시장 조사

Measures how often people buy gasoline, what drives their choice of station, and how they perceive fair versus unfair pump prices — for retailers, fuel brands, and pricing teams. An AI follow-up interview digs into the gap between stated price sensitivity and what actually makes drivers switch or stay loyal.

Compares how shoppers use a retailer's own AI assistant versus general tools like ChatGPT or Gemini for product research and purchase decisions. Built for retail and ecommerce teams deciding where to invest in AI shopping tools. The AI follow-up interview reconstructs the specific moment a shopper picked one over the other and what would change their mind.

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.

고객 충성도를 전반적인 만족도와 함께 측정하기 위한 방법론적으로 견고한 NPS 설문조사입니다. 상호작용 이후 또는 정기적 실시를 위해 설계되었으며, promoter, passive, detractor를 식별하고 고객 경험의 실행 가능한 동인을 발견하는 데 활용됩니다.

Measures unaided and aided brand awareness, category usage, consideration sets, first-choice preference, and perceptual brand equity attributes. Designed for tracking brand health over time across competitive sets.

Captures what shoppers came to buy, how they navigated the store, what they did when an item was missing, and how checkout felt — with an AI follow-up that digs into the real reasoning behind substitution choices and navigation friction. Built for retail insights and category management teams.

Maps how many paid subscriptions people juggle, which categories they'd protect versus cancel first, and what actually triggers a cancellation. Built for subscription businesses and researchers tracking churn risk, with an AI follow-up that reconstructs a real recent cancel-or-keep decision instead of a hypothetical one.

Measures how research teams plan projects, select and evaluate tools, and navigate procurement — providing quantitative benchmarks and qualitative depth on workflow efficiency and pain points.

Measures how customers react to an upcoming or recent price increase — their acceptance thresholds, likelihood to churn, need to re-justify the value, and preferences for how the change is communicated. An AI follow-up digs into the specific reasoning behind each customer's churn risk score, surfacing what would actually change their mind versus what they say in the closed-ended scales.

Tracks how GLP-1 medication-driven weight change is reshaping clothing size, shopping frequency, spend allocation, and what people do with ill-fitting garments. Built for apparel and resale researchers, with an AI follow-up interview that digs into the emotional and financial decisions behind buying new versus altering or holding onto old clothes.

Measures unaided recall and recognition accuracy of brand logos, color palettes, and taglines to benchmark brand asset attribution strength across audience segments.

Groups your customers into meaningful segments by combining behavioral usage patterns, need-based attitudes, purchase-driver trade-offs, and demographics. Built for marketers and researchers building or refreshing a segmentation model. The AI follow-up interview digs into the 'why' behind each respondent's top purchase driver so segments are grounded in real reasoning, not just survey scores.

사람들이 은퇴에 대해 얼마나 준비되어 있다고 느끼는지 — 저축 적정성에 대한 인식, 연금 제도 작동 방식에 대한 이해, 그리고 조언을 구하는 곳(또는 구하지 않는 곳) — 을 측정합니다. 금융 서비스, 복리후생, 자산관리 팀을 위해 설계되었습니다. AI 후속 질문은 응답자의 자신감 점수 이면에 있는 이유를 탐색하여, 일반적인 만족도 점수로는 놓치는 구체적인 지식 격차와 충족되지 않은 조언 니즈를 드러냅니다.

Measures whether a celebrity or influencer endorsement actually transfers credibility to a brand — covering perceived authenticity, trustworthiness, and purchase influence — with an AI follow-up that unpacks why the pairing does or doesn't feel believable to this specific respondent. Built for brand, marketing, and partnerships teams testing a specific spokesperson or creator campaign.

Classifies product features into Must-Be, Attractive, One-Dimensional, and Indifferent categories using the Kano model methodology. Designed for product teams seeking to prioritize their roadmap based on how features drive customer satisfaction.

Measures advertising recall, brand awareness (unaided and aided), consideration lift, and brand preference across major money and payment apps. Designed for market researchers evaluating fintech campaign ROI.

패스트푸드 드라이브스루 이용 시의 속도, 정확성, 직원 친절도, 전반적인 만족도를 측정하여 핵심 경험 요인과 개선 우선순위를 파악합니다.

Measures how comfortable people are letting an AI agent renew, cancel, reorder, or renegotiate their subscriptions on their own — and what safeguards would make that trust possible. Built for product teams evaluating agentic commerce features, with an AI follow-up that digs into why trust differs by task type and what a bad experience would look like.

스타트업 창업자와 운영자가 직면하는 가장 시급한 운영상의 과제를 파악하고 순위를 매기며, 빈도와 심각도, 지불 의향을 측정하여 제품 로드맵 및 가격 책정 결정에 정보를 제공합니다.

Explores how Gen Z respondents use AI chatbots and assistants alongside search engines, social feeds, and influencers to find and vet products — including trust levels, time allocation, and what actually drives a purchase. An AI follow-up interview reconstructs a real recent AI-assisted shopping moment instead of relying on self-reported habits.