How to Analyze Customer Feedback and Reviews in Google Sheets (Sentiment Analysis Without Code)

There's a spreadsheet open on your screen. It has 847 rows of customer feedback—from surveys, app store reviews, support tickets, social mentions, you name it. Someone on the product team asked for "a breakdown of what customers are saying."

Great. So now you get to read 847 comments and somehow summarize them into something actionable.

Except you don't. Not anymore, anyway.

The Problem With Feedback at Scale

Small amounts of feedback are easy to handle. You read a few reviews, spot patterns, maybe tag a handful of comments manually. But at some point—maybe 50 responses, maybe 200—the volume crosses a threshold where manual analysis becomes impractical.

And yet, that's exactly when feedback becomes most valuable. A hundred reviews might contain noise, but a thousand reviews contain real patterns. The customers who love your shipping speed, the ones frustrated by your checkout flow, the segment that keeps asking for a feature you haven't built yet.

All of that insight is sitting in your spreadsheet. You just can't get to it without reading every single row.

What You Actually Need From Feedback Analysis

When someone asks for "feedback analysis," they usually want a few things:

• Sentiment: Is this positive, negative, or neutral? What's the overall ratio?
• Topics: What are people talking about? Pricing? Product quality? Customer service?
• Urgency: Which issues need immediate attention vs. long-term consideration?
• Patterns: Are there clusters of similar complaints or praise?

Traditionally, getting this required either expensive enterprise tools (think Qualtrics, Medallia) or a lot of manual work with spreadsheet filters. Neither option is great for a team that just needs to understand their customer feedback without a six-figure software budget.

Using AI for Sentiment and Topic Categorization

Here's where things have gotten a lot better recently. AI can read text and categorize it—accurately—into whatever buckets you define. Sentiment, topic, urgency, intent, whatever categories matter for your analysis.

I built Categorize AI to do this directly in Google Sheets. Instead of exporting data, uploading it somewhere, and importing results, you just run the categorization right where your data lives.

How It Works

Real Examples: Before and After

Let me show you what this looks like with actual feedback:

Notice how the AI picks up on multiple topics in a single review. That first comment is about both shipping AND product quality. That nuance is important for understanding what's actually driving satisfaction.

Common Category Sets for Feedback Analysis

Here are some category configurations that work well for different types of feedback:

For Product Reviews

For Support Tickets

For NPS Verbatims

Multi-Layer Analysis

Here's something that's hard to do manually but easy with AI: running multiple categorization passes on the same data.

First pass: Sentiment (Positive/Negative/Neutral). Second pass: Topic (Product/Service/Pricing/Delivery). Third pass: Urgency or Priority.

Now you can cross-tabulate. "Show me all Negative feedback about Delivery" or "What topics are mentioned most in Positive reviews?" These are the insights that actually drive decisions.

What About Accuracy?

Fair question. AI sentiment analysis isn't perfect—nothing is when dealing with the ambiguity of human language. But modern models are surprisingly good, especially for clear-cut cases.

The key is that even at 85-90% accuracy, you're getting a huge head start. Instead of reading 1,000 comments, you're spot-checking maybe 100-150. And you can always manually correct edge cases.

Saving Category Sets for Recurring Analysis

If you run the same type of analysis regularly—quarterly NPS surveys, weekly support ticket reviews, monthly product feedback—you can save your category configurations in Categorize AI and reuse them.

Same categories, consistent results, no setup time. Just load your new data and run.

Getting Started

If you want to try this with your own feedback data:

1. Install Categorize AI from the Google Workspace Marketplace
2. Open your spreadsheet with customer feedback
3. Set up your first category set (start with simple sentiment: Positive, Negative, Neutral)
4. Select your data and run
5. Add more category layers as needed (topic, urgency, etc.)

There's a free trial, so you can test it with your actual feedback before deciding if it's worth keeping.

Beyond Sentiment: Other Feedback Analysis Use Cases

Once you get comfortable with sentiment analysis, there are other ways to slice feedback data:

• Feature requests: Categorize by product area or capability
• Competitor mentions: Flag feedback that mentions competitors
• Churn risk: Identify feedback that signals a customer might leave
• Testimonial candidates: Find highly positive reviews suitable for marketing
• Product issues: Categorize bug reports or quality complaints by severity

The Bottom Line

Customer feedback is one of the most valuable data sources any business has. But only if you can actually analyze it. Reading thousands of comments manually isn't realistic. Ignoring the data entirely is a waste.

AI-powered categorization hits the sweet spot: fast enough to be practical, accurate enough to be useful, and simple enough that you don't need a data science team to run it.

Next time someone drops a thousand-row spreadsheet of survey responses on your desk, you'll know what to do. And it won't take all week.