Customer success teams lack a systematic way to identify accounts at risk of non-renewal, prioritize intervention, and execute targeted retention outreach before churn occurs. Manual risk assessment is slow, inconsistent, and reactive.
This workflow combines data signals (usage decline, support tickets, engagement metrics) with AI-driven risk scoring to identify at-risk renewal accounts. When risk thresholds are met, the system triggers a CS playbook with templated outreach, stakeholder alerts, and action tracking. Human approval gates ensure appropriate escalation and personalization before customer contact.
Trigger: Daily or weekly batch evaluation of renewal accounts; or real-time trigger on specific risk signal (e.g., support ticket spike, usage drop below threshold, no logins in 30 days)
Pull account records from CRM, product analytics, and support systems. Normalize data into a unified schema (account ID, ARR, renewal date, last login, support ticket count, NPS). Handle missing fields with defaults or null flags.
Apply weighted scoring logic: usage decline (30%), support escalation (25%), engagement drop (20%), payment friction (15%), contract age/expansion opportunity (10%). Output risk score 0–100 and risk tier (green/yellow/red). Label model assumptions and confidence intervals.
Bucket accounts into playbook categories: high-risk (score 70+, red), medium-risk (50–69, yellow), low-risk (below 50, green). Assign playbook template based on risk driver (e.g., 'usage-decline' playbook vs. 'payment-friction' playbook). Flag expansion opportunities separately.
Retrieve account owner, executive sponsor, primary contact, recent interactions, known issues, and expansion potential. Identify internal stakeholders (sales, product, support) who should be looped in. Prepare one-pager summary for CS team.
CS manager or ops lead reviews flagged accounts, validates risk score, and approves playbook assignment. Option to override risk tier, reassign playbook, or mark account as false positive. Approval required before outreach triggers.
Launch playbook workflow based on approved risk tier and type. Playbook includes: templated outreach email, check-in call script, executive business review offer, product training session, and escalation path. Assign tasks to CS owner with due dates.
Send templated email or SMS to primary contact with personalized context (usage insights, specific pain points, renewal timeline). Include clear CTA (schedule call, join webinar, request demo). Log send event and track open/click metrics.
CS owner follows playbook steps: conduct discovery call, identify unmet needs, present solution roadmap or expansion offer, escalate to product/sales if needed. Document findings and next steps in CRM. Update account health score based on conversation.
Track task completion, email engagement, call completion, and customer response sentiment. Auto-escalate if no response after 7 days. Flag if risk score improves (usage increases, support issues resolve) to adjust playbook intensity.
At renewal decision point, log outcome (renewed, churned, paused, expanded). Record playbook actions taken, customer objections, and resolution. Use data to refine risk model and playbook templates for future cycles.
1. **Audit data sources**: Verify CRM, product analytics, and support system APIs are accessible and current. Document data schemas and refresh frequency. 2. **Define risk signals**: With CS leadership, identify 5–8 key signals that predict churn in your customer base (e.g., login frequency, support ticket volume, NPS drop). Assign weights based on historical correlation with churn. 3. **Build risk model**: Use CRM automation, SQL queries, or ML platform to calculate risk scores. Start simple (weighted sum) and iterate. Test on historical data to validate accuracy. 4. **Design playbooks**: Create 2–3 playbook templates for different risk drivers (usage decline, support escalation, payment friction). Include email templates, call scripts, and task lists. Get CS team input. 5. **Set up automation**: Configure workflow in CRM or automation platform to run daily/weekly. Connect data sources, implement scoring logic, and trigger playbook assignment. 6. **Add approval gate**: Route flagged accounts to CS manager for review and approval before outreach. 7. **Test end-to-end**: Run workflow on sample accounts, validate risk scores, execute playbook manually, and track outcomes. 8. **Launch and monitor**: Roll out to full CS team. Monitor engagement metrics, false positive rate, and renewal conversion. Refine model and playbooks monthly based on results. 9. **Iterate**: After 2–3 renewal cycles, retrain risk model with new outcome data. Adjust signal weights and playbook templates based on what worked.