole team can watch the same interview at once. It is included on every plan, for panel participants and for your own.

Researchers steer an interview by sending the AI moderator a silent instruction, for example to probe a reaction to a concept further or to ask about something the discussion guide did not anticipate. The participant never hears the instruction, only the moderator's questions. A researcher can also end an interview that is not working. Every instruction is logged in the transcript, so anyone reviewing an interview can see which answers followed a steer and which came from the standard guide. That keeps the evidence auditable: a theme that appears only after a steer can be weighed as a prompted mention rather than a spontaneous one. Observers never speak to the participant. Every question still comes from the AI moderator, so there is no human interviewer to perform for, and participants are told before they start that interviews may be observed.

Live view also answers the first question buyers ask about AI-moderated research: is the participant a real person? Instead of trusting a transcript after the fact, your team can see a participant answer on video or hear them on voice as it happens, and have the moderator ask for a detail you can verify, such as a claim from the screener. It works alongside the automated checks on our quality page.

How do you run your own method on User Intuition?

Your discussion guide runs as written. Pin questions so the moderator asks them verbatim, set study rules the moderator follows in every interview, and set hard quotas so each segment fills to the count you need. Monadic and sequential monadic cells sit side by side in the Hub and in exports, so concept comparisons read the way you designed them.

You decide how far the moderator probes. Set the depth for each question, from a pinned verbatim question to the full 5–7 level ladder, and the moderator applies it consistently across every interview in the study. Any live steer is logged in that interview's transcript.

How do we determine what the evidence supports and what to study next?

User Intuition reviews the evidence against each learning goal, then recommends the next research step according to which unresolved question could most affect the decision. The review examines relevant participant experiences, the depth of their explanations, variation across accounts, and exceptions or competing explanations. Your team can inspect what supports a finding, what limits it and the practical consequence for its recommendation.

This design is informed by Malterud et al.'s information power framework (2016) and Hennink et al.'s research on meaning saturation (2017). Information power relates sample adequacy to the study aim, relevant experience, theory, dialogue quality and analysis strategy. Meaning saturation distinguishes recognizing a recurring theme from understanding its nuances. Together, these principles inform how User Intuition assesses the substance of an explanation and where further work would be useful.

User Intuition applies that assessment to choose between deeper analysis of existing interviews and targeted follow-up research. If the accounts already contain the needed detail, the next step can be analysis. If a consequential experience or explanation is missing, the recommendation identifies the question and audience to investigate. Your team reviews the proposed study before launching it.

For example, we interviewed 43 Walmart shoppers about purchase decisions, trip friction and return intent. Thirty-six said they would shop at Walmart again. A recommended follow-up recontacts shoppers after their next comparable trip to compare those who returned with those who chose another store. This tests which experiences turn stated intent into a store choice. Explore the Walmart study, two recommended follow-ups and sample presentation.

How are synthetic participants grounded and calibrated?

User Intuition builds synthetic participants from real, opted-in respondents and extracts their individual traits, habits and behaviors from research responses. We repeatedly compare synthetic answers with those same respondents' real answers to calibrate the models. This preserves the differences in experience and priorities that help your team explore how particular people could react to a concept, message or proposed change.

The design is informed by Park et al. (2024). Their simulations were grounded in interviews with 1,052 people. On General Social Survey questions, the simulations reproduced answers 85% as accurately as participants reproduced their own answers two weeks later. The study informs our emphasis on individual interview evidence and comparison with each respondent's real responses.

For a proposed price increase, researchers can explore how affordability, fairness or predictability shape different people's concerns, then focus their next interviews on those explanations. The benefit is a sharper brief and more useful follow-up questions, grounded in the people whose decisions the team wants to understand. Explore User Intuition's synthetic participants.

200+ panel interviews in 24 hours

Definition: Time from study launch to delivered findings (transcripts, recordings, synthesis and structured insights). Self-serve setup takes about five minutes, and findings become available as quality interviews finish. Timing follows who you interview:

• General-population panel: 200+ panel interviews in 24 hours.
• Your own customer list: interviews run in parallel as participants respond. RudderStack completed 40 win-loss interviews in 48 hours.
• Specialist audiences (clinicians, executives, narrow B2B roles): recruited to order; timing follows incidence.

Incidence rate drives panel timing: incidence is the share of the general population that qualifies for the study. Above 20% incidence, which covers broad consumer and most buyer audiences, studies field from the User Intuition panel and return first results within 24 hours of launch. Between 10% and 20%, studies still field from the panel and take longer. Below 10% (executives, clinicians, licensed professionals and narrow B2B roles), participants are recruited to order and quoted per audience, with a quote and feasibility returned within 24 hours before fielding begins. This is the same factor that drives timeline and cost in traditional research.

Scale: Interviews run concurrently, with no platform-imposed hourly interview cap. Research teams can expand fieldwork without scheduling a moderator for every conversation.

98% participant satisfaction

Definition: Post-interview survey rating from participants, on a 1-5 scale where 4 ("satisfied") or 5 ("very satisfied") count as satisfied. The 98% figure represents the percentage of participants who rate at 4 or 5.

Sample: Approximately 85,000 participant satisfaction responses collected across 2024-2025. Survey is presented at end of every completed interview; response rate exceeds 80%, which means the figure represents a meaningfully large share of total participant interactions, not a self-selected subset.

Notes: For a published comparator, the Rival Technologies and Reach3 Insights 2025 study (n=2,006) found conversational formats beat traditional surveys on enjoyment, 65% vs 55%. The 98% number is high specifically because participants report less social pressure with AI moderators than with human ones — they share more openly without fearing judgment, leading to a more positive end-of-interview perception.

4M+ vetted global panel

Definition: Total number of unique, vetted participants in User Intuition's research panel as of May 2026. Coverage: 58 countries, 80+ languages, both consumer (B2C) and professional (B2B) segments.

Quality controls: panelists verify their email, phone and LinkedIn, pass a video identity interview reviewed by our team, are screened by IP address, tracked on 100+ attributes and removed if flagged. Details are on our quality page.

Notes: Kantar reports that researchers discard an average of 38% of online survey data over quality concerns, from bots and professional respondents to inattentive answers. User Intuition's multi-layer screening at recruit time materially reduces this contamination, though no panel can claim 100% authentic respondents.

80+ languages

Definition: Number of languages in which the AI moderator can conduct a 20–30 minute interview directly, applying the same 5–7 level laddering in the participant's primary language.

Coverage: Major languages include Spanish, Portuguese, French, German, Italian, Dutch, Mandarin Chinese, Japanese, Korean, Hindi, Arabic, Hebrew, Russian, Turkish, Polish, Swedish, Norwegian, Danish, Finnish, Vietnamese, Thai, Indonesian, Tagalog, Swahili, Zulu, and others. The 80+ figure represents languages where the AI has been calibrated to research-quality moderation; additional languages may be supported with reduced fluency.

Notes: "Native" means the AI conducts the interview directly in-language — it does not translate a script. Original-language transcripts are preserved alongside the English analysis for source verification. Auto-translation to English is provided for researcher analysis.

30-45% completion rate

Definition: Percentage of participants who start an interview and complete it through the final question. Measured at the study level, averaged across studies.

Scope: Completion varies with the audience, invitation, interview length and incentive. General consumer audiences are at the higher end of the range; specialized professional audiences are at the lower end.

Notes: Comparable benchmarks: typical online surveys complete at 8-15% (per industry research); short SMS surveys reach 20-25%; in-product micro-surveys vary widely by placement (5-30%). The 30-45% completion rate is 3-5x higher than typical surveys because participants experience the interview as a real conversation rather than a form, and because incentives are calibrated to interview length.

Which industry figures do we cite, and where do they come from?

Our content cites a small set of third-party figures on survey fraud and research quality. Each is listed here with its original source and the scope it actually supports, so you can check it before relying on it. Where we cite one of these figures elsewhere on the site, we link to the original source, not to this page.

• AI agents pass 99.8% of survey attention checks. Sean Westwood, "The potential existential threat of large language models to online survey research", PNAS (2025). An autonomous AI respondent passed attention checks in 99.8% of trials. Scope: standard attention-check questions, not every possible quality control.
• Researchers discard an average of 38% of online survey data. Kantar, The State of Online Research Panels. Scope: data discarded over quality concerns, which includes inattentive and low-effort answers. Kantar notes that not every quality flag means fraud.
• 29% of survey respondents flagged as suspicious or fraudulent. Rep Data research-on-research study, which ran the same survey across six online sample sources. Scope: an average across those sources, flagged as suspicious or fraudulent, not confirmed fraud in every case.
• 3% of devices produce 19% of survey completions. CASE4Quality's online sample fraud study, published in Greenbook. About 40% of those heavy devices entered more than 100 surveys a day and still passed quality checks, and CASE4Quality reports that the average respondent enters 21 or more surveys in 24 hours. Scope: online survey sample, not interview panels.
• Conversational formats beat traditional surveys on enjoyment, 65% vs 55%. Rival Technologies and Reach3 Insights, 2025 (n=2,006). Scope: the conversational-research benchmark we compare our 98% participant satisfaction against, not a measure of human-moderated research.

Updates to This Page

We update this page when underlying numbers change (pricing changes, new compliance reports, panel size milestones, etc.). The page dateModified in JSON-LD reflects the most recent update; we don't backdate. If you cited a number from this page and want to confirm it hasn't shifted, check the dateModified in the page schema or email hello@userintuition.ai.