Catch risk before
it becomes churn
Customer health scores built on product usage miss what customers actually say. Semarize extracts sentiment, risk signals, and expansion intent from every CS conversation.
The problem
Health scores are
lagging indicators
By the time product usage drops, the customer has already decided to leave. The warning signs were in the conversations you weren't analysing.
Renewal risk is hidden in conversations
Customers express frustration, mention competitors, or signal budget issues in calls - but these signals never make it into your health score.
Expansion signals are missed
A customer mentions a new use case or team that could benefit. Without structured extraction, the opportunity stays buried in a transcript.
Sentiment is unmeasured
Product usage metrics tell you what customers do, not how they feel. Sentiment shifts happen in conversations long before usage drops.
CS reviews don't scale
CSMs can't review every call for every account. Manual note-taking is inconsistent. Critical signals slip through.
Why existing tools fail
Existing tools
miss conversation signals
CS platforms track product usage and support tickets - but conversations contain the earliest and most reliable indicators of account health.
CS platforms
Health scores are built on product usage, NPS, and support ticket volume. They miss the frustration expressed on a call or the competitor mentioned in passing.
Conversation intelligence platforms
Produce call summaries for sales teams. They're not designed for CS workflows like renewal risk scoring or expansion detection.
Manual CSM notes
CSMs take notes after calls, but capture is inconsistent. Critical signals like 'we're evaluating alternatives' don't always make it into the CRM.
The Semarize approach
Semarize extracts
leading indicators from conversations
Detect sentiment shifts, churn risk, and expansion intent from every customer interaction - automatically and at scale.
Sentiment tracking per conversation
Track how customer sentiment changes across interactions. Detect shifts from positive to cautious before they become churn.
Churn risk signals
Detect competitor mentions, frustration language, budget concerns, and contract hesitation. Flag at-risk accounts early.
Expansion intent detection
Identify when customers mention new teams, use cases, or growth needs. Surface upsell opportunities automatically.
Structured account health data
Feed conversation-derived signals into your CS platform. Combine usage data with sentiment data for complete health scores.
Bricks & Kits
Example Bricks for
customer success
These Bricks evaluate the specific dimensions that matter for customer success teams. Bundle them into Kits to create reusable evaluation frameworks.
Detects sentiment change compared to previous interactions
Customer expressing intent to leave or evaluate alternatives
Customer mentions new teams, departments, or use cases
Frustrated or escalatory language detected
Alternative products or vendors referenced
Overall satisfaction expressed in conversation
CS Health Kit
kitComprehensive account health signals from every customer conversation.
Output
Structured signals,
not summaries
Every evaluation returns deterministic JSON with typed values, reasons, and evidence spans. Same schema every time.
{
"run_id": "run_ghi789",
"status": "succeeded",
"output": {
"bricks": {
"churn_risk_flag": {
"value": true,
"confidence": 0.92,
"reason": "Customer mentioned evaluating alternatives",
"evidence": ["...looking at a few other options for next year..."]
},
"sentiment_shift": {
"value": "declining",
"confidence": 0.86,
"reason": "Tone shifted from positive in Q3 to cautious",
"evidence": ["...not sure this is the right fit anymore..."]
},
"expansion_intent": {
"value": false,
"confidence": 0.89,
"reason": "No expansion signals detected",
"evidence": []
}
}
}
}Stop guessing
about account health.
Extract churn risk, sentiment, and expansion signals from every customer conversation. Build health scores that include what customers actually said.