Marvin vs User Intuition: AI Interviews and Research Cost

Marvin combines a repository with tools for collecting and interpreting customer accounts. Its AI interviewer can explore a discussion guide and work with product or prototype material. Those capabilities suit teams that want to keep research collection and organizational knowledge in the same workspace.

User Intuition’s user research workflow helps a team investigate a specific uncertainty. A product team might need to understand why a promising onboarding flow works for experienced users but loses first-time users. Interviews develop those contrasting experiences, and evidence review helps determine which explanation should guide the next change.

Marvin’s AI interviewer supports configurable guides and product or prototype links. User Intuition follows the participant’s account across a full interview, exploring concrete examples and exceptions so a surprising answer can inform the direction of the investigation rather than remain an isolated observation.

User Intuition’s laddering approach is informed by Reynolds and Gutman’s laddering method, which connects product attributes to consequences and personal values. For product research, “this feels complicated” may mean a customer cannot predict the next step, worries about making a mistake or cannot recover easily. Tracing the consequence helps the team choose between simplifying the task, clarifying expectations and improving error recovery.

Marvin lets teams share interview links with their own participants or use third-party recruiting. User Intuition supports BYOP and a 4M+ vetted panel, with interviews in 80+ languages. A team can choose the people whose actual experiences match the research purpose and receive findings as quality conversations complete.

Availability and screening requirements affect how quickly an audience completes a study. User Intuition’s concurrency makes it practical to compare several user groups without scheduling every interview sequentially. Each conversation can still explore the participant’s particular experience, helping researchers explain differences rather than reducing all participants to the same short answers.

Marvin’s pricing separates workspace plans from additional interview capacity. Its Free plan includes limited uploads, while paid tiers expand research and collaboration capabilities. A team commissioning new research should include participant access and the required AI interview allowance in its total budget.

User Intuition charges $30 per quality voice interview. Incentives for your own participants are separate, and specialist audiences are recruited to order. Teams can fund a study around a defined hypothesis and use evidence assessment to decide whether the next purchase should investigate a missing user experience or a competing explanation.

An interview about a product problem needs enough detail to connect a task, obstacle and outcome. User Intuition checks the conversation against the study’s learning goals before billing. That helps teams avoid paying interview fees for short reactions that cannot explain why a person abandoned a task or chose an alternative.

Marvin provides tools for tracing and analyzing research material. User Intuition makes automatic interview billing protection explicit and assesses the accepted evidence for the decision it needs to support. Researchers can inspect the source account, examine contrasting experiences and identify whether the explanation is developed enough to guide action.

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 a product team, User Intuition uses these principles to assess whether interviews explain the mechanism behind an obstacle. It can recommend deeper analysis of existing accounts or targeted interviews with people whose context could change the proposed solution.

For example, 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.

Marvin’s MCP tools include both reading research and writing workspace content. User Intuition’s agentic research workflow takes a customer question through study preparation and approved execution, with the plan and spend reviewed by the team.

For example, an assistant can prepare a study around a suspected onboarding barrier, identify the audience and estimate the cost. After approval, it can launch the study and retrieve the findings with supporting accounts. The researcher can use that evidence to decide which product change to test and what remains unanswered.

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 tends to flatten.

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 research, individual grounding helps a team explore how the same feature fits different routines and sharpen the questions that its next human interviews should answer.