Wondering vs User Intuition: Research Options and Pricing

The research company’s profile describes its focus as AI-led customer research. On October 1, 2026, visiting its former website led to a domain-sale listing. The pricing comparison below labels the historical offer so the distinction is clear when planning new research.

For a new study, User Intuition’s user research workflow lets a team define a decision, select the relevant audience and launch an adaptive interview study. Researchers can inspect the findings and source accounts, assess which explanations have support and direct the next study toward the question most likely to change the decision.

A useful discovery interview goes beyond collecting requests. It can examine the situation in which a problem occurs, the alternatives a person has tried and the trade-offs that shape their behavior. Following the answer can reveal an unexpected constraint that a predefined response list would not capture.

User Intuition’s laddering approach is informed by Reynolds and Gutman’s laddering method, which connects product attributes to consequences and personal values. For example, a customer may ask for a simpler dashboard because they use it during a time-sensitive meeting and worry about losing credibility when information is hard to find. Understanding that chain helps the team decide which product change would address the actual problem and which change would merely alter the interface.

This is useful when a product or client decision is still open and the team needs firsthand accounts quickly. A researcher can give several segments the same learning goals while allowing the moderator to explore the particular experiences and motivations behind each person’s answers.

User Intuition supports BYOP or a 4M+ vetted panel. Bring actual customers for a question about your product, or recruit people with the relevant category experience for a broader opportunity study. Specialized audiences and demanding screeners can extend recruitment and require a quote. A large participant count is useful when it adds perspectives that matter to the research purpose.

Wondering’s provider-supplied historical pricing listing dates its pricing information to October 2024. It records a free tier, an Explore subscription with two studies per month and a custom Scale tier. Those figures describe the earlier offer; the former research website is listed for sale.

With User Intuition, standard panel recruitment adds $30 per participant. Incentives for your own participants are separate, and specialist audiences are recruited to order. The team can budget for the initial question, review the resulting evidence and decide whether the next dollar is better spent analyzing existing interviews or collecting a missing customer perspective.

Automatic quality billing helps a team pay for substantive accounts of the experience it needs to understand. A participant may complete a conversation without explaining the decision in enough detail. User Intuition checks the answers against the learning goals before charging, reducing both wasted study budget and administrative work.

The next layer is evidence assessment across the study. Researchers can examine which accounts support a conclusion, whether relevant situations are represented and which exceptions qualify the recommendation. Together, interview checks and study-level review help a team avoid treating a plausible summary as sufficient support for a consequential decision.

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 decision, User Intuition can assess whether a repeated complaint has a consistent cause across customer situations. It can recommend analyzing an existing interview in greater detail or recruiting a missing segment, so the next study investigates an explanation that could change the proposed improvement.

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.

For example, the assistant can review accounts of an onboarding problem and identify a group whose experience is missing. It can draft a focused follow-up around the competing explanations, keeping the research question tied to the decision the team needs to make.

With User Intuition’s agent workflow, the researcher reviews the guide and recruitment plan before approving the budget. New findings return with source accounts that the team can examine and use in an evidence review. The workflow connects planning, execution and learning without requiring the researcher to rebuild the context at every step.

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 teams developing a new study, this helps turn a broad idea into specific questions about the circumstances in which a customer would adopt, reject or switch to an alternative.