Echo
AI sentiment analysis for customer signals.
Turn messy customer conversations into sentiment you can segment and act on.
Echo scores customer sentiment at scale — every ticket, review, survey response and call transcript — and attaches it to the customer, not just the interaction.
Most sentiment tools stop at a dashboard. Echo is built to feed segments.
The problem
Lifecycle teams know tone matters. A frustrated customer rarely fills out a "cancellation intent" form. They leave clues — sharper language in a ticket, a lukewarm review, a survey comment that trails off.
The problem was never insight. It was scale. You sampled, summarized, moved on.
Avg. sentiment (90 days pre-churn)
What Echo does
- Ingest — tickets, reviews, surveys, call transcripts (messy text, no problem)
- Score — per-conversation sentiment with confidence
- Attach — roll up to customer level over time
- Export — feed into segments: "declining sentiment + high value + renewal in 60 days"
Ticket
Score
0.82 → 0.41
Customer
Segment
High value + risk
Play
That last step is a CVM play, not a CX report.
Who it's for
Teams who already have customer text sitting in support, feedback and CRM systems — and want to turn it into a targeting signal without hiring an annotation army.
Current status
Live experiment. Working end-to-end on sample data. I'm stress-testing it on real-world messy inputs before opening it wider.
What it isn't
- Not a full VOC platform
- Not real-time call coaching (yet)
- Not a substitute for talking to customers — it helps you know which customers to talk to
Curious? Reach out or read why sentiment is a CVM signal.
Interested in trying it?
I'm opening experiments to a handful of people at a time. No forms — just tell me what you're working on.