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.
Kit
CS Health Kit
Output
{
"sentiment_shift": "declining",
"churn_risk_flag": true,
"expansion_intent": ["marketing team"]
}
Customer success use cases
From every conversation,
early account signals
01 / Churn risk
Catch churn signals before they become decisions
Customers express frustration, mention competitors, and signal disengagement in calls - often weeks before product usage drops. Semarize extracts churn risk flags, sentiment shifts, and competitor mentions from every CS conversation and returns them as structured fields your health score can use.
Account Health — Conversation signals
Meridian Financial
Tideline Software
NovaTech Solutions
1 account flagged for churn risk
02 / Expansion intent
Surface expansion signals your CS team would otherwise miss
A customer mentions a new team that could use the product. A power user asks about a feature that's in the next tier. Semarize detects expansion intent from conversations and flags it as a structured signal - so upsell opportunities reach account owners before the moment passes.
03 / Health signals
Feed conversation-derived signals into your health score
Product usage data tells you what customers do, not how they feel. Semarize layers conversation signals - sentiment trend, engagement level, QBR risk - on top of usage metrics so health scores reflect the full picture, not just the part your platform can measure automatically.
Opportunity record
Manager reviewThe 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.