Clozd vs User Intuition: Win-Loss Research Costs and Depth

User Intuition’s win-loss research workflow starts with the decision you want to understand: why a buyer selected a competitor, which trade-off changed the shortlist or what blocked renewal. Your team can review the guide, invite the relevant buyers and begin interviewing without a mandatory sales or program-design engagement.

Clozd’s feedback program includes live human interviews and AI-assisted Flex interviews. It also presents Flow, an AI-led interview option, as beta and coming soon. Its program support and revenue-oriented analysis are valuable when a team wants help operating an ongoing feedback program.

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. If a lost buyer says the price was too high, follow-up questions can examine the alternative, the budget owner, the expected outcome and the risk of choosing an unfamiliar provider. That helps a team decide whether the response points to price, positioning, proof or targeting. See how User Intuition applies laddering.

Clozd Flex combines prerecorded questions with real-time AI clarifications. Its human-led interviews add an experienced interviewer’s judgment. User Intuition follows learning goals through a live AI conversation, connecting the buyer’s stated criteria with consequences and motivations.

User Intuition lets your team separate won, lost, stalled and renewed accounts rather than treat them as one audience. The brief can specify who participated in evaluation, who controlled budget and which alternatives were considered. That makes the resulting explanation more useful for sales, product and marketing.

Clozd offers CRM and workflow integrations that connect feedback to the revenue program. For User Intuition, invite the relevant people from your permitted contact list or scope specialist B2B recruitment for category buyers. Standard panel pricing does not imply that a specific lost prospect is available in a panel.

User Intuition lets buyers participate when they are available and runs the interviews in parallel. Your team can review findings as quality conversations finish, then refine a follow-up around the segment or objection that matters most. Participation from a specific group of recent buyers determines the final study timeline.

Clozd live interviews require coordination with an interviewer; Flex lets buyers respond on their own schedule and receive AI follow-ups. A focused User Intuition study can start directly, making it practical to investigate an emerging loss pattern between broader program reviews.

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.

Clozd uses a sales conversation to scope the program and interview mix. A human-led interview, a Flex interview and an ongoing managed program include different work. User Intuition gives a team with its own buyer list a defined platform cost for a focused study, while any incentive budget remains separate.

In its introduction to Flex Interviews, Clozd cited $1,500–$2,000 per traditional live interview and positioned Flex as a lower-cost option. That historical live-interview benchmark helps explain the budget difference; current Live and Flex packages are quoted to the program. User Intuition’s published $30 voice rate makes a smaller buyer study straightforward to budget.

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.

Clozd states that low-quality Flex responses do not count against the purchased interview allotment. User Intuition applies automatic Length, Depth and Coverage checks before billing. Both approaches protect research budgets; User Intuition makes its acceptance criteria explicit within the self-serve workflow.

User Intuition assesses the relevant experience, depth, variation and competing explanations in the interviews. A price objection from several lost deals may coexist with wins at the same price. User Intuition helps your team examine the circumstances behind both outcomes and identify the audience needed for a useful follow-up. The next study can focus on the unresolved decision mechanism instead of repeating the original loss survey. 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. For win-loss research, this directs attention to the missing buyer roles or deal circumstances that could change the explanation of an outcome.

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 whether security concerns explain recent enterprise losses. The assistant can find earlier interviews, prepare a study of buyers who raised security issues and estimate the recruiting budget. After approved launch, it can return the findings and quotes that distinguish a blocking requirement from a secondary objection. Your team reviews the plan and approves spending; the assistant can monitor progress and return findings linked to source interview moments.

Clozd’s MCP connection also brings buyer feedback into AI assistants. User Intuition connects research retrieval with study preparation and approved launch, making it possible to investigate the unanswered part of a question in the same workflow.

User Intuition’s synthetic participants preserve individual experiences and behavioral differences that generic LLM feedback can flatten. For a positioning question, the team can explore how perceived risk, implementation effort and internal incentives might interact. That preparation can make the real buyer interviews more specific about who influenced the decision and what evidence changed their mind. 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.