| name | codexkit-csat-sentiment-analyzer |
| description | Analyze CSAT, NPS comments, reviews, and support feedback for sentiment, recurring themes, customer pain, and service improvement actions. Use for customer support and CX feedback reviews. |
| version | 1.0.0 |
| category | data |
CSAT Sentiment Analyzer
When to Use
- Analyzing CSAT, NPS, app reviews, support comments, or post-interaction feedback.
- Finding recurring customer pain points and service improvement themes.
- Preparing support, CX, product, or leadership feedback summaries.
- Comparing sentiment across segments, channels, agents, products, or time periods.
Procedure
Step 1 - Normalize Feedback
Identify source, date range, channel, score type, segment, and any metadata. Keep raw counts separate from percentages.
Step 2 - Classify Sentiment
Use a simple sentiment label:
- positive
- neutral
- negative
- mixed
- unclear
Include confidence when comments are short or ambiguous.
Step 3 - Code Themes
Group feedback into themes such as speed, quality, pricing, reliability, usability, billing, support tone, missing features, or documentation.
Step 4 - Quantify Patterns
Report counts, percentages, average score, trend direction, and representative examples. Avoid claiming statistical significance without enough data.
Step 5 - Recommend Actions
Link every action to a theme and owner group: support, product, docs, billing, success, operations, or leadership.
Inputs
| Input | Required | Format |
|---|
| Feedback dataset | Yes | Comments, scores, reviews, tickets |
| Date range | Recommended | Start and end date |
| Segments | Optional | Plan, region, product, channel, agent |
| Scoring system | Optional | CSAT 1-5, NPS, thumbs up/down |
| Business context | Optional | Launch, outage, policy change |
Output
## CSAT Sentiment Analysis - [Period]
### Executive Summary
[Key trend, sentiment, and action]
### Score Snapshot
| Metric | Value | Notes |
|--------|-------|-------|
### Theme Breakdown
| Theme | Sentiment | Count | Percent | Representative Comment | Recommended Action |
|-------|-----------|-------|---------|------------------------|--------------------|
### Segment Differences
| Segment | Pattern | Confidence |
|---------|---------|------------|
### Action Plan
| Owner | Action | Evidence | Priority |
|-------|--------|----------|----------|
Quality Criteria
Verification (4C)
| Check | Question |
|---|
| Correctness | Do sentiment labels and theme counts match the raw feedback? |
| Completeness | Are scores, themes, segments, evidence, and actions included? |
| Context-fit | Are the recommendations realistic for the support or CX team? |
| Consequence | Could a small sample or biased feedback source lead to the wrong product or staffing decision? |
Edge Cases
- Very small sample - Mark findings as directional and avoid percentages that imply precision.
- Sarcasm or mixed sentiment - Use "mixed" or "unclear" instead of forcing polarity.
- Personally identifiable information - Mask names, emails, phone numbers, and account IDs.
- Outage or one-off incident - Separate incident-driven feedback from baseline sentiment.
Examples
Prompt: "Analyze these 300 CSAT comments from April. Break down sentiment, top pain themes, and the three actions support should take next."
Good pattern: "Billing confusion appears in 42 of 300 comments (14%). The most common evidence is customers not understanding prorated invoices after plan changes."
Definition of Done
Changelog
