Suzy vs User Intuition: Interview Depth, Speed and Cost

A research team can use User Intuition to investigate why a concept fails with a particular audience, what caused customers to switch or which experience drives repeat purchase. Setup takes five minutes, and your team can use its own participants or recruit through the panel.

Suzy’s Insights workspace combines research sources and lets teams query evidence across projects. User Intuition connects source-linked findings to an assessment of what each learning goal still needs, helping the team decide which interviews or analysis to commission next.

User Intuition’s AI moderator uses 5–7 levels of adaptive laddering, informed by Reynolds and Gutman’s laddering method (1988). Laddering connects a product attribute to its practical consequences and the personal values behind a decision. If a shopper describes packaging as “premium,” the useful finding is what signals that impression and why it changes the purchase. Laddering can uncover that readable ingredients reduce uncertainty about serving the product to a family, giving the team a concrete design priority. See how User Intuition applies laddering.

Suzy’s qualitative approach combines AI conversations with expert design and interpretation, including analysis of explicit and underlying meaning. User Intuition lets your team direct that investigation itself and follow emerging explanations into a targeted next study.

With User Intuition, research teams can invite participants immediately after reviewing the brief and budget. Concurrent interviewing avoids moderator scheduling, while findings arrive as quality sessions finish. This supports short client deadlines and repeated learning between larger research milestones.

For Suzy, the complete timeline follows the research product, audience and any expert design or reporting included in the engagement. User Intuition’s published starting workflow covers sign-up, study preparation and findings; a fieldwork estimate alone covers a narrower part of a project.

User Intuition charges $30 per quality voice interview for moderation and analysis. Standard panel recruitment adds $30 per participant. Incentives you arrange for your own participants are separate; specialist audiences require a recruiting quote. The published pricing lets research teams and agencies budget a focused study before committing to a broader program.

Vendr’s Suzy transaction data reports a median annual contract of $99,000 and an observed range of $35,795 to $183,555. That is a buyer benchmark for contracted software, with the final scope determining the price. User Intuition’s $150 starting study provides a smaller commitment for an individual qualitative question, and lets an agency price research into each client project.

User Intuition checks whether the conversation lasted long enough to address the brief, explored the participant’s experience in depth and covered the research topics. This ties the bill to the substance of the interview, giving your team a clearer basis for accepting work and planning the next study.

Suzy’s qualitative team describes expert review and findings traceable to source conversations. User Intuition makes interview quality part of automatic billing: sessions must meet Length, Depth and Coverage requirements before they are charged. Your team can inspect the underlying interview when deciding how much weight a finding deserves.

User Intuition assesses the relevant experience, depth, variation and competing explanations in the interviews. For a concept study, the review examines whether preference is explained by a meaningful customer experience, whether contrasting reactions are represented and whether the proposed change addresses the reason people gave. A repeated adjective without a concrete account may call for deeper exploration. Recommendations prioritize the gaps with the greatest consequence for the research purpose, so the next step addresses the decision your team must make.

User Intuition’s evidence review is informed by Malterud et al.’s information power framework, which relates sample adequacy to the relevant information interviews provide, and Hennink et al.’s saturation research, which distinguishes identifying a theme from understanding its meaning. User Intuition applies these principles to decide whether a gap calls for deeper analysis of existing interviews or a targeted study with people whose experiences are missing.

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, but stated intent leaves the next store choice unresolved. User Intuition recommends recontacting shoppers after their next comparable trip to understand what distinguished those who returned from those who chose another store. Explore the Walmart study and sample presentation to see the findings and two recommended follow-up studies.

User Intuition’s agent research workflow connects planning and execution. For example, ask the assistant to investigate why occasional buyers reject a new format. It can retrieve relevant earlier interviews, prepare learning goals and audience criteria, and estimate recruiting costs. Your team reviews the plan and approves spending; the assistant can monitor progress and return findings linked to source interview moments.

Suzy’s Insights workspace supports querying and combining research sources. User Intuition’s MCP and API workflow also connects an identified gap to study preparation and approved launch.

User Intuition’s synthetic participants preserve individual experiences and behavioral differences that generic LLM feedback can flatten. For concept work, the useful variation may be between frequent users, occasional buyers and people who rejected the category. User Intuition carries those distinct histories into exploration, helping the researcher sharpen language and prioritize the reactions that warrant a follow-up interview. Calibration uses the differences between modeled and real responses to refine the representation of each person.

This design is informed by Park et al. (2024). Using interviews with 1,052 people, the researchers built simulations that reproduced General Social Survey answers 85% as accurately as those people reproduced their own answers two weeks later. The useful principle is individual grounding paired with measurement against the person represented. See how User Intuition grounds and calibrates synthetic participants.

User Intuition’s preregistered benchmark on 53 consumer panelists found that conditioning on a person’s own interview closed 81% of the fidelity gap, against 19% for demographic profiles alone, in within-domain response prediction; see how accurate synthetic users are.