Auto-Tag Customer Feedback in Google Sheets with AI: A Complete n8n Workflow Guide

Customer feedback is gold, but only if you can make sense of it. When feedback piles up in spreadsheets, manually tagging, categorizing, and analyzing each entry becomes a time sink. You need patterns, sentiments, and emotions extracted quickly. You need automation.

This n8n workflow solves exactly that problem. It automatically pulls unprocessed feedback from Google Sheets, sends it to OpenAI for intelligent tagging and sentiment analysis, then writes the results back to your spreadsheet. No manual work. No missed insights. Just clean, categorized data ready for action.

Prerequisites: What You'll Need Before Starting

Before building this workflow, make sure you have access to these services and credentials set up in n8n:

• Google Sheets API: You'll need a Google account with OAuth2 credentials configured in n8n to read from and write to your spreadsheets.
• OpenAI API: An active OpenAI account with API access. The workflow uses GPT-4o-mini for cost-effective analysis.
• n8n Instance: Either a cloud instance or self-hosted n8n installation where you can create and run workflows.

Your Google Sheet should have two tabs: one called "Feedbacks" where raw customer input lives, and another called "Tags" containing your predefined categories. The Feedbacks sheet needs columns for Status, Sentiment, Tag 1-3, AI Tag 1-2, Primary Emotion, Secondary Emotion, and an Updated Date field.

Key Components: The n8n Nodes That Power This Workflow

This workflow uses a focused set of n8n nodes, each playing a specific role:

• Manual Trigger & Schedule Trigger: Run the workflow on demand or automatically every hour.
• Google Sheets (Read): Fetches allowed tags and new feedback entries that need processing.
• Merge Node: Combines the tags and feedback data streams into a single flow.
• Set Node: Attaches the tags array to each feedback item for processing.
• Split in Batches: Groups feedback into batches of 10 to optimize API calls.
• Code Nodes: Aggregate batch items before AI processing and split results afterward.
• OpenAI Node: The brain of the operation, analyzing feedback and returning structured JSON with tags, sentiment, and emotions.
• Google Sheets (Update): Writes the analysis results back to your spreadsheet.

How to Build the Workflow: Step-by-Step Implementation

Step 1: Set Up Your Triggers and Data Sources

Start by adding both trigger nodes to your canvas. The Manual Trigger lets you test runs instantly, while the Schedule Trigger keeps things running automatically every hour. Both triggers connect to two Google Sheets nodes that run in parallel.

The first Google Sheets node pulls from your "Tags" tab. This is your master list of categories like "Bug," "Feature Request," "Payment," or "UI Issue." The second node fetches from your "Feedbacks" tab, specifically looking for rows where the Status column is empty, meaning they haven't been processed yet.

Step 2: Merge and Prepare Your Data

Use a Merge node set to "Choose Branch" mode to combine the tags and feedback streams. This ensures every feedback item has access to your full tag library. Next, add a Set node that transforms the tags into an array format. This node creates a single "tags" field containing all your allowed categories, making it easy to pass to the AI.

Step 3: Batch Processing for Efficiency

Add a Split in Batches node configured to process 10 items at a time. This is crucial for two reasons: it keeps your OpenAI API costs manageable and prevents timeout errors when dealing with large feedback volumes. After the split, add a Code node that aggregates each batch into a single JSON object containing an array of feedback items and the tags list.

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This aggregation step is important because it allows you to send multiple feedback entries in one API call instead of making individual requests for each piece of feedback. You're essentially saying "here are 10 feedbacks, analyze them all at once."

Step 4: Configure the OpenAI Analysis

This is where the magic happens. Add an OpenAI node and select the GPT-4o-mini model. The system prompt is comprehensive and tells the AI exactly what to do. It instructs the model to analyze each feedback for sentiment on a five-point scale from Very Negative to Very Positive, identify a primary emotion like frustration or satisfaction, optionally detect secondary emotions, and apply up to three tags from your allowed list.

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The prompt includes a critical two-tier tagging system. Tier 1 forces the AI to thoroughly examine your predefined tags first, matching them semantically and thematically, not just by exact keywords. Only if no relevant tag exists does it move to Tier 2, where it generates up to two custom AI tags as a fallback. This ensures consistency in your categorization while still handling edge cases.

Step 5: Process and Write Results Back

After the OpenAI node returns its analysis, add another Code node to split the batch results back into individual items. This node parses the JSON response from OpenAI and creates separate output items for each analyzed feedback, extracting the row number, tags array, sentiment, AI-generated tags, and emotions.

Finally, connect a Google Sheets Update node. Configure it to match rows by row number and update the Status, Sentiment, Tag 1-3, AI Tag 1-2, Primary Emotion, Secondary Emotion, and Updated Date columns. The Status field gets set to "Updated" if tags were found, or "Needs Review" if the AI couldn't confidently categorize the feedback.

Step 6: Loop Back for Continuous Processing

Connect the output of your Google Sheets Update node back to the Split in Batches node. This creates a loop that continues processing until all feedback items in the current batch cycle are complete. The Split in Batches node automatically handles this by checking if there are more items to process.

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Benefits and Real-World Use Cases

This workflow transforms feedback management from a manual chore into an automated insight engine. Product teams can quickly identify trending issues by filtering on specific tags or sentiment scores. Support teams can prioritize responses based on emotion detection, addressing frustrated customers first. Marketing teams can track sentiment over time to measure how product changes affect customer perception.

The multilingual support is particularly powerful. The prompt explicitly tells the AI to analyze feedback in any language natively, including Persian and Farsi. This means global teams can process feedback from different markets without translation steps.

The two-tier tagging system gives you the best of both worlds. You maintain control over your taxonomy through predefined tags, ensuring consistency across your organization. But when feedback touches on new topics you hadn't anticipated, the AI-generated tags flag these emerging themes for you to consider adding to your master list.

Perhaps most importantly, this workflow scales effortlessly. Whether you receive 50 or 5,000 pieces of feedback per month, the automation handles it. The batch processing ensures efficient API usage, and the hourly schedule means your data stays fresh without manual intervention.

Taking It Further

Once you have this workflow running smoothly, consider these enhancements. Add a Slack or email notification node to alert your team when high-priority negative feedback comes in. Create a dashboard using Google Data Studio that connects to your tagged feedback sheet. Set up additional OpenAI nodes to generate response suggestions for common feedback types. The foundation is solid, and the possibilities for extension are endless.

Customer feedback deserves more than a spreadsheet graveyard. With this n8n workflow, every piece of input gets the attention it deserves, automatically categorized, analyzed, and ready to drive decisions. Build it once, and let it run forever.