AlphaSense vs User Intuition: Market and Customer Research

A diligence team may need earnings commentary, analyst perspectives and interviews with industry specialists. AlphaSense brings those activities into a common research environment. Its AI-led expert-call workflow includes a project brief, screening questions, expert sourcing and post-call analysis.

User Intuition fits a different starting point: interview customers who switched suppliers, rejected a concept or struggled with onboarding. Bring those participants or recruit from the 4M+ vetted panel, then use the findings to decide what to change. Research teams and agencies can run one study for a client or maintain a recurring buyer-research 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. For example, “the product is expensive” can lead to the implementation effort, uncertainty about adoption and personal accountability that make the investment feel risky. That gives a team a more specific response than lowering the price. See how User Intuition applies laddering.

AlphaSense supports AI-led and researcher-led expert calls, and its Voice of Customer interviews include current and churned B2B customers. Both platforms can explore customer decisions. User Intuition’s self-serve workflow lets your team define the audience and brief directly, then use evidence assessment to decide which customer experience to investigate next.

With User Intuition, define the decision, select the audience and launch without a mandatory sales call. Interviews can happen at the same time across participants, so adding customers does not require adding moderator calendars. Recruiting a narrow specialist audience can take longer than inviting an available customer list.

AlphaSense can answer questions from its existing library without commissioning a new interview. For a custom expert-call project, the path includes defining the expert profile, sourcing suitable people and obtaining their participation. Those are separate timelines from searching an existing transcript.

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.

AlphaSense sells annual per-seat and enterprise subscriptions. Its quote depends on the research package and access required. A team buying a market-content library is funding an ongoing research environment; User Intuition lets a team budget a specific set of customer interviews without that annual commitment.

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.

AlphaSense expert-call terms belong to the sourced expert project and agreement. User Intuition makes the acceptance rule explicit at the interview level: automatic quality checks before charging.

User Intuition assesses the relevance, depth and variation of customer accounts for each learning goal. For a switching-cost hypothesis, that includes whether the evidence comes only from retained customers, whether switchers describe different trade-offs and whether the same explanation holds across segments. Recommendations focus on the consequential gaps, including deeper analysis when the existing interviews already contain the detail.

The design draws on information power—how much relevant information a sample provides—and meaning saturation—whether a theme is understood in depth. User Intuition applies those principles to judge which customer experiences or explanations need closer investigation. See how User Intuition assesses evidence.

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 the assistant to investigate why buyers delay adoption. It can check earlier interviews for implementation concerns and prepare a follow-up focused on customers whose experience is missing. Your team reviews the plan and approves spending; the assistant can monitor progress and return findings linked to source interview moments.

AlphaSense’s MCP connector answers questions from its entitled content with citations. Its Agent API supports research workflows. User Intuition’s workflow centers on commissioning and assessing customer interviews, so a team can use market research to frame a hypothesis and customer research to investigate it.

User Intuition’s synthetic participants preserve individual experiences and behavioral differences that generic LLM feedback can flatten. For diligence, explore the tension between an attractive market story and the concerns of an actual buyer: switching effort, budget ownership or operational disruption. Use these responses to identify assumptions worth probing with the target audience. 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. For customer research, that informs a person-specific model whose responses can be checked and refined against the same customer’s experience. See how User Intuition grounds and calibrates synthetic participants.