제품·UX

A structured weekly feedback instrument for beta testers that captures reliability issues, perceived performance, task-oriented usability, and improvement priorities to guide QA and product teams.

Evaluate candidate product or feature names on clarity, appeal, and distinctiveness to identify the strongest option. Designed for product and UX teams conducting comparative name testing with target users.

A research-grade monadic concept testing instrument measuring overall appeal, message clarity, uniqueness, relevance, credibility, purchase intent, and comparative advantage. Suitable for early-stage concept screening with consumer or B2B audiences.

Diagnoses why power users complete or abandon purchases, benchmarks competitor performance across key dimensions, and surfaces priority product gaps to guide retention and conversion strategy.

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.

Evaluates three banner copy variants on perceived clarity, tonal fit, and call-to-action effectiveness to identify the highest-performing message for Pro feature promotion.

Collects structured customer input on feature preferences scored by impact, urgency, and usage frequency to inform product roadmap prioritization decisions.

Measures how well a makeup product performs in real routines — application, wear time, and repurchase intent — plus what actually drives the buy decision. An AI follow-up interview digs into the specific moment a product over- or under-delivered, surfacing details closed questions miss.

A structured survey for active product users to evaluate current integration usage, identify unmet needs, and prioritize capabilities — providing actionable data for roadmap planning.

Helps product and platform teams rank integration partners by business impact, quantify switching costs, and identify the biggest blockers to partner changes. Designed for integration decision-makers and technical stakeholders.

Evaluates documentation completeness, example quality, and information findability from the user's perspective. Designed for product and developer-experience teams seeking prioritized, actionable improvement signals.

Measures user perceptions of progressive feature releases across clarity, control, communication, and confidence. Designed for product teams seeking to optimize rollout processes and communication strategies.

베타 테스터의 온보딩 편의성, 피드백 루프 품질, 인지된 제품 가치를 측정하여 정식 출시(GA) 전 개선 사항과 도입 우선순위를 안내합니다.

Measures how users across world regions interpret and prefer date, time, number, and currency display formats, providing product and UX teams with actionable localization insights to reduce ambiguity and formatting errors.

Evaluates software error message clarity, perceived responsibility, user confidence, and recovery experience to optimize UX copy, reduce support volume, and improve trust.

A structured stakeholder assessment that measures launch confidence, track-level readiness, risk prioritization, and operational preparedness to support data-driven go/no-go decisions.

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.

Measures product essentiality using the Sean Ellis PMF framework alongside competitive switching risk and perceived differentiation to guide product strategy and roadmap prioritization.

Captures accessibility barriers and WCAG compliance gaps reported by assistive technology users during real digital product sessions. Designed for UX and accessibility teams seeking to prioritize remediation based on user-reported severity and impact.

An internal diagnostic survey for teams that run or consume A/B tests, measuring trust in experiment results, identifying sources of flakiness, and prioritizing process and tooling improvements.