Analyze NPS comments for churn reasons in minutes
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"I never really figured out how to set up the integrations — by the time I did, we'd already moved on to another tool."
"It does what I need but honestly the price jumped at renewal and I couldn't justify it to my manager anymore."
"We switched because we needed bulk export and it just wasn't there. We asked for it twice and nothing happened."
"When things broke during our busiest month, support took four days to get back to us. We couldn't wait that long."
What teams usually miss
Passive NPS respondents (scores 7–8) often contain early churn signals that go unread because teams focus almost exclusively on detractors.
Without AI analysis, teams manually tag comments and miss subtle theme clusters — like pricing complaints that only appear when bundled with a missing feature mention.
Churn drivers often vary by customer segment or time period, but spreadsheet-based review makes it nearly impossible to spot these distinctions at scale.
Decisions you can make from this
Prioritize which product gaps to close first based on how frequently they appear in churn-correlated NPS comments.
Trigger proactive outreach to at-risk accounts by identifying the exact language patterns that precede cancellation.
Redesign your onboarding flow by pinpointing the specific steps where detractors say they felt lost or unsupported.
Build a business case for pricing or packaging changes using direct customer quotes tied to renewal drop-off themes.