The Sentiment & Retention Radar
Spots unhappy customers before they leave
How It Works
The Sentiment Radar ingests customer communication data on a rolling basis. An AI sentiment analysis layer scores each interaction and tracks trends over time. When an account's sentiment score drops below a threshold, or shows a sudden negative shift, a risk alert is generated with full context: what changed, which interactions triggered the flag, and suggested intervention approaches. A human customer success rep reviews and decides how to respond.
Step-by-Step Flow
Connect communication channels (email, support tickets, NPS, check-in notes)
Set baseline sentiment thresholds and risk alert triggers
AI sentiment model scores every interaction continuously
Risk scorer flags accounts with declining or sudden negative sentiment
CS rep receives alert with context: what happened, why it flagged, suggested response
Rep acts. Outcome logged, model calibrated over time.
Best For
- B2B SaaS or services companies with recurring revenue
- Customer success teams managing 20+ active accounts
- Companies where churn is measured and meaningful to the business
This is customized for your business.
Every node, tool, and logic path shown here gets adapted to your team structure, your CRM, and your existing workflows. What you see is the proven pattern. What we build together is built specifically for you.
Implementation Notes
Data sources include support ticket platforms (Zendesk, Intercom, Freshdesk), email threads pulled via Gmail or Outlook API, NPS survey responses, and manual CSM check-in notes entered via a Slack command or intake form. The sentiment model runs on each communication and outputs a score from -1 to 1 with a confidence level. Per-account scores are averaged over rolling 14-day and 30-day windows. A risk alert fires when an account's 14-day average drops more than 0.3 points from its 30-day average, or falls below an absolute threshold of -0.2. The alert is delivered via Slack with three components: a timeline of recent sentiment scores, a list of the specific communications that drove the drop with quoted excerpts, and two to three suggested response approaches ranked by predicted impact. The CS rep can reply in Slack to log the outcome, which is used to calibrate the model. Integration with Gainsight or ChurnZero is supported for teams using a dedicated CS platform. Prerequisites: 60 days of historical communication data to establish reliable per-account baselines.