User Interviews vs User Intuition: Recruitment and Study Costs

User Intuition’s AI moderator conducts the conversation, using 5–7 levels of laddering informed by Reynolds and Gutman’s original laddering research (1988). It explores experiences, consequences, and motivations, then keeps findings tied to source evidence. That reduces the separate moderation work a team must staff for a depth-interview study. The benefit is an explanation of which product attributes matter, what they change for the participant, and why that consequence influences a decision. See how User Intuition applies laddering.

User Interviews’ plans list recruiting, scheduling, incentive distribution, and AI insights and analysis. Its Research Hub adds participant operations and CRM capabilities.

For research teams and agencies with a strong human moderation practice, User Interviews can support an existing process. User Intuition is especially useful when interviewing capacity and evidence review need to expand without adding moderator hours for every session.

User Intuition lets research teams and agencies run multiple studies and audiences in parallel without an hourly interview cap. Start with five-minute self-serve setup and review findings as quality interviews finish. Specialist audiences are recruited to order.

With User Interviews, recruitment and coordination support the interview workflow your team chooses. A human-moderated study still relies on participant and moderator calendars, followed by analysis. User Intuition’s AI moderator lets eligible participants take the interview when they are available and produces findings as quality sessions finish.

User Intuition’s panel and own-participant options let a team begin with the people suited to the question. An existing recruiting relationship can remain useful: bring eligible participants into User Intuition for interviewing, while respecting consent and the recruiting provider’s terms.

At those published rates, User Interviews recruiting costs $245 for five consumer sessions or $490 for five B2B sessions, before incentives and any advanced-targeting charges. Annual plans change the rates. AI analysis is listed, so add only the execution or service costs your actual workflow requires.

For User Intuition, the platform fee is $30 per quality voice interview and standard recruiting adds $30 per person. BYOP incentives remain separate, and specialist audiences require a recruiting quote. Review User Intuition pricing.

User Intuition gives an agency a simple starting budget without a required subscription. Compare matched audience eligibility before presenting savings to a client; standard consumer recruiting and specialist B2B sourcing are different purchases.

User Intuition’s billing checks protect your budget from interviews that lack depth or coverage, even when the participant passes the screener. Someone can pass a screener and still provide rushed, shallow answers that do not address the research brief. User Intuition checks the completed conversation before billing, protecting the study budget without requiring researchers to pursue a refund for an interview that fails its quality checks.

User Interviews’ fraud policy describes participant reporting, investigation, and refunds where necessary. Its participant feedback process also captures rushed or shallow responses. These protections matter when comparing the effort required to resolve a quality problem.

User Intuition’s evidence review assesses relevant experience, depth, variation, and competing explanations. For each learning goal, its review shows what the evidence supports, what limits the conclusion, and what that means for the research decision. It turns the most consequential missing perspectives into a focused recruiting brief, helping your team find people whose experiences could change the recommendation.

User Intuition’s evidence review is informed by Malterud et al.’s information power framework, which emphasizes the relevant information interviews provide, and Hennink et al.’s saturation research, which distinguishes identifying a theme from understanding it in depth.

User Intuition applies these principles to assess whether existing interviews contain enough relevant detail to answer your research question. It can recommend deeper analysis when the evidence is already available, or targeted follow-up interviews when important experiences or explanations are missing.

For example, we interviewed 43 Walmart shoppers to understand how they made purchase decisions, where they encountered friction, and what would bring them back. Thirty-six said they would shop at Walmart again, with price, proximity, and familiarity featuring in their accounts. The next question is what drives their next store choice. User Intuition recommends recontacting shoppers after their next comparable trip to compare the experiences of those who returned with those who chose another store.

Explore the Walmart study and sample presentation, including the interviews, findings, and two recommended follow-up studies.

User Interviews’ participant operations can remain valuable when a team already maintains an opt-in audience. You can keep that recruiting process and use User Intuition to conduct the interviews and analyze the resulting evidence.

User Intuition’s agent workflow lets your assistant search earlier studies, prepare the brief, estimate platform costs and any additional recruitment, monitor approved fieldwork, and retrieve source-linked findings. The research question and the evidence stay connected as the work moves from planning to analysis.

For example, a team with a customer panel can ask its assistant to investigate an onboarding problem, draft a study for eligible panel members, and review the plan and cost before launch. Once participants complete interviews, the assistant can return findings with supporting quotes and propose questions for a follow-up. This allows the team to build on its established recruitment process while adding AI-moderated fieldwork and evidence review.

User Intuition’s synthetic participants help your team identify the experiences to prioritize in your next recruiting brief and sharpen the questions for those participants. The models start with real people who have opted in. Their research responses provide the basis for extracting individual traits, habits, and behaviors. User Intuition then compares synthetic responses with real responses from the same respondents and uses the differences to iteratively calibrate the models.

The purpose is to preserve the experiences and behavioral differences that generic LLM feedback can flatten. Accuracy is evaluated against actual respondent answers, so calibration has a human reference point throughout the process.

This approach is informed by Park et al. (2024), who built simulations from interviews with 1,052 people. On the General Social Survey, those simulations reproduced participants’ answers 85% as accurately as participants reproduced their own answers two weeks later. The study demonstrates the value of grounding simulations in individual interviews and measuring them against the people represented.