금융·보험

비수탁형 지갑 설정 과정, 시드 문구 복구, 네트워크 수수료 투명성에 대한 사용자의 이해도, 자신감, 선호도를 평가하여 UX 마찰 지점을 파악하고 이탈을 줄이고자 합니다.

Measures financial confidence, budgeting habits, and money stress for everyday consumers, with an AI follow-up that digs into the real story behind their biggest financial worry instead of a generic rating.

Measures ease, clarity, trust, and drop-off points across KYC/AML identity verification flows. Designed for compliance, product, and UX teams seeking to reduce verification abandonment and improve onboarding completion rates.

Evaluates self-custody wallet onboarding friction, seed phrase recovery confidence, and on-chain transaction clarity to identify UX improvements. Designed for crypto wallet product teams targeting active wallet users.

Measures how clients rate your firm's advice quality, deadline reliability, fee transparency, and year-round value beyond tax season. Built for accounting and bookkeeping firms running annual or post-engagement check-ins, with an AI follow-up that digs into the specific interaction behind a client's satisfaction score instead of generic praise or complaints.

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 how households actually track spending, allocate income, and cope with shortfalls — not just what they intend to do. Includes a spending trade-off exercise, a priority ranking of financial goals, and an AI follow-up interview that reconstructs the last time the budget broke down and what would have prevented it.

Measures Buy Now, Pay Later adoption patterns, provider preferences, spending thresholds, trust drivers, and consumer concerns. Designed for fintech product teams, lenders, and market researchers seeking segmentation and attitudinal insights from adult consumers.

Captures how people actually budget, save, and handle debt month to month, how much financial stress they carry, and how they prioritize competing money goals. Built for banks, fintech apps, and financial coaches benchmarking real behavior instead of stated intentions. An AI follow-up interview digs into the story behind their stress level and top goal — what's blocking progress and what's actually working.

Measures cardholder satisfaction, effort, and friction across the end-to-end dispute lifecycle—from initiation through resolution—to identify process improvements and support retention.

Measures new and early-stage crypto users' comprehension of gas fees, blockchain networks, and wallet/dApp permissions to identify knowledge gaps and UX friction points in Web3 onboarding flows.

Measures how customers actually use your mobile banking app — login, transfers, bill pay, deposits — and where friction shows up, paired with a prioritization exercise for what to fix next. An AI follow-up interview reconstructs a real recent session in detail, surfacing the exact moment things broke down instead of vague complaints.