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What's hiding in your survey verbatims?
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Sample output
This is what lands in your inbox
Sentiment overview
- Positive 52%
- Neutral 23%
- Negative 25%
Top themes
Top risk signal
HIGH — Billing → Refund Delay
38 verbatims mention delayed refunds.
C-Risk score: 8.4/10— High probability of escalation and churn.
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What you get
A real analysis. Not a teaser.
This isn't a truncated demo. You get the same analysis engine that paying customers use — applied to a subset of your data.
Sentiment Overview
Overall sentiment distribution (positive / neutral / negative) with a breakdown by channel if your data includes one. See the emotional temperature of your customer base at a glance.
- Positive 52%
- Neutral 23%
- Negative 25%
Top 5 Emerging Themes
The five most frequent topics in your verbatims, extracted automatically — with representative quotes for each. See what customers are actually talking about, not what you assume they care about.
Top Risk Signals
The aspects of your experience generating the most negative sentiment — flagged as potential complaint-risk areas. Ranked by severity, with sample evidence.
Your email report will include
- Full sentiment overview (overall + by channel/segment if available)
- Top 10 themes with frequency and representative quotes
- Aspect-based sentiment analysis on top 5 themes
- Top 3 risk signals with severity ratings
- Recommended next analyses (from the 40+ analyses available in InsightNarrator)
- Data quality summary (completeness, anomalies detected)
Your data is processed in the EU and deleted after report generation.
Analyzed by the same multi-agent engine used by paying customers.
No newsletter signup. No sales call unless you ask for one.
Sample output
Here's what a report looks like.
This is an actual report generated from a sample NPS dataset (fictional SaaS company, 500 verbatims). Your report will have the same structure, tailored to your data.
VOC Health Check report
Acme SaaS — NPS Q3 2026
Generated: 2026-08-10 14:32 UTC
1. Sentiment overview
- Positive 52%
- Neutral 23%
- Negative 25%
| Sentiment | Count | % |
|---|---|---|
| Positive | 260 | 52% |
| Neutral | 115 | 23% |
| Negative | 125 | 25% |
Overall sentiment score: +27 (net positive, trending down from +34 last quarter)
2. Top 10 themes
| # | Theme | Mentions | Sentiment |
|---|---|---|---|
| 1 | Pricing & Plans | 142 | Negative |
| 2 | Customer Support | 110 | Mixed |
| 3 | Onboarding Experience | 76 | Positive |
| 4 | Product Speed | 60 | Negative |
| 5 | Feature Requests | 48 | Mixed |
| 6 | Billing & Payments | 42 | Negative |
| 7 | Integration | 35 | Positive |
| 8 | Documentation | 28 | Positive |
| 9 | Mobile App | 22 | Negative |
| 10 | Account Management | 18 | Mixed |
3. Aspect-based sentiment (top 5 themes)
Pricing & Plans
12/8/80 · ↓ worsening
Customer Support
34/22/44 · ↓ worsening
Onboarding
78/12/10 · ↑ improving
Product Speed
15/5/80 · → flat
Billing & Payments
8/4/88 · ↓ worsening
4. Top 3 risk signals
HIGH — Billing → Refund Delay
38 verbatims mention delayed refunds.
“I've been waiting 3 weeks for my refund. This is unacceptable.”
C-Risk score: 8.4/10— High probability of escalation and churn.
MEDIUM — Support → Response Time
24 verbatims mention slow support responses.
“Took 4 days to get a reply, and it was a template.”
C-Risk score: 6.1/10— Watch closely, may escalate.
MEDIUM — Pricing → Plan Confusion
19 verbatims mention unclear pricing tiers.
“I can't figure out which plan I'm on or what I'm paying for.”
C-Risk score: 5.7/10— Friction point, not yet a complaint driver.
5. Recommended next analyses
Based on your dataset, we recommend running:
- 1.Sentiment Driver Analysis — identify what specifically drives negative sentiment
- 2.Root Cause Analysis on Billing themes — trace the refund delay issue
- 3.Churn Risk Prediction — score individual responses for churn probability
- 4.Feature Request Prioritization — rank the 48 feature mentions by business impact
These analyses (and 40+ more) are available in the full InsightNarrator platform.
Want the full picture? Run all 43 analysis types on your complete dataset.
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