Survey Data → ChatGPT Sentiment Analysis → Airtable Action Items
Process customer feedback surveys through AI analysis and automatically create prioritized action items with sentiment scoring and categorization.
Workflow Steps
Google Forms
Export survey responses
Collect your survey data from Google Forms, Typeform, or similar tool. Export responses to a CSV file containing all open-ended feedback, ratings, and demographic data. Ensure you have at least 20-50 responses for meaningful analysis.
ChatGPT
Perform sentiment and thematic analysis
Upload the survey data to ChatGPT and request sentiment analysis (positive/negative/neutral scores), theme identification, and priority ranking. Ask it to categorize feedback into buckets like 'Product Issues,' 'Service Complaints,' 'Feature Requests,' etc., and assign urgency levels.
ChatGPT
Generate actionable recommendations
Have ChatGPT create specific, actionable items based on the analysis. Request output in a structured format with columns for: Issue Category, Sentiment Score, Frequency, Recommended Action, Priority Level, and Assigned Team. Ask for both quick wins and long-term strategic recommendations.
Airtable
Create tracking database
Import ChatGPT's structured recommendations into Airtable. Set up views filtered by priority and category, add status tracking fields (Not Started/In Progress/Complete), and assign team members. Create automated notifications for high-priority items and set up progress tracking.
Workflow Flow
Step 1
Google Forms
Export survey responses
Step 2
ChatGPT
Perform sentiment and thematic analysis
Step 3
ChatGPT
Generate actionable recommendations
Step 4
Airtable
Create tracking database
Why This Works
ChatGPT's natural language processing excels at sentiment analysis and theme extraction from unstructured feedback, while Airtable provides the project management structure needed to ensure insights become actions.
Best For
Customer success teams, product managers, or business owners who need to quickly process and act on large volumes of customer feedback
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