Industry-specific survey templates

A periodic check-in that measures how often people reflect, how self-aware they feel, and what's actually blocking deeper reflection — paired with an AI follow-up that walks through one real moment of insight instead of vague self-ratings. Built for coaches, HR wellbeing programs, and personal-development tools tracking growth over time.

Captures how employees see their progress at the midpoint of the year — goal status, manager support, obstacles, and priorities for the second half — so managers and HR can course-correct before year-end reviews lock in. An AI follow-up interview reconstructs the real story behind each person's progress rating and top obstacle instead of just the label they picked.

Maps what people actually eat, which cuisines and attributes they favor, and what really drives a food choice in the moment — price, health, convenience, or taste. Built for food brands, restaurants, and meal-kit teams shaping menus or products. An AI follow-up interview reconstructs the last real meal decision instead of relying on stated preferences alone.

Measures how satisfied clients or stakeholders are with a completed or in-flight project — covering scope clarity, timeliness, budget adherence, communication, and deliverable quality — with an AI follow-up that digs into the real story behind the overall rating instead of a generic star score. Built for agencies, consultancies, and internal project teams running post-project or milestone reviews.

Measures how often people watch TV news and read newspapers, which formats and sources they trust most, and what factors drive their choice of news media — with an AI follow-up that uncovers the real story behind rising or falling engagement. Built for media researchers, broadcasters, and publishers tracking audience shifts.

Explores how customers actually use their debit card day to day — spend allocation, fees, fraud concerns, and feature priorities — with an AI follow-up interview that uncovers the real story behind a decline, surprise fee, or trust issue instead of stopping at a satisfaction score. Built for banks, credit unions, and fintechs evaluating their debit card experience.

현재 Apple 및 Samsung 기기 사용자가 각 브랜드를 카메라, 배터리, 소프트웨어, 디자인, 가성비 측면에서 어떻게 평가하는지 비교하고, best-worst 트레이드오프와 점수 배분을 통해 실제 구매 결정을 이끄는 요인을 드러냅니다. AI 후속 인터뷰가 브랜드 충성도 또는 전환 계획의 실제 계기를 심층적으로 파고듭니다. 모바일 브랜드 및 제품 팀을 위해 설계되었습니다.

고객이 만족도나 충성도 점수를 넘어 귀사의 브랜드에 얼마나 깊이 정서적으로 연결되어 있는지를 측정합니다 — 정체성, 향수, 신뢰, 상실감을 아우릅니다. AI 후속 인터뷰를 통해 가장 강한 유대감을 형성한 특정 기억이나 순간을 드러내어, 수치 이면의 정성적 색채를 제공합니다.

Explores how people actually spend their free time, how satisfied they are with that balance, and what gets in the way of doing more of what they enjoy. Built for leisure, wellness, media, and lifestyle brands mapping real behavior, not stated preferences — with an AI follow-up that reconstructs a specific recent moment when leisure time fell short.

Measures how donors, volunteers, board members, and program participants feel about a non-profit's transparency, impact, and communication — with an AI follow-up that digs into the real reasons behind loyalty or hesitation instead of surface-level satisfaction scores. Built for development, program, and communications teams.

Measures how people access, experience, and think about STI/STD testing services — testing frequency, comfort with providers, and the practical or emotional barriers that keep people from testing regularly. Built for sexual health clinics and public health teams, with an AI follow-up that digs into the real reasons behind gaps in testing behavior.

Measures how LGBTQ+ audiences and allies encounter and judge queer representation in film, TV, and streaming — how often they see it, where, and whether it feels authentic — with an AI follow-up interview that digs into one specific portrayal that shaped their view. Built for media researchers, studios, and advocacy orgs.

방문의 주요 접점(전반적 경험, 직원, 속도, 환경)에 대해 간단한 스마일 아이콘 평가를 활용하는 빠르고 즉각적인 피드백 조사입니다. 소매, 접객, 의료 및 기타 서비스 환경을 위해 설계되었습니다. AI 후속 인터뷰를 통해 전반적 평가 뒤에 숨은 '이유'를 파고들어, 낮은 점수가 미스터리로 남지 않고 높은 점수가 지켜야 할 요소를 드러내도록 합니다.

Explores how households think about switching to or expanding renewable energy — current usage, the trade-offs that matter most, and what's holding people back. An AI follow-up interview digs into the real story behind the biggest barrier or motivation a respondent picks, going beyond checkbox answers. Built for utilities, solar installers, and energy programs sizing demand and messaging.

Evaluates how customers research, compare, and buy consumer electronics from your store — covering website usability, price perception, and trust in shipping/support — with an AI follow-up that reconstructs the real decision journey behind their most recent purchase. Built for e-commerce and electronics retail teams optimizing conversion and retention.

Measures how prone employees are to feeling bored or understimulated at work, using a trait-style statement battery, situational triggers, and coping responses. Built for HR, L&D, and people-analytics teams. An AI follow-up interview reconstructs a real recent boring moment to separate personality-driven boredom proneness from fixable role-design problems.

Measures how motivated, supported, and connected colleagues feel day to day, benchmarks a recommend-to-a-friend engagement score, and identifies which drivers (recognition, growth, workload, manager support) matter most. An AI follow-up interview digs into the real story behind each person's score instead of settling for a number.

이메일, 전체 회의, 채팅 도구 같은 채널 전반에서 귀사의 사내 커뮤니케이션이 직원에게 얼마나 잘 정보를 전달하고, 참여를 이끌어 내며, 도달하는지를 평가합니다. 진술 배터리와 우선순위 지정 활동, 그리고 효과가 있었거나 부족했던 최근의 구체적인 커뮤니케이션 사례를 파고드는 AI 후속 질문을 포함합니다.

Measures how much control people feel they have over key areas of their life — work, money, health, relationships, and time — plus what's driving that feeling. An AI follow-up interview digs into the one domain where control feels lowest, reconstructing a real recent moment instead of relying on abstract self-ratings.

Measures how easy, clear, and satisfying your product's interface is to use in real day-to-day tasks — combining a multi-element usability rating, a trade-off exercise on what to fix first, and an AI follow-up interview that reconstructs the exact moment a user got stuck instead of relying on vague complaints.

Measures how project teams actually use their construction project extranet for document control, RFIs, and drawing markup, and where version confusion or slow approvals cost time. An AI follow-up interview reconstructs a specific recent incident to surface the real workflow friction behind the ratings.

Uncovers which retirement housing features — cost, location, care access, privacy, community life — actually drive decisions, using trade-off exercises and price sensitivity questions instead of simple ratings. Built for senior living operators, developers, and planners, with an AI follow-up that probes what respondents would sacrifice to secure their top priority.

Qualifies and recruits candidates for a focus group by screening for category usage, conflicts of interest, prior research participation, and scheduling fit, then capturing core demographics. An AI follow-up interview asks candidates to describe their real experience with the topic in their own words, surfacing rehearsed 'professional respondent' answers and gauging how articulate and engaged they'll be in the room.

A guessing-game survey for partners, family, friends, or teammates to test how accurately they can predict someone's habits, preferences, and values — with an AI follow-up that unpacks the reasoning behind their most confident (and shakiest) guesses. Built to be customized with the actual subject's traits before sending.

Captures how shoppers choose, evaluate, and switch between grocery stores and channels — price sensitivity, loyalty behavior, and channel mix — for retail and CPG researchers. An AI follow-up interview digs into the real story behind a shopper's most recent store choice or channel switch, beyond what a closed question can capture.

가격, 상품 구색, 서비스, 편의성 측면에서 귀사의 매장을 지목된 경쟁사와 벤치마킹한 후, AI 후속 질문을 통해 쇼핑객이 특정 매장을 다른 매장보다 선택한 구체적인 순간을 재구성합니다. 지갑 점유율(share-of-wallet)과 경쟁적 포지셔닝을 추적하는 소매 인사이트 팀을 위해 제작되었습니다.

사람들이 실제로 유기농 식품을 얼마나 자주 구매하는지, 어떤 인증과 표시가 실제로 구매 결정을 좌우하는지, 유기농 라벨에 대한 신뢰도, 그리고 가격 민감도를 측정합니다 — 일반화된 태도가 아니라 최근의 유기농 구매(또는 구매하지 않은) 경험 뒤에 있는 실제 이야기를 재구성하는 AI 후속 인터뷰가 포함됩니다.

A relationship health check that scores satisfaction, trust, intimacy, communication, and love languages, then uses an AI follow-up interview to dig into whichever dimension scored lowest. Built for dating apps, matchmakers, and relationship coaches who need more than a vague compatibility score.

쇼핑객이 특정 슈퍼마켓을 선택하고 계속 이용하게 만드는 실제 요인 — 가격, 신선도, 편의성, 리워드 — 을 밝혀냅니다. 최선-최악 상충 관계 연습, 만족도 배터리, 그리고 최근의 매장 전환 또는 전환 고민의 실제 이야기를 재구성하는 AI 후속 인터뷰를 활용합니다. 식료품 소매 및 카테고리 인사이트 팀을 위해 설계되었습니다.

Measures how satisfied employees are with the substance of their job — the tasks, autonomy, variety, skill fit, and workload — separate from pay or manager quality. Built for HR and people-ops teams running role-design reviews, with an AI follow-up that digs into the specific task or moment behind their lowest-rated aspect of the work.

Measures how often employees experience or witness gender-based discrimination — in pay, promotion, meetings, and everyday treatment — and how safe they feel speaking up. An AI follow-up interview reconstructs a specific incident in the respondent's own words, revealing what actually happened and what response would have felt fair, for HR and DEI teams benchmarking climate and prioritizing fixes.

Maps how shoppers browse, search, and decide across in-store, online, and marketplace channels — where product searches succeed or break down, which purchase factors carry the most weight, and what an AI follow-up interview uncovers about the last time someone couldn't find what they wanted. Built for retail, e-commerce, and merchandising teams optimizing search, navigation, and discovery.

Measures how productive, focused, and supported employees feel while working remotely, and pinpoints the specific barriers eating into their day. An AI follow-up interview digs into the single biggest barrier each person identifies, reconstructing a real recent workday instead of relying on generalized complaints.

A point-factor job evaluation questionnaire for HR and compensation teams grading or re-grading a role — covering decision authority, supervisory scope, effort, and working conditions, with an AI follow-up that pressure-tests the scoring against a real high-stakes task the job has actually handled.

Captures how graduating students rate their academics, career readiness, and campus life as they head out the door, plus their post-graduation plans and likelihood to recommend the school. An AI follow-up interview digs into the specific moment or decision behind their lowest-rated aspect instead of settling for a generic score. Built for institutional research and student affairs teams running exit polls each term.

Surfaces what people genuinely value most — career, family, health, growth, community, freedom — and how closely their daily choices match those priorities. Useful for coaches, HR teams, and researchers studying wellbeing or culture fit. The AI follow-up interview digs into the story behind a respondent's top value and any gap between stated priorities and lived time.

Assesses how prepared people actually felt, what emergency communications and procedures worked, and where gaps showed up during a real event or drill. Built for emergency management teams, facilities, and HSE departments benchmarking readiness, with an AI follow-up that reconstructs exactly what happened during the respondent's most recent disaster or drill instead of relying on general perceptions.

학생들이 명확성, 참여도, 피드백, 접근성, 공정성 측면에서 교수의 강의를 어떻게 경험하는지를 측정하며, 전반적인 만족도와 추천 의향도 함께 파악합니다. 학기말 평가를 진행하는 학과 및 강의 담당자를 위해 설계되었으며, 학생의 평가에 가장 큰 영향을 미친 특정 순간이나 과제를 깊이 있게 탐구하는 AI 후속 인터뷰가 포함되어 있습니다.

Captures how patients experience a physician practice end-to-end — scheduling ease, wait times, physician communication, and likelihood to recommend — with an AI follow-up that digs into the 'why' behind recommendation scores, especially from patients who were lukewarm or dissatisfied. Built for practice managers and physician groups tracking patient experience.

Measures how often people drink energy drinks, which brands and formats they choose, what drives that choice, and what they'd pay for a new product. Built for beverage brand and category teams, with an AI follow-up interview that reconstructs the real story behind a recent brand switch or purchase decision instead of relying on stated preference alone.

Captures how easily visitors accomplish what they came to do on your site — navigation, speed, content clarity, and overall satisfaction — with an AI follow-up that digs into the exact moment a task succeeded or broke down. Built for product, UX, and marketing teams running ongoing site feedback.

사람들이 음주 후 실제로 운전 여부를 어떻게 결정하는지, 어떤 대안을 이용하는지, 어떤 장애 요인이 방해가 되는지, 그리고 그 위험을 실제로 얼마나 크게 인식하는지를 탐구합니다. 예방 캠페인을 설계하는 교통안전 기관, 고용주, 주점, 차량 공유 또는 보험 프로그램을 위해 만들어졌습니다. AI 후속 인터뷰는 가상의 선의가 아닌 마지막 실제 결정 순간을 재구성합니다.

Measures awareness, support, and concerns among residents near a wind energy project — covering noise, visual impact, trust in the developer, and perceived local benefits. An AI follow-up interview digs into the real reason behind each respondent's level of support, giving developers and utilities the qualitative context behind the numbers before community meetings or permitting decisions.

Captures how satisfied people feel about their life overall, what they value most, and how their time and energy actually line up with those values. Built for researchers, coaches, and wellbeing programs who want more than a satisfaction score — the AI follow-up digs into why that score is what it is and where priorities and daily life have drifted apart.

Measures how people actually use a social networking platform — frequency, trust, feature value, time allocation, and loyalty via a recommendation score. Built for product, growth, and trust & safety teams at social platforms and community apps. The AI follow-up interview digs into the specific moment behind each promoter or detractor score instead of generic praise or complaints.

Measures how customers experienced a recent installation job — scheduling ease, technician professionalism, first-time-right completion, and value for price — with an AI follow-up that digs into what actually went wrong (or right) during the visit. Built for home services, appliance, flooring, HVAC, solar, and similar installation businesses.

Captures how well a product, device, or software integrates with a customer's existing tech stack, workflows, and environments — for IT, engineering, and procurement teams running vendor or upgrade compatibility evaluations. An AI follow-up interview digs into the single worst compatibility issue found, reconstructing exactly what broke and what workaround (if any) was used.

Measures how clients experience counselling delivered by a non-profit — trust in their counsellor, progress toward goals, and barriers to attending sessions. Built for program managers and supervisors tracking access gaps and impact for funders. The AI follow-up interview uncovers the story behind each client's progress and recommendation ratings, beyond a simple score.

Gauges how employees really feel about working here — recommendation likelihood, day-to-day support, and what would make them stay or leave — paired with an AI follow-up that digs into the story behind their score instead of just the number. Built for HR and people teams running a quarterly or annual pulse check.

Measures how shoppers perceive a store's personality — trustworthy, trendy, premium, approachable — and whether that character matches what they actually want. Includes a best-worst trade-off on trait priority and an AI follow-up interview that digs into the mental image customers form of the brand and why.