Userology vs User Intuition: AI Interview Costs and Depth

Userology’s research platform uses screen context to inform follow-up questions during product tasks. That can help a design team connect a participant’s explanation with the action they took in an interface.

User Intuition’s user research workflow explores the experiences that shape adoption, preference and switching. A team can investigate why a task matters, which alternatives customers use and what would make a proposed improvement valuable. Evidence assessment then helps decide whether the resulting explanation is well supported or needs further investigation.

A screen-aware moderator can ask about a hesitation or an unexpected action while someone uses a product. A motivation study can extend beyond that interaction to investigate the work, habits or pressures that make the task important. The research question determines which context matters most.

User Intuition’s laddering approach is informed by Reynolds and Gutman’s laddering method, which connects product attributes to consequences and personal values. A participant may ask for faster reporting, explain that delays interrupt a client meeting, and reveal that confidence in front of stakeholders drives the preference. That connection helps a team choose an improvement with a clear customer consequence, rather than treating every requested feature as equally valuable.

For an international study, a consistent learning goal lets the team compare the consequences of similar experiences in different settings. The guide can investigate how local habits, constraints or buying processes affect the outcome, while participants speak in their preferred supported language.

User Intuition supports BYOP or a 4M+ vetted panel. Bring actual customers when a study concerns your product’s specific experience, or recruit relevant prospective users for a broader adoption question. Specialized profiles and screening requirements affect recruitment time and cost. Results arrive as conversations finish, so the team can begin assessing the evidence during fieldwork.

Userology’s pricing terms distinguish session charges from the fee to unlock the platform. The five-session usage component is $100, with access and applicable recruitment costs in addition. Research+ provides separately scoped support and capabilities.

For User Intuition, five quality voice interviews with standard panel recruitment total $300. Recruitment adds $30 per standard participant; Incentives for your own participants are separate, and specialist audiences are recruited to order. Researchers can start with a focused question, review what the interviews establish and purchase further work when a consequential gap remains.

User Intuition evaluates whether each conversation has enough relevant substance to address the learning goals. A plausible participant profile or a completed session does not by itself establish that the interview has produced useful depth. Automatic checks protect the study budget without creating a manual refund task for the researcher.

Userology uses AI-supported screening and participant vetting. User Intuition combines audience selection with interview-level billing protection: failed Length, Depth or Coverage checks mean no charge. The evidence review then considers how the accounts, taken together, support the proposed finding.

User Intuition’s evidence review is informed by Malterud et al.’s information power framework and Hennink et al.’s meaning-saturation research. These approaches connect sample adequacy to relevant information and distinguish identifying a theme from understanding it in depth. For product research, User Intuition can assess whether a requested improvement addresses a recurring consequence in users’ work. It can recommend analyzing a detailed existing account or recruiting a missing workflow segment, directing the next study toward an assumption that could change the product decision.

A shopper study illustrates how this evidence review works. We interviewed 43 Walmart shoppers to understand purchase decisions, trip friction and return intent. Thirty-six said they would shop at Walmart again. User Intuition recommends recontacting shoppers after their next comparable trip and comparing those who returned with those who chose another store. The follow-up tests which experiences turn stated intent into a store choice. Explore the Walmart study, two recommended follow-ups and sample presentation.

For example, an assistant can identify conflicting explanations for an onboarding problem in past studies. It can propose a focused brief for the users whose experience would help resolve the disagreement, rather than asking the team to recreate the research context from scratch.

With User Intuition’s agent workflow, the team reviews the guide, audience and cost before approving spending. The assistant returns findings and the underlying accounts for assessment. This keeps the next study connected to the evidence gap that motivated it and makes the research process easier to repeat.

User Intuition builds synthetic participants from real, opted-in respondents, using their individual traits, habits and behaviors. It repeatedly compares synthetic responses with the same respondents’ real answers to calibrate the models. That grounding preserves differences in experience and priorities that generic LLM feedback flattens.

This design is informed by Park et al. (2024). Their simulations of 1,052 people reproduced General Social Survey answers 85% as accurately as those people reproduced their own answers two weeks later. The study informs User Intuition’s emphasis on individual grounding and repeated calibration. For product teams, this can reveal why the same proposed feature would matter differently to a frequent user, an occasional user and a person relying on an alternative workflow.