설문 템플릿

사람들이 엔터테인먼트 챗봇(친구 역할, 롤플레이, 유머, 스토리텔링)을 얼마나 자주 사용하는지, 무엇이 재방문을 유도하는지, 그리고 경험이 어디에서 기대에 못 미치는지를 측정합니다. 또한 AI 후속 질문을 통해 기억에 남는 특정 대화를 재구성하여 실제로 무엇이 재미있고, 그럴듯하며, 혹은 실망스러웠는지를 드러냅니다. 캐릭터 또는 엔터테인먼트 중심의 AI 경험을 출시하는 제품 및 콘텐츠 팀을 위해 제작되었습니다.

For podcast hosts and producers to learn how guests actually experienced booking, recording, and the finished episode. Covers pre-show communication, recording-day comfort, and post-production follow-through, with an AI follow-up interview that digs into the specific moments behind a guest's overall rating.

Measures how teams actually use their project management tool day to day — adoption, feature-level satisfaction, and the friction points that drive people back to spreadsheets — with an AI follow-up that reconstructs a specific recent workflow moment instead of general sentiment. Built for product, IT, and operations teams evaluating or renewing tools.

For podcast hosts and producers to learn how guests actually experienced booking, recording, and the finished episode. Covers pre-show communication, recording-day comfort, and post-production follow-through, with an AI follow-up interview that digs into the specific moments behind a guest's overall rating.

직원들이 업무를 잘 수행하고 고객에게 자신 있게 서비스를 제공하는 데 필요한 도구, 권한, 교육, 지원을 갖추고 있는지 측정합니다. 취약한 고객 경험을 일선 지원의 공백까지 추적하고자 하는 CX 및 인사 리더를 위해 설계되었으며, AI 후속 질문을 통해 직원이 업무에 막혔거나 권한을 부여받았다고 느낀 실제 순간을 재구성합니다.

Captures what happened when a customer raised a complaint — how easy it was to submit, how the resolution process felt, and whether it was actually fixed. An AI follow-up interview digs into the specific moment satisfaction dropped (or rose), surfacing what a closed-ended score alone would miss. Built for support, CX, and quality teams reviewing complaint handling.

Measures how easily shoppers find products, move through checkout, and trust your site with their payment details — with an AI follow-up that reconstructs the exact moment shoppers got stuck or almost abandoned their cart, rather than just a satisfaction score.

Captures a complete brief for an internal or client graphic design request — project type, goal, audience, brand constraints, and priority trade-offs — so designers stop guessing what 'good' looks like. An AI follow-up interview digs into the real goal behind the ask and surfaces conflicting stakeholder expectations before work begins.

Evaluates how people actually use an interior design planning tool or app — from room visualization to furniture shopping — and where they get stuck. Closed questions surface satisfaction and feature priorities; an AI follow-up interview digs into the specific moment the experience broke down, giving product teams a concrete story instead of a vague complaint.

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.

Measures how safe, fair, and supported people feel in their day-to-day work — trust in leadership, psychological safety, workload fairness, and manager impact — for HR and people teams tracking team health between engagement cycles. An AI follow-up reconstructs the specific moment behind each person's rating instead of settling for a number on a dashboard.

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.

수신자가 엽서, 카탈로그, 편지 등 다이렉트 메일을 디지털 채널과 비교하여 실제로 어떻게 인지하고 읽고 행동하는지를 측정하며, 반응을 이끄는 요인에 대한 best-worst 과제와 특정 우편물이 누군가의 우편함에 도착한 후 무슨 일이 일어났는지를 재구성하는 AI 후속 질문을 포함합니다. 캠페인 성과를 평가하는 마케팅 팀과 다이렉트 메일 대행사를 위해 설계되었습니다.

Measures how well your website or marketing chatbot resolves visitor questions, builds trust, and moves people toward a purchase or signup. An AI follow-up interview reconstructs the respondent's most recent chatbot conversation in detail, surfacing exactly where it helped or broke down. Built for marketing and CX teams evaluating a live chatbot deployment.

Measures whether a specific influencer or creator campaign actually changed awareness, trust, and purchase behavior — not just impressions. Built for brand and growth marketers evaluating a campaign, with an AI follow-up that reconstructs the real moment a customer decided (or chose not) to act after seeing the content.

Confirms employees understand and accept the terms of their company laptop agreement — device care, data security, personal use limits, and return obligations — while capturing current equipment condition. An AI follow-up interview probes real-world edge cases (travel, shared devices, public wifi) to surface where policy understanding actually breaks down.

For HR partners and managers to audit a single employee separation right after it happens: were final pay, access revocation, property return, and exit paperwork all completed on time? The AI follow-up interview probes what caused any delays or missteps so process gaps get fixed before the next termination.

Measures how confident employees are that they're paid correctly and on time, where payroll errors and confusion actually happen, and how easy it is to get help when something looks wrong. An AI follow-up interview reconstructs the most recent payroll issue an employee ran into, step by step, instead of relying on a vague satisfaction score. Built for HR and payroll teams auditing their process.

사람들이 엔터테인먼트 챗봇(친구 역할, 롤플레이, 유머, 스토리텔링)을 얼마나 자주 사용하는지, 무엇이 재방문을 유도하는지, 그리고 경험이 어디에서 기대에 못 미치는지를 측정합니다. 또한 AI 후속 질문을 통해 기억에 남는 특정 대화를 재구성하여 실제로 무엇이 재미있고, 그럴듯하며, 혹은 실망스러웠는지를 드러냅니다. 캐릭터 또는 엔터테인먼트 중심의 AI 경험을 출시하는 제품 및 콘텐츠 팀을 위해 제작되었습니다.

Evaluates how well an airline or travel site's AI chatbot handles real booking, change, and support tasks — covering task completion, trust, and where users bail out to a human. An AI follow-up interview reconstructs exactly what happened in the respondent's most recent chatbot session, not just how they'd rate it in hindsight.

Measures how well an AI scheduling chatbot helps patients book, reschedule, or get answers about medical appointments, covering task completion, trust, and friction points — with an AI follow-up interview that digs into the specific moment the chatbot helped or failed.

Measures how teams actually use their project management tool day to day — adoption, feature-level satisfaction, and the friction points that drive people back to spreadsheets — with an AI follow-up that reconstructs a specific recent workflow moment instead of general sentiment. Built for product, IT, and operations teams evaluating or renewing tools.

소프트웨어 엔지니어를 위한 구조화된 성과 점검으로, 자가 평가 역량 점수, 행동 빈도 질문, 우선순위 트레이드오프 연습을 AI 후속 인터뷰와 결합합니다. 이 인터뷰는 개발자가 겪은 가장 큰 장애물과 가장 낮게 평가한 역량 영역을 깊이 파고들어, 관리자가 1:1 면담과 성장 계획에 활용할 수 있는 구체적이고 세부적인 정보를 제공합니다.

Captures how a specific software release landed with users — installation friction, stability, performance, and which features actually helped — for product and engineering teams shipping regular updates. The AI follow-up interview reconstructs exactly what broke or slowed someone down instead of settling for a vague 'it had bugs.'

최근 학부모-교사 상담에서 학부모가 얼마나 준비되어 있었고, 자신의 의견이 경청되었으며, 충분한 정보를 얻었다고 느꼈는지, 실제로 어떤 주제가 다루어졌는지, 그리고 명확한 다음 단계 계획이 마련되었는지를 파악합니다 — AI 후속 질문을 통해 제기된 구체적인 우려 사항과 그것이 해결되었는지 여부를 재구성합니다. 상담 품질을 점검하는 학교 및 학부모회를 위해 제작되었습니다.

Captures why families register for your Vacation Bible School, their scheduling and logistics preferences, and any health or accessibility needs — with an AI follow-up that surfaces the real motivations and hesitations behind a family's choice instead of just checkbox answers.

Measures how likely students are to recommend a course, program, or instructor to a peer, what drives that willingness, and where the experience falls short. An AI follow-up interview digs into the specific reasons behind each student's score, surfacing concrete moments and details that a rating alone can't capture. Built for academic departments, bootcamps, and course teams tracking word-of-mouth and retention risk.

Captures a couple's wedding date, venue, guest count, budget priorities, and style preferences ahead of a florist consultation, then uses an AI follow-up interview to surface the emotional story and specific imagery behind their vision — so the design team can propose flowers that match what the couple actually pictures, not just the style words they checked.

Captures how attendees rated sessions, speakers, networking, and logistics, plus their likelihood to return or recommend — with an AI follow-up interview that digs into the single moment or gap that most shaped their overall verdict. Built for event and community teams planning next year's agenda and budget.

Captures how attendees experienced your event — from content and speakers to venue and networking — and uses an AI follow-up interview to uncover the specific moment behind their satisfaction and recommendation scores. Built for event, marketing, and community teams running post-event debriefs.

Captures how borrowers experienced applying for a mortgage — from documentation and communication to rate transparency and closing timeline — for lenders and brokers who want to cut friction out of the process. An AI follow-up interview digs into the single biggest pain point each borrower hit, reconstructing exactly what happened instead of a vague complaint.

Measures how business borrowers experienced applying for and repaying an equipment loan — from why they picked the lender to how clear the terms felt — for lenders and finance teams tracking borrower experience. An AI follow-up interview reconstructs the actual decision journey and the moment borrowers almost walked away.

Measures how customers actually use your mobile banking app — login, transfers, bill pay, deposits — and where friction shows up, paired with a prioritization exercise for what to fix next. An AI follow-up interview reconstructs a real recent session in detail, surfacing the exact moment things broke down instead of vague complaints.

A periodic self-assessment covering physical health, sleep, stress, activity levels, and emerging symptoms — used by clinics, employers, or wellness programs to spot concerns early. An AI follow-up interview digs into whatever symptom or concern the respondent flagged as most pressing, capturing duration, severity, and impact that a checkbox can't.

Confirms that a new group therapy member understands confidentiality limits, voluntary participation, and what to expect, while gauging comfort with sharing in front of others. An AI follow-up interview surfaces the specific worries behind low comfort or confusion so facilitators can address them before the first session. Built for therapists and group practices running intake.

Measures how people actually use an AI therapist chatbot, how helpful and trustworthy they find it, and whether it has ever felt unsafe or judgmental — for product and clinical safety teams. The AI follow-up interview reconstructs a specific real conversation instead of relying on vague satisfaction ratings.

관심도, 선호하는 형식, 시간, 필수 요소를 측정하여 주최자가 손님들이 실제로 참석하고 싶어 하는 졸업 파티를 계획할 수 있도록 돕습니다. 망설이는 손님을 확신에 찬 참석 의사로 바꿔줄 요소를 심층적으로 파고드는 AI 후속 질문이 포함되어 있습니다.

Captures how players rate the difficulty, fairness, and fun of your online football trivia quiz, which topics they want more of, and how likely they are to return, recommend, or pay for premium content. An AI follow-up interview digs into the specific question or moment that shaped their score, revealing what to fix or double down on.

Captures the essential background, goals, and priorities of a new client before an engagement begins — company profile, decision context, and success criteria — with an AI follow-up that digs into the real business problem behind their stated goals so your team starts the relationship with clarity instead of assumptions.

Measures how everyday consumers manage money: budgeting habits, financial stress, product usage, spending allocation, and satisfaction with their primary bank or financial provider. An AI follow-up interview digs into the real story behind a recent financial decision or stressful moment instead of settling for a scale score. Built for banks, fintechs, and researchers tracking consumer financial wellbeing.

A pre-conversation intake survey for sales, real estate, and consultative teams to capture a prospective buyer's needs, timeline, budget, and decision-making process before the first meeting. The AI follow-up digs into whichever decision factor matters most to that buyer and surfaces the deal-breakers a form alone would miss.

Measures how personally invested, informed, and careful shoppers are when choosing products in a given category — from perceived risk and personal identity to time spent researching — with an AI follow-up interview that reconstructs the real story behind a recent purchase decision. Built for brand and category teams who need to know how much shoppers actually think before they buy.

Measures how members perceive the value of their association benefits, how likely they are to renew and recommend membership, and which offerings deserve more investment — with an AI follow-up that digs into the real reasons behind a member's renewal score instead of a generic satisfaction rating. Built for membership and engagement teams running annual or renewal-cycle check-ins.

Measures how satisfied volunteers are with their role, training, recognition, and coordination — and what would keep them coming back. Includes a best-worst trade-off to prioritize fixes and an AI follow-up interview that digs into the real reason behind a volunteer's satisfaction or hesitation to continue. Built for volunteer coordinators and nonprofit program managers.

Captures new volunteer applicants' availability, skills, and cause interests so nonprofits can match them to the right role fast — with an AI follow-up that surfaces the personal motivation behind their sign-up instead of a generic checkbox reason.

For employees requesting to onboard a new vendor or supplier. Captures the business need, estimated spend, urgency, risk factors, and alternatives already considered so procurement and operations teams can triage requests quickly. The AI follow-up interview probes why existing approved vendors won't meet the need and surfaces risk details a form alone would miss.

Measures how thoroughly people prepare for meetings they lead and attend — agendas, objectives, materials, and roles — and where preparation habits break down. An AI follow-up interview reconstructs a specific under-prepared meeting to surface the real friction behind missed prep steps, for operations teams trying to improve meeting quality.

직원들이 실제로 언제 회의에 참석할 수 있고 참석할 의향이 있는지, 회의 시간이 실제로 어떻게 배분되는지, 그리고 일정 관리의 마찰이 어디에서 집중 업무를 갉아먹는지를 파악합니다. AI 후속 인터뷰를 통해 최근 잘못 진행된 특정 회의 하나와 그것을 개선할 수 있었던 방법을 심층적으로 파헤칩니다. 회의 문화를 점검하는 운영, IT, 인사 팀을 위해 제작되었습니다.

Measures how shelf appeal, unboxing experience, materials, and clarity of a product's packaging shape purchase decisions. Built for brand, packaging, and product teams testing a design or concept, with an AI follow-up interview that reconstructs the exact moment packaging almost won or lost the sale.

Measures how clients perceive a broker's fees or commission — the point where a price feels like a bargain, fair, expensive, or a dealbreaker — and which fee structures and service trade-offs matter most to them. Built for real estate, financial, insurance, or mortgage brokerages, with an AI follow-up that uncovers the real reasoning behind each price threshold.

Tests how a product package performs on the shelf — standout appeal, first-noticed elements, message clarity, and purchase intent — with a best-worst trade-off for design priorities and an AI follow-up that reconstructs the split-second decision to buy or pass.

Captures whether spa clients truly understand and freely consent to treatment risks, health disclosures, and photo or data use before their appointment — built for spa managers and compliance teams. The AI follow-up interview explores any hesitation or confusion behind risk-factor answers instead of settling for a checked box.

A pre-interview consent form that confirms participants understand what they're agreeing to — recording formats, how their words may be used, and their right to withdraw — plus an AI follow-up that catches confusion or hesitation before the real interview starts. Built for researchers running qualitative interviews, usability sessions, or customer discovery calls.

Measures how employees actually handle passwords, multi-factor authentication, updates, and suspicious messages day to day — not just what they know in theory. Built for IT and security teams auditing awareness programs, with an AI follow-up that reconstructs a real near-miss or incident instead of a hypothetical one.

Qualifies prospective gaming console buyers by purchase timeline, budget sensitivity, and feature priorities, then captures contact details for sales follow-up. The AI follow-up interview digs into what's actually holding back a purchase decision, giving sales reps a real objection to address instead of a generic 'still deciding.'

Surveys customers who did (or didn't) buy an extended warranty or protection plan to uncover what drove the decision, how they rate the value and claims experience, and who these buyers are demographically. An AI follow-up interview digs into the reasoning behind the purchase call, giving revenue teams the 'why' behind attach-rate and pricing decisions.

Captures how prospects experienced your live product demonstration — clarity, relevance, and perceived value — plus a clear read on purchase intent. An AI follow-up interview digs into the real reasoning behind that intent, surfacing hidden objections and what would move the deal forward that a rating alone won't reveal.

Measures public familiarity with kangaroos, perceptions of their characteristics, attitudes toward kangaroo conservation, and behavioral intentions. Suitable for wildlife researchers, conservation organizations, or educational program evaluators.

Measures leisure travelers' trip frequency, destination decision drivers, transportation preferences, satisfaction, and likelihood to recommend — providing actionable segmentation and experience insights for travel brands, tourism boards, or academic research.

A structured instrument for collecting student feedback on course satisfaction, instructional effectiveness, and learning outcomes. Designed for end-of-term deployment to support data-driven course improvement.

Captures why employees are requesting changes to their work schedule — shifted hours, compressed weeks, remote days, or reduced hours — and how urgent and job-critical the need is. Built for HR and People teams shaping flexible-work policy, with an AI follow-up that digs into the real constraint behind the request and what a workable compromise would look like.

Gauges how a specific proposed change request lands with the people it affects — awareness, perceived risk, resource readiness, and willingness to support it — built for change managers, PMOs, and transformation leads preparing a rollout or approval decision. The AI follow-up interview digs into each respondent's single biggest concern and what would turn hesitation into active support.

Measures whether employees actually noticed, understood, and trust a recent payroll change notice (e.g., new pay schedule, provider, or deduction) and how confident they are their next paycheck will be correct — with an AI follow-up that digs into exactly what's still unclear or worrying to the people who need the most help. Built for HR, payroll, and change-management teams rolling out a payroll transition.

Measures how aware, concerned, and behaviorally engaged people are with climate change — from perceived risk and trust in institutions to everyday actions — with an AI follow-up that digs into the gap between stated concern and real-world behavior. Built for sustainability teams, communicators, and researchers tracking public climate sentiment.

소비자가 책임 있는 물 사용을 가격, 품질 및 기타 제품 속성과 비교하여 어떻게 저울질하는지를 측정합니다. 소비자 패널을 대상으로 지속가능성에 기반한 트레이드오프를 정량화하고 물 관리에 대한 지불 의향의 배경에 있는 태도적·상황적 요인을 파악하기 위해 설계되었습니다.

A structured audit instrument for assessing manufacturing suppliers across environmental, social, and governance pillars. Designed for procurement and sustainability teams to identify ESG risks, benchmark supplier maturity, and prioritize collaborative improvement initiatives.