AI Customer Feedback Analysis
Send customer reviews, survey responses, or support tickets by text, call, email, or Slack. Receive sentiment analysis, theme extraction, and actionable insights delivered to any screen.
Chatari analyzes customer feedback when you send reviews, survey responses, NPS comments, or support ticket data by text, call, email, or Slack. Chatari uses Claude for nuanced sentiment analysis and theme extraction, and GPT-4o for structured categorization and reporting. The finished analysis includes sentiment distribution, recurring themes, verbatim highlights, and prioritized recommendations. A feedback analysis costs 10-150 credits depending on volume and depth.
Analyzing hundreds of customer reviews or survey responses manually takes a full day or more. Chatari processes the data and delivers a structured analysis with actionable themes in minutes.
Customer Feedback Is Collected but Not Analyzed
Companies collect customer feedback from multiple channels: NPS surveys, app store reviews, support tickets, social mentions, and sales call notes. But reading, categorizing, and extracting actionable themes from hundreds or thousands of feedback entries is a manual slog that rarely gets done consistently.
- NPS surveys return open-text responses that sit unread in a spreadsheet
- App store reviews accumulate without systematic analysis of recurring issues
- Support ticket themes are identified anecdotally, not through structured analysis
- Product teams make roadmap decisions without comprehensive feedback synthesis
How Chatari Handles It
Send your feedback data
Email a spreadsheet of survey responses, paste NPS comments into Slack, text a batch of app store reviews, or call Chatari and describe the feedback themes you have been hearing.
Sentiment and themes are analyzed
Claude reads each piece of feedback and classifies sentiment (positive, neutral, negative). Recurring themes are identified and grouped: feature requests, pain points, praise, and churn risks.
Insights are synthesized
GPT-4o quantifies the analysis: percentage breakdown by sentiment, theme frequency rankings, and trend comparisons across time periods. Verbatim quotes are pulled to illustrate each theme.
Analysis delivered to any screen
The finished feedback analysis is pushed to you as a formatted report with sentiment charts, theme summaries, representative quotes, and prioritized action recommendations.
Example Workflows
Real examples of how you can delegate this task across different channels.
"Analyze these 5 customer reviews: [paste 5 app store reviews]. What are the main themes? What should we fix first?"
Sentiment breakdown (3 positive, 1 neutral, 1 negative), top themes (onboarding praised, mobile performance criticized, pricing questioned), priority recommendation (fix mobile performance lag). Delivered as a text reply. 3-8 credits.
"Subject: Q4 NPS Analysis. Body: Attached is our Q4 NPS survey export. 847 responses with scores and open-text comments. Please analyze: overall sentiment, top themes in promoters vs. detractors, quarter-over-quarter trends (Q3 data attached as well), and specific product areas mentioned most."
Comprehensive NPS analysis with: score distribution, sentiment breakdown by promoter/passive/detractor, top 5 themes for each group with frequency counts, QoQ trend comparison, product area heat map, representative verbatim quotes, and a prioritized list of 10 recommendations ranked by frequency and severity. Delivered as PDF. 150 credits.
"@chatari Here are the top 20 support tickets from this week [paste ticket summaries]. What patterns do you see? Anything we should escalate to engineering?"
Support ticket analysis with: issue categorization (3 categories identified), frequency breakdown, severity assessment, 2 tickets flagged for engineering escalation with reasoning, and a suggested FAQ entry to deflect the most common issue. Delivered in your Slack channel. 10 credits.
"We have been hearing from our sales team that prospects keep asking about the same three things: mobile app, API access, and SSO. I want to quantify how important these are. Can you pull our G2 and Capterra reviews and analyze what features are mentioned most?"
Chatari researches your G2 and Capterra review profiles, analyzes the most-mentioned features and pain points, cross-references with competitor reviews, and delivers a feature demand analysis with quantified frequency data and competitive context. Delivered as a report link via text. 56 credits (2-min call) + 150 credits (research) = 206 total.
What Gets Delivered
Sentiment Analysis Report
Distribution of positive, neutral, and negative sentiment across your feedback data with trend analysis.
10-150 depending on volumeTheme Extraction
Recurring themes identified, categorized, and ranked by frequency. Each theme includes representative verbatim quotes.
10-150 depending on volumeCompetitive Feedback Analysis
Analysis of competitor reviews from G2, Capterra, or app stores to identify their strengths and weaknesses from customer perspectives.
150 per competitorAction Recommendations
Prioritized list of product, support, or experience improvements based on feedback analysis, ranked by frequency and impact.
Included in analysis reportsFrequently Asked Questions
What types of customer feedback can Chatari analyze?
Chatari analyzes NPS survey responses (scores and open text), app store reviews (iOS and Android), G2/Capterra reviews, support ticket summaries, social media mentions, customer interview transcripts, and any other text-based feedback. Send the data in whatever format you have.
How does Chatari handle sentiment analysis?
Claude reads each piece of feedback and classifies sentiment based on the full context of the comment, not just keyword matching. This means sarcasm, mixed feedback, and nuanced opinions are handled more accurately than simple positive/negative classifiers.
Can Chatari compare feedback across time periods?
Yes. Send feedback data from multiple time periods (e.g., Q3 and Q4 survey exports) and request a trend comparison. Chatari identifies new emerging themes, improving areas, and worsening pain points across the periods.
Can Chatari analyze competitor reviews?
Yes. Request a competitive feedback analysis and Chatari uses Perplexity to pull reviews from G2, Capterra, app stores, and other review platforms. The analysis shows what customers praise and criticize about competitors, which helps you position your product. 150 credits per competitor.
How many reviews or responses can Chatari analyze at once?
For text and Slack, share up to 20-30 pieces of feedback inline. For larger volumes (hundreds or thousands of responses), email a spreadsheet. Claude's extended context window handles large feedback sets effectively. Complex analyses of large datasets cost 150 credits.
How much does AI feedback analysis cost?
Simple analysis of a handful of reviews costs 3-10 credits. Comprehensive analysis of hundreds of survey responses costs 150 credits. Competitive review research costs 150 credits per competitor. On an Essential plan ($49/month, 2,400 credits), you can run about 16 comprehensive analyses per month.
Sources
Ready to ai customer feedback analysis?
Start delegating tasks by text, call, email, or Slack.
Get StartedPlans start at $19/month. All channels included.
Related Workflows
Reviewed by Chatari Editorial Team. Last updated: 2026-02-26.