DiligenceSquared vs User Intuition: Customer Diligence and Cost

User Intuition’s commercial due diligence workflow helps your team investigate retention, switching, expansion and purchase criteria directly. Set the hypotheses, review the interview guide and approve the recruiting budget. Your team can change the next study as new evidence changes the investment question.

DiligenceSquared’s consulting model includes a senior project lead, AI-led research and consultant review. Its platform links findings to interviews or datasets. That service is valuable when the team wants an external adviser to own the broader diligence analysis and deliverable.

DiligenceSquared describes a six-step workflow. A project lead with consulting experience scopes the research after an initial call. AI agents gather evidence from interviews, datasets and web research. AI and consultants validate the data, AI analyzes it, consultants review and synthesize, and the client receives an interactive report in which each claim links to its source interview or dataset. It also lists voice of customer, market entry, competitive landscape and market sizing among its use cases. That breadth suits an investor who wants one external team to carry the whole commercial analysis.

User Intuition covers one layer of that work and gives your team control of it. The deal team writes the guide, chooses the audience, approves the budget and reads findings as interviews finish. Teams can pair the two: an adviser owns the broader analysis while the deal team runs the customer workstream and reads the evidence itself.

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. An answer such as “switching would be difficult” becomes useful when the interview establishes what would have to move, who would approve a change and what event could justify the effort. That separates routine inertia from a defensible reason to renew and gives the investment team more specific assumptions to test. See how User Intuition applies laddering.

DiligenceSquared’s published workflow includes research plans, interview questions and probes, data validation and synthesis by consultants. The choice concerns the operating model for that depth: a scoped advisory engagement or a self-serve interview program that your team controls.

Consider a renewal thesis. A customer who calls the product “sticky” has offered an adjective. User Intuition follows up on which workflows depend on it, what a replacement would cost in time and retraining, who would sponsor the change and what event could trigger a review. The answer separates a customer who would stay because the product is better from one who would stay because leaving is hard. Those two cases carry different implications for pricing power and churn risk, and the investment committee can read the quote and listen to the recording behind each one.

User Intuition lets the brief distinguish current customers, former customers and buyers who evaluated another option. Those experiences answer different questions: everyday value, reasons for leaving and competitive selection. Specify the relevant role, product exposure and decision responsibility before fieldwork.

For a narrow enterprise category, audience feasibility determines how fast the study can run. Use the target’s permitted customer contacts where available, or scope specialist recruiting through B2B participant recruitment. This makes the recruiting budget reflect the people the hypothesis requires.

When the seller supplies customer contacts, the sample reflects who the seller chose. Recruiting comparable buyers from the 4M+ vetted panel gives a second view. Screeners and hard quotas fix role, product exposure and company profile, the User Intuition team reviews a video identity interview with every panelist, and AI-answer screening runs on every session. Where permitted contacts exist, the same guide runs on both groups so the findings can be compared.

User Intuition can run interviews across customers and segments in parallel. Your team can review early findings while recruitment continues and decide whether the next question concerns retention, an expansion use case or a competing explanation. Specialist audiences are recruited to order, so timing follows incidence; reachable general-population audiences can move from sign-up to findings in under 24 hours.

DiligenceSquared combines AI-led data collection and analysis with consultant scoping, validation and synthesis. Its engagement timeline includes that advisory work. User Intuition is useful when your team already owns the diligence question and wants to launch a specific customer workstream directly.

A deal team that learns of a target can write the guide and launch the same day. Early findings arrive as interviews finish, so the team can tighten the next question before exclusivity narrows. Specialist audiences are recruited to order, so timing follows incidence; general-population panel studies return 200+ panel interviews in 24 hours.

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.

For a larger workstream, 50 User Intuition quality voice interviews cost $1,500 in platform fees using your own participants. Standard panel recruiting takes that total to $3,000; specialist B2B targeting is quoted separately. DiligenceSquared scopes consulting engagements without a public universal rate. Match the audience and advisory responsibilities when setting the budget.

Because pricing is per interview, a firm can size the customer workstream to the deal. An early screen might use a few interviews with a hypothesis to test, and a target that enters exclusivity might justify a larger study with several segments. Each study is a separate, published budget line, and no subscription has to be renewed between deals.

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.

DiligenceSquared describes AI and consultant validation of evidence. Its scoped engagement terms govern deliverables and any replacement work. User Intuition’s automatic no-charge policy applies at the interview level, making the cost of collecting evidence explicit.

For an investment committee, the useful question is which interviews count. User Intuition excludes conversations that fail Length, Depth or Coverage from billing, and the evidence review then reads the accepted interviews against each learning goal. The committee sees a finding, the quotes behind it and a statement of what limits it.

User Intuition assesses the relevant experience, depth, variation and competing explanations in the interviews. For diligence, the review can separate evidence about current value from evidence about switching or future expansion. A positive experience with one department may leave the cross-department growth thesis unresolved. Your team can inspect the supporting quotes and focus the next study on buyers with the missing experience. 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 diligence, the practical question is whether the customer accounts explain a material retention or growth hypothesis across the relevant buying situations.

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 guide to presenting customer diligence to an investment committee shows how to map each investment assumption to the customer evidence that supports or weakens it. That mapping is the output a team needs from an evidence review: for each assumption, what the interviews show, what the evidence leaves open and which study would close the gap.

User Intuition’s agent research workflow connects planning and execution. For example, ask an assistant to test whether integration effort protects renewals. It can search earlier studies, draft questions for recent renewal and switching decisions, and estimate recruitment for the missing customer roles. Your team reviews the plan and approves spending; the assistant can monitor progress and return findings linked to source interview moments.

DiligenceSquared also provides MCP access to its research evidence. Its published workflow centers on consultant-led engagements; User Intuition connects an assistant to self-serve study preparation, approved launch and the findings that guide the next workstream.

User Intuition’s synthetic participants preserve individual experiences and behavioral differences that generic LLM feedback can flatten. A team investigating renewal risk can explore questions about habit, integration effort and the cost of change, then use that preparation to write sharper questions for recent renewers and switchers. 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.