Dynata vs User Intuition: Survey Sample and AI Interviews

Dynata supports research teams that already have a survey or research operation and need audience access, fieldwork tools or services. Its offerings extend beyond participant recruitment to survey delivery and AI-assisted qualitative probing. A researcher can select the components needed for an existing study process.

User Intuition’s consumer research workflow combines the brief, audience, adaptive conversation, analysis and next-step assessment. A team can explore why customers adopt a product, what they find difficult and which competing explanation could change the recommendation, without assembling separate recruiting, interviewing and analysis tools.

Dynata’s Quali-Quant AI uses SmartProbe to ask contextual follow-ups within survey research. User Intuition develops the participant’s account across the interview, following concrete examples, unexpected details and counterexamples that help explain the decision rather than only expanding one survey response.

User Intuition’s laddering approach is informed by Reynolds and Gutman’s laddering method, which connects product attributes to consequences and personal values. For customer research, saying a service is convenient may reflect a saved task, a reduced burden on family members or confidence that a deadline will be met. Understanding that consequence helps teams decide which experience to improve and how to describe the benefit accurately.

Dynata’s self-serve sample workflow includes audience selection, feasibility, pricing, payment and launch. It is useful when a team has its research instrument ready and needs sample access. User Intuition supports a 4M+ vetted panel, BYOP and interviews in 80+ languages within its interview-study workflow.

User Intuition fields 200+ panel interviews in 24 hours from its 4M+ vetted panel. Already buy sample from Dynata? Route it into User Intuition interviews through a custom link. User Intuition findings arrive as quality conversations complete, with no need to schedule one moderator per participant. Teams can compare experiences across segments while letting each interview explore the details relevant to that person and the learning goals.

Dynata’s self-serve sample price depends on audience and study requirements. Buying respondents for an existing survey is a different unit from buying an interview study with moderation and findings. Research teams should budget for the complete workflow they intend to use, including their own instrument and analysis where those are separate.

User Intuition charges $30 per quality voice interview. Incentives for your own participants are separate, and specialist audiences are recruited to order. Researchers can start with a focused investigation and fund additional work when evidence review identifies a missing experience or competing explanation that could change the decision.

Dynata’s QualityScore uses behavioral and other signals to detect poor responses, and it describes removing and replacing low-quality respondents during fieldwork. User Intuition’s billing rule adds an explicit check of interview Length, Depth and Coverage before the platform charges for the conversation.

A screened respondent and a completed session are starting points. User Intuition examines whether the answers provide substantive accounts against the brief, then assesses the accepted evidence for gaps and exceptions. That connects participant eligibility, conversation quality and the strength of the explanation the team plans to act on.

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 customer research, User Intuition uses these principles to assess whether a theme explains the behavior that matters to the decision. It can recommend deeper analysis of existing accounts or interviews with an underrepresented group whose experience could change the conclusion.

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.

Dynata’s developer platform exposes actions for integrating audience and survey services into a research operation. User Intuition’s agentic research workflow lets a researcher commission a new interview investigation through a compatible assistant, with the plan and budget reviewed before launch.

For example, the assistant can identify that prior feedback names a problem without explaining when it changes a buying decision. It can prepare a study of contrasting experiences, then retrieve the resulting findings and supporting quotes. Evidence review helps prioritize a follow-up that could change the recommendation rather than adding interviews without a clear purpose.

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 research planning, individual grounding helps a team formulate more specific questions and identify contrasting experiences to explore, making subsequent interviews more useful to the decision.