시장 조사

Tests reaction to a new product or service concept — appeal, differentiation, purchase intent, and an optimal price range — for teams deciding whether to build or launch. An AI follow-up interview digs into what would make respondents actually buy or walk away.

For consumer packaged goods and product teams running in-home or trial-use tests. Captures actual usage behavior, attribute-level ratings, purchase intent, and what matters most to improve — with an AI follow-up interview that reconstructs a specific moment of use to explain the ratings behind it.

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 where budget-pressured shoppers trade down to store brands and where they still splurge on premium products, using a best-worst trade-off exercise and a budget-allocation task to map real priorities. An AI follow-up interview reconstructs the last splurge purchase to uncover the emotional vs. practical logic behind when 'worth it' actually wins. Built for CPG, retail, and pricing teams.

Measures how much shoppers trust functional food and beverage claims around fiber, protein, and gut health/probiotics, what evidence makes a claim believable, and where skepticism creeps in. An AI follow-up interview digs into a specific claim the respondent doubted or trusted and reconstructs what actually changed their mind. Built for brand, insights, and regulatory teams evaluating label and marketing language.

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.

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.

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.

Measures how shoppers react when a familiar food product is reformulated with added protein or fiber claims — covering purchase intent, trust in the new claims, taste expectations, and price sensitivity. Built for brand and innovation teams testing packaging or recipe changes, with an AI follow-up that digs into the real tension between health claims and taste concerns behind the numbers.

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 how GLP-1 medications (like Ozempic, Wegovy, or Zepbound) are reshaping household grocery and snack shopping — spending shifts, category cuts, and snack decision factors. Built for CPG and retail researchers, with an AI follow-up interview that reconstructs a real recent shopping trip instead of relying on general impressions.

Captures how listeners actually consume your podcast — ideal episode length, topic preferences, ad tolerance, and what would push them to subscribe or share it — with an AI follow-up that digs into the real reason behind their loyalty score instead of a generic satisfaction number.

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.

Benchmarks how customers and prospects rate you against specific named competitors on the criteria that actually drive their choice — combining a max-diff prioritization, a head-to-head perception battery, and an AI follow-up that digs into the exact moment a competitor won or nearly won the deal.

일련의 로고 또는 시각적 아이덴티티 옵션이 인지도, 브랜드 적합성, 감정적 톤, 그리고 일대일 선호도 측면에서 어떻게 평가받는지 테스트합니다. 로고 후보군을 비교하거나 리디자인을 평가하는 브랜드 및 마케팅 팀을 위해 제작되었으며, 단순히 순위를 기록하는 것을 넘어 우승 선택을 이끈 구체적인 시각적 단서를 파고드는 AI 후속 질문을 포함합니다.

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.

Reconstructs the full journey behind a considered purchase — how customers first discovered the product, which sources shaped their decision, what nearly stopped them, and how confident they feel now. An AI follow-up rebuilds the actual sequence of events around the moment of hesitation, not just the tidy summary customers give afterward.

고객이 어떤 기능이 함께 묶이기를 원하는지, 각 기능 범주가 얼마나 큰 가치를 더하는지, 그리고 기능이 개별 판매될 경우 얼마를 지불할 의향이 있는지를 측정합니다. 계층형 패키지를 설계하는 제품 및 가격 책정 팀을 위해 제작되었습니다. AI 후속 질문은 순위 자체에서 멈추지 않고 응답자가 최상위로 순위를 매긴 번들 뒤에 숨은 이유를 파헤칩니다.

Measures how deal-driven your customers really are: what triggers a discount-motivated purchase, whether frequent promotions erode brand perception, and how willing shoppers are to pay full price. An AI follow-up reconstructs the last purchase decision they made under a discount to separate genuine price sensitivity from habit.

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.