Prompt: Analyze Customer Reviews for Patterns

Customer reviews contain gold. Patterns in what people love, hate, and wish for tell you exactly what to improve, what to double down on, and what to build next. This prompt analyze customer reviews patterns pulls those patterns out of any volume of reviews.

Your customers already told you what they want. This prompt helps you hear it.

The Prompt

You are a customer insights analyst. Analyze the following batch of customer reviews and identify patterns.

PRODUCT/SERVICE: [what the reviews are about]
REVIEW SOURCE: [Google, Trustpilot, Amazon, internal feedback, etc.]

REVIEWS:
[Paste all reviews here. Include star rating if available.]

ANALYZE FOR:

1. WHAT THEY LOVE (positive patterns)
   - List each recurring positive theme
   - Quote 2-3 verbatim examples for each theme
   - Rank by frequency (most common first)

2. WHAT THEY HATE (negative patterns)
   - List each recurring complaint
   - Quote 2-3 verbatim examples for each
   - Rank by frequency and severity

3. WHAT THEY WISH FOR (feature/improvement requests)
   - List each request or suggestion
   - Note how many reviews mention each

4. LANGUAGE MINING
   - List 10 exact phrases customers use to describe the product/problem/benefit
   - These should be verbatim quotes useful for marketing copy

5. SENTIMENT DISTRIBUTION
   - What percentage are positive, neutral, negative?
   - Any trend over time? (if dates are available)

6. COMPETITIVE MENTIONS
   - Did any reviews mention competitors? Which ones and in what context?

7. RECOMMENDATIONS
   - Based on the patterns: What should we keep doing? What should we fix immediately? What should we build next?

Keep the analysis specific. "Customers like the product" is not useful. "7 of 15 reviews specifically praised the speed of onboarding" is useful.

Where to Get Reviews

Your own platforms: Google Reviews, Trustpilot, product reviews on your website. These are direct feedback.

Competitor reviews: Mine their reviews for language and complaints. Their weaknesses become your positioning opportunities.

Social mentions: Reddit threads, Facebook comments, Twitter replies. These are less structured but often more honest.

Using the Output

The "What They Love" section feeds your marketing. Use the exact language customers use in your ads and landing pages. If 8 out of 20 reviews mention "saves me 10 hours a week," that phrase belongs in your headline.

The "What They Hate" section feeds your product roadmap and operations improvements. Fix the top complaint first.

The "Language Mining" section is pure gold for copywriting. Real customer language converts better than anything you write from scratch.

Running It Quarterly

New reviews accumulate. Run this analysis every quarter to track whether patterns change. Is the complaint you fixed 3 months ago still appearing? Did a new issue emerge?

Trends in reviews predict churn before churn metrics show it. Catch problems while they are patterns, not crises.

Build These Systems

Ready to implement? These step-by-step tutorials show you exactly how:

• How to Create Automated Review Request Campaigns - Ask happy customers for reviews automatically at the right moment.
• How to Build an AI Fake Review Detector - Detect potentially fake or fraudulent reviews using AI analysis.
• How to Automate Review Monitoring Across Platforms - Monitor new reviews across all platforms and get instant notifications.