Customer Insights from Reviews & Feedback

Your Customers Tell You What to Fix—AI Helps You Listen at Scale

Analyzing hundreds of reviews manually takes forever. AI does it in minutes and finds patterns you'd miss.

Sentiment Analysis

Batch sentiment analysis:

Analyze sentiment of these customer reviews:
[Paste 20-50 reviews]

For each:
- Sentiment: Positive/Neutral/Negative
- Key topics mentioned
- Urgency level

Summary:
- Overall sentiment breakdown (%)
- Most common complaints
- Most praised features

Theme Identification

Finding patterns:

Customer feedback (200 responses):
[Paste or summarize]

Identify top 10 themes:
- How many mention each theme
- Sentiment per theme
- Specific quotes for each
- Priority ranking

Feature Requests

Product roadmap from feedback:

Analyze these feature requests:
[Paste customer requests]

Organize by:
- Most requested features
- Quick wins (high impact, low effort)
- Long-term improvements
- Edge cases to ignore

Format as product roadmap priorities.

Survey Analysis

Open-ended survey responses:

Survey question: [Your question]
Responses: [Paste 50-100 responses]

Analyze:
- Common answers (group similar)
- Unexpected insights
- Actionable takeaways
- Quote best examples

Support Ticket Analysis

Finding systemic issues:

Support tickets from last month:
[Paste ticket summaries or topics]

Identify:
- Most common issues
- Root causes
- Which need product fixes vs. better documentation
- Priority for engineering team

Competitive Intelligence

Competitor review analysis:

Competitor X's reviews:
[Paste sample reviews]

What are customers:
- Praising (their strengths)
- Complaining about (their weaknesses)
- Requesting (unmet needs)

Opportunities for our product?

Tracking Over Time

Sentiment trend analysis:

Review sentiment by month:
Jan: [X positive, Y negative]
Feb: [X positive, Y negative]
...

Analyze trends:
- Improving or declining?
- Correlation with events (launches, changes)
- Specific themes driving changes