What is Gradsgate?
Gradsgate is a white-labeled alumni management software designed to help educational institutions build and maintain alumni databases while offering effective communication and engagement tools. The platform provides a branded social network where alumni can connect, message, post, like, and share job opportunities and events from a central hub.
It features automatic data updating that pulls information from social networks and public pages, eliminating manual efforts. The system includes an alumni directory with advanced filtering, email marketing integrated with Sendgrid for guaranteed delivery, surveys with analytical reporting, and fundraising tools with donor-friendly pages. Additionally, it offers event management, job boards, and discount/offer capabilities to enhance alumni engagement.
Features
- Automatic Data Updating: Updates alumni information automatically from social networks and public pages
- Branded Social Network: Facilitates alumni communication through connecting, messaging, posting, and job/event announcements
- Alumni Directory: Online listing of alumni with filters based on degree, major, graduation year, and more
- Email Marketing: Reach segmented alumni with campaigns integrated with Sendgrid for guaranteed delivery
- Surveys: Create and analyze surveys with reports in PDF and Excel formats
- Fundraising/Donations: Create donor-friendly giving pages for quick alumni donations
- Events Management: Manage and announce events directly through the portal
- Job Board: Post employment opportunities for students and graduates
Use Cases
- Universities managing alumni databases and engagement
- Colleges strengthening alumni connections through targeted communication
- Educational institutions automating alumni data updates
- Schools facilitating alumni networking and job opportunities
- Universities conducting alumni surveys and fundraising campaigns
Related Queries
Helpful for people in the following professions
Gradsgate Uptime Monitor
Average Uptime
100%
Average Response Time
379.67 ms
