Most CS teams identify churn risk too late — usually when the customer requests a save call. By then, the decision is mostly made. AI can surface churn signals weeks earlier from data the team already has. Here is the workflow.
1. Sentiment trajectory in tickets. Sentiment worsening over 60 days is a strong predictor.
2. Decreasing engagement without a reason. Lower logins, lower feature use, lower email open rates.
3. Champion turnover. Your primary contact left or changed roles.
4. Mentions of competitors in tickets or calls. "We are evaluating alternatives." Late signal, but unmissable.
5. Adoption stalls. Customer never hit the second-value milestone after onboarding.
6. Renewal date approaching with low engagement. Standard early-warning timing.
Scan our customer base for churn risk. For each customer in this list, here is the data: [PASTE — ticket history, NPS scores, usage data, key contact info] For each customer, return: 1. Churn risk score (1-10) with reasoning 2. Top 2 specific signals driving the score 3. Days to expected churn if no intervention (rough estimate) 4. The single highest-leverage intervention 5. Who should run the intervention (CSM / Manager / Exec) 6. The 1-sentence message that would land most cleanly Return ranked by score. Be honest — do not soften scores to make the report look healthier. CSM team will use this to prioritize their week.
Risk 8-10: Executive sponsor outreach within 48 hours. Specific commitment to address top concern.
Risk 6-7: CSM strategic call. Re-validate value proposition for their current situation.
Risk 4-5: Targeted re-engagement campaign. Specific use-case content. Champion identification.
Risk 1-3: Standard cadence. Continue monitoring.
The intervention must address the actual signal — not generic "checking in." If the signal is champion turnover, the intervention is to identify and engage the new contact, not a discount.