리서치

Measure behaviors people won't admit directly: the list experiment (item count technique) asks only HOW MANY statements apply — never which — so individual answers stay genuinely deniable while group comparisons reveal the true rate. The native question type randomizes control and treatment lists for you.

Benchmarks how customers perceive your brand against named competitors — consideration, head-to-head ratings, and switching likelihood — then uses an AI follow-up interview to surface the real story behind a preference or switch. Built for product marketing, competitive intelligence, and strategy teams tracking share of consideration.

A complete consent flow for research studies: plain-language study information, granular consent statements with a comprehension check, signature capture, and contact enrollment. Built for IRB-style requirements — participants confirm they understand, not just that they scrolled.

A voice AI discovery interview that follows Mom Test rules automatically: past behavior, not hypotheticals; specifics, not compliments. It asks about the last time the problem happened, what it cost, and what they've already tried — the questions founders forget to ask when a polite prospect starts flattering their idea.

A structured concept testing survey for evaluating new e-commerce ideas with online shoppers. Measures appeal, clarity, perceived differentiation, and trial intent to inform positioning decisions before launch.

대학원생, 박사후연구원, 교수진이 실제로 연구 시간을 어떻게 사용하는지, 그리고 연구비, 멘토링, 확보된 연구 시간, 협업이 그들의 연구를 얼마나 잘 지원하는지를 측정합니다. AI 후속 인터뷰는 각 응답자가 지목한 가장 큰 장애 요인을 심층적으로 파고들어, 모호한 불만 대신 구체적이고 최근의 사례를 재구성합니다. 연구 지원을 벤치마킹하는 연구지원처, 학장, 연구책임자(PI)를 위해 설계되었습니다.

A true factorial vignette experiment: respondents judge realistic scenarios whose key details (price framing, messenger, wording) rotate systematically, so you can measure how each factor shifts judgment. The built-in vignette question type generates the scenario combinations for you.

A clean recruiting screener: knock-out questions first, quota-ready demographics, an attention check, and an articulation check that verifies participants can give the rich answers qualitative research needs. Designed to disqualify politely and comply with prefer-not-to-say norms.

Measures A/B testing ease-of-use, guardrail adoption, result trust, and decision confidence among product and engineering teams. Use it to identify friction points, governance gaps, and training needs to scale experimentation.

A self-serve usability session: participants share their screen, complete guided tasks on your website or prototype while thinking aloud, and the AI moderator watches, prompts, and probes — then debriefs them. You get recordings, transcripts, and task-level analysis without scheduling a single session.

An open-ended card sorting study for navigation and information architecture: participants group your content into categories that make sense to them, then an AI interviewer probes the logic behind their groupings — the part a drag-and-drop grid alone never captures.

A repeatable diary entry participants complete each time they use your product in real life: a photo of the moment, what they were trying to do, and what got in the way — with an AI probe on the day's friction. Run it daily or weekly to see usage where it actually happens, not in a lab.

Maps the complete journey of a customer's most recent e-commerce session—from intent and actions to friction points and blockers—to diagnose conversion drop-off and prioritize UX improvements.

Assesses experimentation program maturity across culture, process, tooling, governance, and outcomes. Designed for product, growth, and data teams to benchmark capabilities and identify improvement priorities.

A structured diary study instrument for capturing in-the-moment e-commerce experiences across touchpoints and tasks. Designed for repeated daily entries to identify pain points, emotional states, and task-completion barriers throughout the customer journey.

Segments online shoppers by purchase behavior, experience drivers, and communication channel preferences. Use this to build data-driven buyer personas and optimize channel strategy for e-commerce brands.

Pre-qualifies potential stakeholder interviewees by assessing role fit, recent domain involvement, and scheduling availability for 45–60 minute research sessions.

A structured 3-entry diary study for UX researchers to capture real online shopping journeys, friction points, and purchase decision drivers over a 14-day period.

Measures consumer purchase frequency, channel preferences, decision drivers, and trade-offs in online shopping. Designed for ecommerce researchers and product teams seeking actionable shopper segmentation insights.

The standard 10-item SUS questionnaire, scored 0–100 and benchmarkable against decades of published research — with correct alternating positive/negative wording preserved. An AI debrief interview follows the score, so you learn what to fix, not just where you stand.