비영리·공공

Measures how members perceive the value of their association benefits, how likely they are to renew and recommend membership, and which offerings deserve more investment — with an AI follow-up that digs into the real reasons behind a member's renewal score instead of a generic satisfaction rating. Built for membership and engagement teams running annual or renewal-cycle check-ins.

Measures how satisfied volunteers are with their role, training, recognition, and coordination — and what would keep them coming back. Includes a best-worst trade-off to prioritize fixes and an AI follow-up interview that digs into the real reason behind a volunteer's satisfaction or hesitation to continue. Built for volunteer coordinators and nonprofit program managers.

Captures new volunteer applicants' availability, skills, and cause interests so nonprofits can match them to the right role fast — with an AI follow-up that surfaces the personal motivation behind their sign-up instead of a generic checkbox reason.

Measures how connected residents feel to their local community, how often they engage with neighbors and local institutions, and what barriers keep people from participating more, with an AI follow-up that digs into a specific recent moment of connection or isolation.

Assesses how volunteers experience training, communication, recognition, and support, and how likely they are to keep volunteering or recommend the program. An AI follow-up interview digs into the specific experience behind their satisfaction score so program staff know exactly what to fix or protect.

신규 회원이 가입, 오리엔테이션, 그리고 조직 초기 참여 과정을 어떻게 경험하는지, 그리고 무엇이 재가입 또는 참여 확대를 예측하는지 측정합니다. AI 후속 인터뷰는 각 회원의 재가입 가능성 뒤에 숨은 실제 이유를 깊이 파고들어, 폐쇄형 질문이 놓치는 마찰 지점을 드러냅니다. 비영리단체, 협회, 시민단체를 위해 설계되었습니다.

Measures how volunteers experience onboarding, coordinator support, and the meaningfulness of their assignments at a community service agency, plus what would make them volunteer more often. An AI follow-up interview digs into the specific moment that shaped their satisfaction or hesitation, surfacing concrete fixes a generic rating scale would miss.

Captures how new and current volunteers discover an opportunity, decide to sign up, and experience the application and onboarding process — with an AI follow-up that digs into the real reason behind their strongest motivator or biggest friction point. Built for nonprofits and volunteer coordinators optimizing recruitment funnels and retention.

Measures how satisfied members are with your nonprofit's value, communication, and mission impact, plus renewal intent and which benefits matter most. Built for membership and development teams, with an AI follow-up interview that digs into the specific reasons behind each member's renewal decision so you know what to fix or protect before dues are due.

신규 자원봉사자가 최초의 관심에서 첫 봉사 활동까지 얼마나 수월하게 이동하는지를 측정합니다 — 가입 과정의 마찰, 안내의 명확성, 동기 부여를 비영리 자원봉사 코디네이터를 위해 살펴봅니다. AI 후속 인터뷰는 등록이 지연되거나 순조로웠던 구체적인 순간을 재구성하여, 폐쇄형 평가만으로는 놓칠 수 있는 개선점을 드러냅니다.

Maps the professional skills, interests, and time capacity of prospective or current volunteers so nonprofits can match people to the right skills-based roles. An AI follow-up interview digs into why a volunteer's top skill area excites them and what would actually get in the way of them signing up.

Measures why donors give, what shapes their satisfaction with the giving experience, and how likely they are to give again — covering motivations, program allocation preferences, and price sensitivity for asks. An AI follow-up interview reconstructs the real story behind each donor's likelihood-to-give-again score.

Screens prospective volunteers on motivation, availability, skills, and role fit, using a best-worst trade-off question to surface which roles they actually want most (not just tolerate). An AI follow-up interview digs into the story behind their top motivation and preferred role so coordinators can place new volunteers well from day one.

Gauges how members feel about a community or nonprofit fitness center — value for cost, facility quality, program fit, and likelihood to renew — for recreation departments, YMCAs, and community centers tracking retention. An AI follow-up interview digs into the real reason behind a member's renewal decision so staff can act on it, not just the number.

Gauges how satisfied community members are with an organization's programs, services, and communication, and what would most improve their experience. Built for nonprofits, civic groups, and neighborhood associations; the AI follow-up interview digs into the specific experience behind each person's satisfaction rating and top priority instead of settling for a number.

Measures how members perceive the value of their dues, where pricing tolerance breaks down, and which benefits justify the fee — for associations and nonprofits weighing a rate change. An AI follow-up interview digs into the reasoning behind each member's value rating instead of just the number.

Captures how donors feel about your communication, transparency, and impact reporting, plus what would make them give again — with an AI follow-up that digs into the real reasons behind their loyalty or hesitation instead of just a score. Built for development and fundraising teams running annual or post-campaign donor check-ins.

Measures how members experience and value your online fashion community — participation habits, sense of belonging, content quality, and moderation trust — with an AI follow-up that digs into the specific moment a member felt most (or least) connected. Built for community managers and nonprofit-style membership programs running fashion forums, swap groups, or style-sharing platforms.

신규 신청자에게 회원 가입 신청 절차가 얼마나 명확하고 빠르며 동기를 부여하는지 측정합니다 — 협회, 노동조합, 클럽, 시민단체를 위한 설문입니다. AI 후속 인터뷰를 통해 신청자가 실제로 가입한 이유와 신청서 작성을 거의 포기할 뻔한 요인을 재구성합니다.

Explores how, why, and how much people give to charitable causes — covering giving channels, trust, cause priorities, and barriers to giving more. An AI follow-up interview digs into the real story behind a donor's most recent or largest gift, surfacing motivations and friction that closed-ended questions miss.