dunnhumby vs User Intuition: Shopper Research and Cost

dunnhumby’s customer strategy and insights connect customer data with research and commercial decisions. Its retailer collaboration services help brands work with retailer-trusted information and category priorities. That context matters when a recommendation needs to fit a retailer relationship or a broader customer strategy.

User Intuition’s shopper research workflow investigates the question your team needs to resolve next. For example, a category team can explore why shoppers substitute another product when a preferred item is unavailable, which compromise they accept and what would bring them back. The study connects an observed choice to the circumstances behind it.

dunnhumby’s primary research examines motivations and barriers and can connect attitudes with behavioral data. User Intuition explores the shopper’s account through adaptive follow-ups: the trip mission, the alternatives on the shelf and the reason a substitution felt acceptable or risky.

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, an unavailable pack size may mean a wasted trip, a disrupted meal plan or doubt that the store can be relied on. Understanding the consequence helps a retailer decide whether availability, assortment, communication or the proposition itself needs attention. In its ALP case study, Turning Point Brands ran market-specific studies and found that merchandising, not only brand strength, was the primary growth lever; the next quarter it beat its plan by over 40%.

User Intuition supports a 4M+ vetted panel, your own participants and interviewing in 80+ languages. A team can explore the same hypothesis across trip missions, customer groups or markets while allowing each person to describe the experience in their own terms. Findings become available as quality interviews complete.

A study about a particular retailer, recent purchase or rare shopping behavior needs an appropriate screener and recruitment plan. Panel studies return 200+ interviews in 24 hours; narrow shopper segments are recruited to order. The benefit of concurrent interviews is that the team can expand the investigation without scheduling one moderator for every conversation, while retaining depth in each account.

dunnhumby’s retailer collaboration programs combine shared retail information and commercial workflows. That purchase differs from funding a small set of new shopper interviews. A team should budget for the insight it needs, including data access or specialist retail support when those are part of the decision.

User Intuition charges $30 per quality voice interview. Incentives for your own participants are separate, and specialist audiences are recruited to order. A team can start with a focused hypothesis and expand the study when evidence review identifies a missing shopper experience that could change the recommendation.

For a retail decision, a participant needs to explain the actual shopping context, alternatives and trade-off. A short statement such as “the other store is cheaper” may leave the reason unresolved. User Intuition checks the conversation against the learning goals before billing for the interview.

dunnhumby combines data science and primary research expertise to support retail decisions. User Intuition’s automatic quality billing protects the interview budget, while its evidence review examines what the accepted accounts support. The team can then separate a clear explanation from a theme that needs more detail before changing the assortment or proposition.

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 uses these principles to assess whether a pattern explains a consequential shopper behavior. It can recommend revisiting accounts for a competing explanation or interviewing shoppers with a different trip mission when that experience is 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. 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.

dunnhumby’s agentic commerce work helps retailers use customer and product information in AI shopping experiences. User Intuition’s agentic research workflow supports the investigation behind a commercial decision, from the research brief to the returned accounts.

For example, an assistant can prepare interviews around the assumption that an assortment change will keep a shopping trip in the store. The team reviews the audience and approves spending. The findings can reveal which substitutions are acceptable and which send the shopper elsewhere, informing both the decision and its follow-up research.

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 retail teams, individual grounding helps distinguish an appealing general proposition from one that fits a particular shopper’s habits, making the next interview brief more useful.