가격·패키지

A choice-based conjoint that shows respondents realistic product profiles built from attributes and levels — plan tier, price, support, contract terms — and measures which combinations win. AI follow-ups capture the reasoning behind choices that a choice matrix alone can't explain.

Tests whether prospects can correctly interpret your plan names, pick the tier that actually fits them, and understand what changes when they upgrade. Built for pricing and product teams evaluating a new plan lineup before launch, with an AI follow-up that digs into exactly which name or feature line caused hesitation or a wrong guess.

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 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.

Maps how a target market perceives a product or service's price using the four classic price-sensitivity questions, then layers in purchase intent, feature value trade-offs, and a budget allocation exercise. An AI follow-up interview digs into the reasoning behind each respondent's expensive/cheap thresholds so pricing teams get the numbers and the 'why' behind them.

Measures willingness-to-pay thresholds for an everyday consumer product using the Van Westendorp Price Sensitivity Meter with Newton-Miller-Smith purchase intent extensions. Ideal for pricing teams seeking acceptable price ranges and optimal price points.

Measures prospect perceptions of pricing page clarity, perceived value fairness, and decision readiness to identify friction points and conversion barriers on SaaS pricing and packaging pages.

Measures how online shoppers perceive, interpret, and respond to prices displayed in foreign currencies, evaluating display format clarity, perceived fairness, trust, and behavioral impact to guide global ecommerce pricing UX.

차량 공유, 배달, 여행, 소매 전반에 걸친 동적 가격 및 급증 가격에 대한 소비자 태도를 측정하고, 주요 공정성 요인, 투명성 선호도, 수용 가능한 가격 임계값을 파악하여 가격 전략에 반영합니다.

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.

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 acceptable price ranges, perceived value, and purchase intent for consumer products using the Van Westendorp Price Sensitivity Meter. Designed for product managers and pricing researchers evaluating new or existing product pricing strategies.

Measures price fairness perceptions, value-for-money judgments, and maximum willingness to pay among recent price-exposed prospects and customers. Ideal for pricing optimization, launch pricing validation, and competitive positioning research.

Measures consumer price sensitivity using Van Westendorp methodology, feature prioritization, and billing preferences for fintech subscription plans. Designed for product and pricing teams evaluating consumer willingness to pay across global markets.

Measures how users perceive paywall messaging, timing, and pricing fairness. Use with digital content consumers to identify UX friction points and inform subscription conversion strategy.

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 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.