Customer Feedback Analysis
Analyze customer feedback at scale using AI to identify themes and sentiment, organize insights in a structured database, and trigger automated responses for critical issues.
Workflow Steps
ChatGPT
Analyze Sentiment and Themes
Feed batches of customer feedback from surveys, reviews, and support tickets into ChatGPT with a prompt that performs sentiment analysis, categorizes feedback into themes like pricing, usability, and feature requests, and assigns a priority score. Output the results as structured JSON with consistent field names for downstream processing.
Airtable
Store and Visualize
Use the Airtable API to insert each analyzed feedback item into a base with fields for source, sentiment, theme, priority, original text, and AI summary. Create gallery and kanban views grouped by theme and filtered by sentiment. Build summary dashboards using Airtable's chart and pivot table extensions.
Zapier
Trigger Automated Responses
Set up Zapier triggers that watch the Airtable base for new entries with negative sentiment or high-priority scores. Route critical feedback to the appropriate team via Slack or email. For product feature requests, automatically create entries in your product backlog. Send acknowledgment emails to customers who left negative feedback.
Workflow Flow
Step 1
ChatGPT
Analyze Sentiment and Themes
Step 2
Airtable
Store and Visualize
Step 3
Zapier
Trigger Automated Responses
Why This Works
ChatGPT can process and categorize feedback far faster than manual review, Airtable provides the flexible structured storage and visualization layer, and Zapier ensures that insights translate into timely actions rather than sitting in a database.
Best For
Product managers, customer experience leaders, and operations teams who receive hundreds of feedback data points monthly and need to identify patterns and respond quickly to issues.
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