Qualitative data is a goldmine. Open-ended survey responses, donor notes, petition comments, program logs, support tickets all contain rich, valuable information about the individuals that organizations care most about. The problem is that text data is notoriously hard to use in a program and at scale. If you do decide to manually code the text, it ends up being slow, inconsistent, and hard to re-do if your criteria change.
That’s where AI-powered text classification with LLMs shines. With tools like Civis AI Taxonomer, you can transform free text into consistent, analysis-ready labels and with a human-in-the-loop workflow, analysts can iterate and validate LLM performance in real-time. This allows teams to quantify themes, monitor trends, and make decisions faster.
Learn how labeling workflows are evolving in our overview of transforming the labeling of text data.
Below are 10 practical, real-world categories you can classify with GenAI today.
10 categories, 20 real-world examples
1) Survey Open-Ends (Awareness/Attribution)
- Input: “I saw your campaign through a friend’s Facebook post.” → Classified as: Social media
- Input: “I read about it in my daughter’s school newsletter.” → Classified as: Community partner
Why it matters: See which channels actually drive discovery.
2) Donor Call Notes (Giving Motivation & Readiness)
- Input: “I’d love to learn more about supporting a scholarship fund for first-generation students.” → Classified as: Potential major donor
- Input: “I can’t give this year because of unexpected medical expenses.” → Classified as: Financial hardship
Why it matters: Prioritize outreach and tailor stewardship.
3) Petition Comments (Issue Priorities)
- Input: “Affordable housing is the number one issue facing my community.” → Classified as: Affordable housing
- Input: “I worry about sending my kids to the park because it doesn’t feel safe at night.” → Classified as: Public safety
Why it matters: Quantify what supporters care about most.
4) Volunteer Field Notes (Engagement Drivers)
- Input: “I was food insecure in college and know what it feels like to worry about your next meal.” → Classified as: Personal experiences
- Input: “This gives me a sense of community after retiring and losing my work connections.” → Classified as: Social and community
Why it matters: Design programs that match volunteers’ motivations.
5) News Source Catalogs (Outlet Classification)
- Input: “TechRadar – The latest technology news and reviews…” → Classified as: Non Major US News
- Input: “Associated Press – …in-depth coverage on international, politics, lifestyle…” → Classified as: Major US News
Why it matters: Clean media datasets for study, monitoring, and outreach.
6) Survey Open-Ends (Churn/Decreased Engagement Reasons)
- Input: “We moved and it’s too difficult to get into the city.” → Classified as: Proximity
Input: “I lost my job and can no longer afford the membership.” → Classified as: Economic hardship
Why it matters: Identify top churn drivers and intervene earlier.
7) Chatbot Interactions (Civic Education & Services)
- Input: “I used to be registered to vote in Pennsylvania, I moved to Virginia. How can I change my registration?” → Classified as: Voter registration
- Input: “I lost my driver’s license, do I need identification to vote?” → Classified as: Identification requirements
Why it matters: Optimize FAQs, triage flows, and service guidance.
8) Program Implementation Notes (Operational Themes)
- Input: “Volunteers did not understand how to support the program; more training and onboarding is needed.” → Classified as: Training
- Input: “iPads onsite weren’t configured properly—glitchy, low battery. We switched to paper.” → Classified as: Technology
Why it matters: Spot systemic blockers and prioritize fixes.
9) Support Tickets (CX Insights & Roadmap Signals)
- Input: “I can’t log in. My two-factor authentication isn’t working.” → Classified as: Access & Authentication
- Input: “It would be great if the platform supported direct exports to Tableau.” → Classified as: Feature Request
Why it matters: Quantify pain points and inform roadmap.
10) News Articles (Content Understanding Beyond Keywords)
- Input: “The Federal Reserve announced today that it will hold interest rates steady, signaling caution amid global uncertainty.” → Classified as: Breaking News
- Input: “Community rallies to rebuild playground destroyed in fire: ‘We’re stronger together,’ says local organizer.” → Classified as: Human Interest
Why it matters: Track tone and purpose.
Turn your qualitative data into decisions
If you’re ready to convert open-ended feedback into structured, defensible insights, see how Civis AI Taxonomer can help.