Decision Analyst vs User Intuition: Research and Cost

Decision Analyst’s qualitative services include depth interviews, ethnography, online communities and group methods. Its specialists can select and adapt a method around the research problem. That service is useful when the engagement needs expert design and interpretation across several kinds of evidence.

User Intuition’s shopper research workflow suits a team that owns the brief and needs direct access to customer conversations. A retailer might investigate why shoppers leave a category without buying, compare the constraints they describe and identify which change to the assortment, message or experience deserves another test.

Decision Analyst’s online qualitative methods include live video interviews and extended discussions with follow-up questioning. User Intuition’s moderator can trace a recent shopping decision from the first answer into the alternatives, constraints and consequences that explain it.

User Intuition’s laddering approach is informed by Reynolds and Gutman’s laddering method, which connects product attributes to consequences and personal values. For shopper research, “I could not find what I wanted” may reflect navigation, uncertainty about the product or concern about making the wrong choice for someone else. Following those consequences helps a team choose between improving findability, clarifying the offer or providing more convincing reassurance.

Decision Analyst’s Big Qual combines interactive questions, exercises and real-time results for larger groups. That differs from a series of individual depth interviews, where each participant can follow their own explanation. The selected method and audience determine the overall research schedule.

User Intuition supports your own participants and a 4M+ vetted panel, with interviews in 80+ languages. Findings arrive as quality conversations finish. Research teams can compare experiences across relevant shopper groups while preserving the detail behind each person’s choice, then prioritize the difference that should guide the next study.

User Intuition charges $30 per quality voice interview. Incentives for your own participants are separate, and specialist audiences are recruited to order. Teams can begin with a defined learning goal and fund a follow-up when the evidence identifies a specific gap that matters to the decision.

Decision Analyst’s engagement can include researcher time, methodological design, fieldwork and analysis across multiple methods. User Intuition’s price supports a team-operated interview workflow. This distinction helps a buyer budget the work it needs: a managed research program, a focused investigation or interviews that complement a larger study already underway.

A shopper interview should explain an experience, rather than only repeat a preference. User Intuition checks that the conversation develops substantive answers and addresses the learning goals before applying the platform fee. This helps a small research budget produce evidence that can inform a specific retail or product action.

Decision Analyst also invests in participant quality, including screened qualitative panelists and moderator expertise. User Intuition’s billing rule makes its own commitment explicit: failed interviews incur no charge or refund-request work. Evidence review then examines what the accepted conversations support and whether another audience or a competing explanation needs investigation.

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 retail decision, User Intuition applies these principles to examine whether an explanation fits the occasions and constraints represented in the study. It can recommend deeper analysis of existing accounts or interviews with shoppers whose different choices could change the recommendation.

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.

A category team might ask why shoppers notice a new shelf message but continue choosing the familiar product. An assistant can examine prior studies and prepare interviews that explore what the message means, whether the promise is credible and what would justify switching. Your team reviews the audience and approves the spend.

User Intuition’s agentic research workflow then returns findings with source accounts. A researcher can compare explanations, assess which one supports the proposed action and prepare a targeted follow-up. The result is a shorter path from an unanswered shopper question to evidence the team can use.

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 a shopper team, the benefit is a clearer set of questions about the choices people make in context, ready to investigate through focused interviews.