Kantar vs User Intuition: Research Depth, Speed and Cost

For research teams and agencies, User Intuition makes it practical to investigate a question between larger client engagements. Define the decision, prepare a guide, choose an audience and approve the cost before launch. Your team can then change the next study as the evidence develops.

Kantar Marketplace offers self-serve or expert-supported work, with pay-as-you-go and committed purchasing options. Kantar’s broader services add specialist design and interpretation. Match the specific service to the question: a benchmarked ad test and an exploratory interview program provide different kinds of evidence.

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. A claim that a brand “feels reliable” becomes more useful when the participant describes what happened, what depended on the purchase and why a failure would matter. That helps researchers translate brand language into specific product or service priorities. See how User Intuition applies laddering.

Kantar Converser pairs AI conversations with qualitative expertise and cultural interpretation. User Intuition connects its laddered interviews to an evidence review for each learning goal, giving the team a practical way to decide which explanation to investigate next.

User Intuition lets your team launch concurrent interviews across audiences without scheduling each session around a moderator. A panel study returns 200+ interviews in 24 hours, and a study on your own list runs in parallel as participants respond. That supports recurring questions about shoppers, positioning or product decisions rather than waiting to combine them into a larger project.

Kantar’s AI-enabled interviews and automated Marketplace studies also support faster research than a fully manual process. Their timelines depend on the selected service, audience and expert work. User Intuition’s direct starting path makes the setup and budget explicit before fieldwork.

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.

Kantar Marketplace also offers pay-as-you-go purchases, alongside commitments for larger programs. Kantar pricing varies across automated tests, expert-supported interviews and syndicated or benchmarked products. User Intuition’s published per-interview rate lets your team cost the qualitative workstream before committing to a broader research package.

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 makes the bill depend on the substance of the interview, giving your team a clearer basis for accepting work and planning the next study.

Kantar describes audience quality and anti-fraud controls across its research products. Their deliverable and billing terms depend on the selected service. User Intuition makes automatic interview acceptance part of its pricing: a session must pass Length, Depth and Coverage checks before it is charged.

User Intuition’s consumer interviews can explore why a proposition resonates, which situation makes a feature valuable or what caused a customer to choose an alternative. These explanations help a team generate a better hypothesis and decide which change deserves testing.

Kantar Marketplace provides benchmarked research methods and quantitative analysis across brand, creative and innovation work. An agency can use that measurement to identify a problem, then use User Intuition to examine the concrete experiences behind it. The deliverable should make clear whether it is a benchmark score, an interview finding or a proposed next test.

User Intuition assesses the relevant experience, depth, variation and competing explanations in the interviews. For a brand or shopper study, the review can distinguish a familiar reason such as price from the circumstances that make it decisive. It also looks for exceptions that could change the recommendation, such as customers who buy despite a higher price. Those differences shape the next audience and guide. 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 an assistant why shoppers switch after a packaging change. It can search previous interviews, prepare a brief for recent switchers and estimate recruiting costs. The findings can then inform the recommendation and the next unresolved question. Your team reviews the plan and approves spending; the assistant can monitor progress and return findings linked to source interview moments.

For Kantar work, agent access and reuse depend on the particular product and licence. User Intuition provides a documented route from research retrieval to study preparation and approved launch, so your team can act on the gap it identifies.

User Intuition’s synthetic participants preserve individual experiences and behavioral differences that generic LLM feedback can flatten. For a brand brief, the team can explore how habits, perceived risk and purchase context affect reactions to a proposition. This preparation helps focus the human interviews on the experiences most likely to change the recommendation. 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.

Kantar also develops AI and synthetic-data methods grounded in its research assets. For User Intuition, the specific design is person-level grounding and iterative comparison with the real respondents represented.