Turning Unstructured Feedback into Actionable Insights
You have 1,000 open-ended text responses. Now what? How to use AI to tag, cluster, and extract value from the chaos of unstructured data.
Structured data (1-5 star ratings) is easy to analyze but low in value. Unstructured data (open text, transcripts) is high in value but a nightmare to analyze.
If you launch a survey and get 500 text responses, you usually:
- Read the first 20.
- Skim the rest.
- Pick a few quotes that support what you already believed.
- Ignore the rest.
This is "Data Waste." Here is how to fix it.
The Taxonomy Approach
Before AI, you had to build a "codebook."
- Tag:
Pricing - Tag:
UX - Tag:
Feature Request
You would manually tag every row. It took days.
The AI Clustering Approach
Modern LLMs (Large Language Models) excel at pattern matching. You can feed 1,000 responses into Fieldrun and ask:
"Cluster these responses into the top 5 themes, and provide a representative quote for each."
Example Output:
- Theme: Onboarding Friction (34%)
- Quote: "I gave up after the second step because I didn't have my API key ready."
- Theme: Pricing Confusion (22%)
- Quote: "I don't know the difference between Pro and Enterprise."
- Theme: Mobile App Crash (15%)
- Quote: "It closes every time I try to upload a photo."
Sentiment Analysis 2.0
Old sentiment analysis just gave you "Positive" or "Negative." New AI analysis gives you Aspect-Based Sentiment.
- "The app is beautiful, but it's too expensive."
- UI Design: Positive
- Pricing: Negative
This granularity allows you to say: "Users love our product (score 4.5), but hate our billing system (score 1.2)."
Conclusion
Don't fear open-ended questions. With AI, you can process 5,000 text answers faster than you can process 50. Ask the broad questions, and let the machine handle the sorting.