고객 경험

Measures the concrete business or life outcomes customers have achieved since adopting your product or service — not just satisfaction, but what actually changed. Includes an AI follow-up interview that reconstructs a specific before-and-after story behind the numbers, giving you case-study-ready evidence of real impact.

Measures article findability, clarity, completeness, and task-completion outcomes for knowledge base users. Use to identify content gaps and prioritize help center improvements.

지원 상호작용 직후 고객의 노력, 만족도, 충성도를 측정합니다. 3분 이내에 실행 가능한 CSAT, CES, NPS 벤치마크를 확보하고자 하는 CX 팀을 위해 설계되었습니다.

Measures customer satisfaction, perceived fairness, and friction points across the retail return and refund process to inform policy improvements and reduce post-purchase churn.

전반적인 만족도, 추천 의향, 특정 접점에 대한 만족도를 파악하는 빠른 2~3분 체크인입니다. 단순한 점수뿐만 아니라 각 점수 뒤에 숨은 진짜 이유를 파고드는 AI 후속 질문이 포함되어 있습니다. 구매 후, 지원 후 또는 분기별 관계 펄스용으로 설계되었습니다.

Tracks how often and through which channels customers actively engage with your brand, what's pulling them in or pushing them away, and which levers would deepen engagement — with an AI follow-up that digs into the real reasons behind a customer's current engagement level.

A recurring pulse survey for active SaaS product users that measures satisfaction, captures bugs, and prioritizes feature improvements over a rolling 14-day window.

Captures structured cancellation feedback from recently churned customers to identify primary churn drivers, unmet value expectations, and win-back levers for product and retention teams.

Evaluates a recent online support interaction — channel used, resolution, effort, and satisfaction — for teams running live chat, email, or chatbot support. An AI follow-up interview digs into what actually happened during the contact so low scores come with a specific, fixable story instead of just a number.

A repeatable diary entry participants complete each time they use your product in real life: a photo of the moment, what they were trying to do, and what got in the way — with an AI probe on the day's friction. Run it daily or weekly to see usage where it actually happens, not in a lab.

지원 상담 직후의 만족도, 노력, 해결 품질을 파악하고, 좋은 서비스 경험에서 무엇이 가장 중요한지를 포착합니다. AI 후속 인터뷰가 일이 잘 풀렸거나 틀어진 구체적인 순간을 파고들어, 평점만으로는 설명할 수 없는 세부 사항을 드러냅니다. 지원, CX, 운영 팀을 위해 설계되었습니다.

Measures how easily customers or employees got their issue resolved through your help desk — first-contact resolution, effort, agent quality — for one specific support interaction. An AI follow-up interview reconstructs exactly what caused friction (or what went smoothly) so support leaders know what to fix, not just how people rated it.

Find your biggest opportunities by measuring the same attributes twice — how much each matters, and how well you deliver it today. The gaps become your priority list, and slider matrices keep it fast enough that people actually finish. AI follow-ups dig into the biggest gap each respondent reports.

어떤 고객이 사례 연구, 추천사, 또는 비디오 스토리로 만들 만한 성과를 가지고 있는지 파악하고, 소개될 의향이 있는지 측정합니다. AI 후속 인터뷰는 일반적인 칭찬을 넘어, 고객 스토리를 낯선 사람에게도 설득력 있게 만드는 구체적인 전후 상황 — 수치, 전환점, 증거 — 를 재구성합니다.

Captures how customers actually reach you for help, how easy and effective that contact was, and which channels they'd pick again — with an AI follow-up that reconstructs their most recent support interaction in detail instead of relying on vague satisfaction ratings.

Measures loyalty program members' satisfaction with reward relevance, redemption ease, and perceived value relative to competitors to identify retention drivers and improvement priorities.

Measures how clients experienced your firm across communication clarity, responsiveness, billing transparency, and case outcome perception. Built for law firms and legal ops teams doing post-matter or annual relationship check-ins, with an AI follow-up that digs into the gap between what clients expected and what actually happened — especially around outcomes and any billing surprises.

Measures how well a recent technical support interaction resolved the customer's issue, how much effort it took, and how the agent performed — built for support and CX teams tracking ticket quality beyond a single CSAT number. An AI follow-up interview reconstructs what actually happened on the ticket instead of just the rating given.

Measures the usability of the subscription cancellation process, perceived fairness of retention offers, and key churn drivers. Designed for current or recently canceled subscribers to inform win-back strategy and CX improvements.

Measures how customers experience estimate transparency, timeliness, and work quality at your service department or repair shop, and how those add up to trust. An AI follow-up digs into the specific moment — often around cost or communication — where trust was won or lost.