Sales AI Skill

Health Score Churn Prevention

Calculate customer health scores from usage, engagement, and support data to identify at-risk accounts and trigger proactive retention interventions. Use when monitoring customer health, predicting churn risk, building retention playbooks, or creating proac...

Health Score & Churn Prevention

Predict and prevent revenue loss through data-driven health scoring and proactive intervention.

Workflow

  1. Ingest multi-dimensional customer data: usage, support tickets, NPS, billing, engagement.
  2. Calculate composite health score (0-100) using weighted model across all dimensions.
  3. Segment customers into health tiers: Healthy, At-Risk, Critical.
  4. Auto-trigger intervention workflows based on health tier and churn risk score.
  5. Assign at-risk accounts to CSM with prioritized action plan.
  6. Track intervention effectiveness and iterate on scoring model.
  7. Generate monthly churn risk report for leadership.

Health Score Model

HEALTH SCORE CALCULATION (0-100)
==================================

DIMENSION          | WEIGHT | METRIC                               | Score Calculation
-------------------|--------|--------------------------------------|------------------
USAGE (30%)        | 30 pts |                                        |
                   |        | Login frequency (last 30 days)        | 0-5 pts
                   |        | Feature adoption breadth             | 0-5 pts
                   |        | Core workflow completion rate         | 0-10 pts
                   |        | Usage trend ( WoW / MoM)              | 0-5 pts
                   |        | Storage/capacity utilization          | 0-5 pts
                   |        | Last active date                      | 0-5 pts

ENGAGEMENT (20%)   | 20 pts |                                        |
                   |        | NPS/CSAT score                        | 0-5 pts
                   |        | Meeting attendance rate               | 0-5 pts
                   |        | Email response rate                   | 0-5 pts
                   |        | Training completion                   | 0-5 pts

SUPPORT (20%)      | 20 pts |                                        |
                   |        | Open ticket count (critical)          | 0-5 pts
                   |        | Avg resolution time vs SLA            | 0-5 pts
                   |        | Ticket sentiment trend                | 0-5 pts
                   |        | Repeat issue frequency                | 0-5 pts

FINANCIAL (15%)    | 15 pts |                                        |
                   |        | Payment history (late payments)       | 0-5 pts
                   |        | Seat utilization rate                 | 0-5 pts
                   |        | Expansion trend (upsell/cross-sell)   | 0-5 pts

RELATIONSHIP (15%) | 15 pts |                                        |
                   |        | Stakeholder count (multi-threaded)    | 0-5 pts
                   |        | Executive sponsor engagement          | 0-5 pts
                   |        | Contract renewal date proximity       | 0-5 pts

HEALTH TIERS:
  80-100: GREEN — Healthy (low churn risk, focus on expansion)
  60-79:  YELLOW — Caution (moderate risk, proactive check-in)
  40-59:  ORANGE — At Risk (high risk, intervention required)
  20-39:  RED — Critical (immediate action, executive escalation)
  0-19:   BLACK — Imminent Churn (retention specialist + discount authority)

CHURN RISK PREDICTION:
  Model outputs probability of churn within:
  - 30 days: [%]
  - 60 days: [%]
  - 90 days: [%]
  - 180 days: [%]
  
  Based on historical patterns, usage decline trajectories,
  and comparable customer churn events.

Intervention Playbook by Health Tier

INTERVENTION PLAYBOOK
======================

GREEN (80-100) — NURTURE & EXPAND:
  ────────────────────────────────
  Action Items:
    1. Schedule QBR/EBR (if not already on cadence)
    2. Identify expansion opportunities (additional seats, modules, features)
    3. Request case study or reference commitment
    4. Invite to customer advisory board
    5. Send quarterly value report (ROI achieved)
  
  Trigger: Health score >80
  Owner: Account Manager / CSM
  Timeline: Standard quarterly cadence
  Budget: Standard CSM allocation

YELLOW (60-79) — PROACTIVE CHECK-IN:
  ──────────────────────────────────
  Action Items:
    1. CSM scheduled check-in call within 5 business days
    2. Review usage patterns and identify drop-off areas
    3. Offer targeted training or onboarding refresher
    4. Check for unresolved support issues
    5. Survey stakeholder satisfaction
    6. Document risk factors and monitor weekly
  
  Trigger: Health score drops below 80 OR single dimension drops >20%
  Owner: CSM
  Timeline: Within 5 business days
  Budget: Standard CSM allocation + training resources

ORANGE (40-59) — ACTIVE RETENTION:
  ─────────────────────────────────
  Action Items:
    1. Executive check-in within 48 hours (VP CSM or Director)
    2. Root cause analysis: why did health decline?
    3. Create 30-day turnaround plan with customer
    4. Assign dedicated success resources (if applicable)
    5. Review and resolve ALL open support tickets
    6. Executive-to-executive outreach (CEO to customer exec)
    7. Weekly health score monitoring
    8. Consider retention offer (extended term, bonus features)
  
  Trigger: Health score drops below 60 OR critical dimension fails
  Owner: Senior CSM + VP Customer Success
  Timeline: Immediate (48 hours)
  Budget: Retention budget allocated (up to 15% discount authority)

RED (20-39) — CRITICAL RETENTION:
  ────────────────────────────────
  Action Items:
    1. War room: cross-functional retention team assembled
    2. Customer executive call within 24 hours
    3. Full health diagnostic + customer sentiment deep-dive
    4. Custom retention offer prepared (discount, credits, enhanced SLA)
    5. Alternative solutions discussed (downgrade vs. churn)
    6. Daily health score monitoring
    7. Escalation to CRO/CFO if deal value exceeds threshold
  
  Trigger: Health score drops below 40 OR explicit churn signals
  Owner: Director of Customer Success + Legal + Finance
  Timeline: Immediate (24 hours)
  Budget: Up to 30% discount + custom terms (requires CFO approval)

BLACK (0-19) — IMMINENT CHURN:
  ────────────────────────────
  Action Items:
    1. CEO-level outreach (personal call or visit)
    2. Maximum retention offer authorized
    3. Post-mortem analysis: what went wrong and why
    4. If churn confirmed: graceful offboarding + winback plan
    5. Lessons documented and shared with product/sales teams
    6. Re-engagement campaign scheduled (90/180/365 days)
  
  Trigger: Health score <20 OR contract termination notice received
  Owner: VP/Head of Customer Success + CEO (for strategic accounts)
  Timeline: Immediate
  Budget: Full retention authority (requires executive approval)

Churn Signal Detection

AUTOMATED CHURN SIGNALS
========================

HIGH SEVERITY SIGNALS (Immediate Alert):
  ─────────────────────────────────────
  - NPS score drops from 8+ to ≤4
  - All user logins stop for 14+ days
  - Contract termination request received
  - Customer explicitly mentions competitor
  - Legal complaint or formal notice
  - Payment failure with no communication
  - Key champion leaves company

MEDIUM SEVERITY SIGNALS (48-Hour Review):
  ──────────────────────────────────────
  - Usage drops >40% week-over-week
  - Support ticket volume spikes >3x normal
  - NPS/CSAT drops 2+ points from average
  - QBR/EBR meeting declined 2+ times
  - Email response rate drops below 20%
  - Seat count decreasing (users offboarding)
  - Feature adoption declining (new features unused)

LOW SEVERITY SIGNALS (Weekly Monitoring):
  ──────────────────────────────────────
  - Usage trend declining gradually (>10% MoM)
  - Meeting attendance inconsistent
  - Single support ticket with negative sentiment
  - Email open rate declining
  - One stakeholder disengaged
  - Payment slightly late (1-5 days)

SIGNAL COMBINATION EFFECT:
  - 1 low signal: Monitor
  - 2+ low signals: Flag for CSM review
  - 1 medium + 1 low: Trigger Yellow intervention
  - 2+ medium signals: Trigger Orange intervention
  - 1 high signal: Trigger Red intervention
  - Any high signal: Immediate escalation regardless of score

Churn Analytics & Reporting

CHURN ANALYTICS DASHBOARD
============================

Portfolio Health Overview:
  Total Customers: [count]
  Healthy (Green): [count] ([%])
  Caution (Yellow): [count] ([%])
  At Risk (Orange): [count] ([%])
  Critical (Red/Black): [count] ([%])
  Average Health Score: [X/100]

Churn Metrics:
  Monthly Churn Rate: [%] (target: <2%)
  Annualized Revenue Churn: [%] (target: <10%)
  Gross Revenue Retention (GRR): [%] (target: >90%)
  Net Revenue Retention (NRR): [%] (target: >110%)
  Churned Customers This Month: [count]
  Revenue Lost from Churn: [$ amount]
  Revenue Saved from Retention: [$ amount]
  Saved-to-Lost Ratio: [X:1]

Intervention Effectiveness:
  Interventions triggered this month: [count]
  Intervention success rate: [%] (health improved post-intervention)
  Average time to recovery (Orange → Green): [X days]
  Average time to recovery (Red → Yellow): [X days]
  Retention offer acceptance rate: [%]
  Average discount on retention offers: [%]

Churn Root Cause Analysis:
  Top Churn Reasons (This Quarter):
    1. [Reason] — [count] ([%])
    2. [Reason] — [count] ([%])
    3. [Reason] — [count] ([%])
    4. [Reason] — [count] ([%])
    5. [Reason] — [count] ([%])
  
  Churn by Customer Segment:
    SMB: [%] | Mid-Market: [%] | Enterprise: [%]
  Churn by Product Tier:
    Basic: [%] | Pro: [%] | Enterprise: [%]
  Churn by Tenure:
    <6 months: [%] | 6-12 months: [%] | 1-2 years: [%] | 2+ years: [%]

Predictive Model Performance:
  Churn prediction accuracy: [%] (true positive rate)
  False positive rate: [%] (healthy customers flagged)
  False negative rate: [%] (churned customers missed)
  Model precision: [%] | Model recall: [%]
  Model F1 score: [X]

Edge Cases

Integration Points