# Customer Success Retention > Use when building customer success and retention programs. - Skill: `loopyluci/customer-success-retention` (Agent Skill) - Install (CLI): `npx skillmds@latest add loopyluci/customer-success-retention` - Raw SKILL.md: https://api.skillmd.com/api/skills/loopyluci/customer-success-retention/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - License: MIT - Author: LoopyLuci (https://skillmd.com/u/loopyluci) - Updated: 2026-09-08 - Page: https://skillmd.com/skills/loopyluci/customer-success-retention --- # Customer Success and Retention Building customer success programs that reduce churn, increase retention, and drive expansion revenue — from onboarding and health scoring through engagement and advocacy. ## When to Use - Building a customer success function from scratch - Reducing churn and improving retention rates - Designing customer onboarding and engagement programs - Implementing customer health scoring - Driving upsells, cross-sells, and advocacy ## Customer Lifecycle ``` Acquisition → Onboarding → Adoption → Value Realization → Expansion → Advocacy │ │ └── At Risk ←── Churn Risk ←─── Dormant ←─────┘ ``` ## Onboarding Design ```python from typing import Dict, List, Optional from datetime import datetime, timedelta class OnboardingProgram: """Design and manage customer onboarding sequences.""" def __init__(self, product_name: str, time_to_value_days: int = 30): self.product = product_name self.ttv_days = time_to_value_days self.steps = [] def add_step(self, day: int, title: str, description: str, owner: str = 'auto', success_criteria: str = "") -> 'OnboardingProgram': """Add an onboarding step.""" self.steps.append({ 'day': day, 'title': title, 'description': description, 'owner': owner, 'success_criteria': success_criteria, }) return self def generate_plan(self) -> str: """Generate complete onboarding plan.""" plan = f"\n🚀 {self.product} Onboarding Plan ({self.ttv_days} days to value)\n" plan += "=" * 50 + "\n" milestones = {'week': 1, 'month': 1} for step in sorted(self.steps, key=lambda s: s['day']): week = step['day'] // 7 + 1 if week > milestones['week']: plan += f"\n── Week {milestones['week']} Complete ──\n" milestones['week'] = week plan += f"\nDay {step['day']}: {step['title']}" plan += f"\n → {step['description']}" if step['success_criteria']: plan += f"\n ✓ Success: {step['success_criteria']}" return plan # Standard 30-day onboarding example def default_onboarding(product_name: str) -> OnboardingProgram: onboarding = OnboardingProgram(product_name) onboarding.add_step(0, "Welcome & Account Setup", "Send welcome email with login credentials and getting-started guide", 'auto', "User logs in within 24 hours") onboarding.add_step(3, "First Success Call", "Schedule 30-min call to define goals and success criteria", 'customer_success', "Goals documented in system") onboarding.add_step(7, "Core Feature Training", "Walk through top 3 features that deliver quickest value", 'customer_success', "User completes core actions") onboarding.add_step(14, "Integration Setup", "Configure any integrations or data imports", 'customer_success', "Integrations active") onboarding.add_step(21, "Best Practices Review", "Review usage data and suggest optimizations", 'customer_success', "Adoption score > 60%") onboarding.add_step(30, "Business Review & Next Steps", "30-day review: results achieved, upcoming milestones, expansion opportunities", 'customer_success', "NPS collected, Q2 plan documented") return onboarding ``` ## Health Scoring ```python class CustomerHealthScore: """Calculate customer health scores to identify at-risk accounts.""" WEIGHTS = { 'product_usage': 0.30, 'support_interaction': 0.20, 'engagement': 0.20, 'business_outcomes': 0.20, 'sentiment': 0.10, } @staticmethod def calculate(customer: Dict) -> Dict: """Calculate health score (0-100) for a customer.""" score = 0 components = {} # Product usage (0-100) usage = customer.get('login_frequency', 0) * 10 feature_adoption = customer.get('features_used', []) max_features = customer.get('total_features', 10) usage_score = min(100, usage + (len(feature_adoption) / max(max_features, 1) * 50)) components['product_usage'] = usage_score score += usage_score * CustomerHealthScore.WEIGHTS['product_usage'] # Support interaction tickets = customer.get('open_tickets', 0) resolution_time = customer.get('avg_resolution_hours', 24) support_score = max(0, 100 - tickets * 20 - resolution_time * 2) components['support_interaction'] = support_score score += support_score * CustomerHealthScore.WEIGHTS['support_interaction'] # Engagement last_login = customer.get('days_since_login', 30) email_opens = customer.get('email_open_rate', 0.5) engagement_score = max(0, 100 - last_login * 3) + (email_opens * 20) components['engagement'] = min(100, engagement_score) score += min(100, engagement_score) * CustomerHealthScore.WEIGHTS['engagement'] # Business outcomes if customer.get('goals_achieved', 0) > 0: outcomes = (customer.get('goals_achieved', 0) / max(customer.get('goals_set', 1), 1)) * 100 else: outcomes = 50 # Neutral components['business_outcomes'] = min(100, outcomes) score += min(100, outcomes) * CustomerHealthScore.WEIGHTS['business_outcomes'] # Sentiment (NPS, CSAT) nps = customer.get('nps_score', 50) sentiment = (nps + 100) / 2 # Convert -100..100 to 0..100 components['sentiment'] = sentiment score += sentiment * CustomerHealthScore.WEIGHTS['sentiment'] health = round(score) return { 'score': health, 'rating': 'green' if health >= 70 else 'yellow' if health >= 40 else 'red', 'components': components, 'trend': customer.get('health_trend', 'stable'), } ``` ## Churn Prediction ```python class ChurnPredictor: """Predict customer churn risk based on behaviors.""" RISK_FACTORS = { 'decreased_usage': { 'description': 'Usage dropped 30%+ compared to previous 30 days', 'weight': 0.30, }, 'no_login': { 'description': 'Has not logged in for 14+ days', 'weight': 0.25, }, 'negative_support': { 'description': 'Opened support ticket with negative sentiment in last 7 days', 'weight': 0.15, }, 'missed_renewal': { 'description': 'Contract renewal date passed without action', 'weight': 0.15, }, 'budget_change': { 'description': 'Customer indicated budget cuts or changing priorities', 'weight': 0.10, }, 'competitor_mention': { 'description': 'Customer mentioned evaluating competitors', 'weight': 0.05, }, } @staticmethod def assess(customer: Dict) -> Dict: """Assess churn risk for a customer.""" risk_score = 0 active_risks = [] if customer.get('usage_change', 0) < -0.3: risk_score += ChurnPredictor.RISK_FACTORS['decreased_usage']['weight'] active_risks.append('decreased_usage') if customer.get('days_since_login', 0) > 14: risk_score += ChurnPredictor.RISK_FACTORS['no_login']['weight'] active_risks.append('no_login') if customer.get('recent_negative_tickets', 0) > 0: risk_score += ChurnPredictor.RISK_FACTORS['negative_support']['weight'] active_risks.append('negative_support') risk_pct = round(risk_score * 100) return { 'customer_id': customer.get('id', 'unknown'), 'churn_risk_pct': risk_pct, 'risk_level': 'high' if risk_pct >= 40 else 'medium' if risk_pct >= 20 else 'low', 'active_risk_factors': active_risks, 'next_action': ChurnPredictor._suggest_action(active_risks), } @staticmethod def _suggest_action(risks: List[str]) -> str: actions = { 'decreased_usage': 'Schedule re-engagement call, offer training', 'no_login': 'Send re-engagement email with feature highlights', 'negative_support': 'Escalate support ticket, assign dedicated support', 'missed_renewal': 'Contact for renewal discussion immediately', 'budget_change': 'Prepare ROI presentation, offer discount if needed', 'competitor_mention': 'Share competitive comparison, strengthen relationship', } if not risks: return 'No action needed — customer is healthy' return '; '.join(actions.get(r, 'Monitor') for r in risks) ``` ## Expansion Playbook ```python class ExpansionPlaybook: """Define upsell, cross-sell, and expansion strategies.""" PLAYBOOKS = { 'upsell': { 'trigger': 'Customer reaches 80%+ of plan limits', 'approach': 'Show value of next tier, offer migration support', 'timing': 'During quarterly business review', }, 'cross_sell': { 'trigger': 'Customer asks about a feature in another product', 'approach': 'Demo complementary product, share case studies', 'timing': 'After value milestone achieved', }, 'advocacy': { 'trigger': 'NPS ≥ 9, high usage, active engagement', 'approach': 'Invite to customer advisory board, request case study', 'timing': 'After 6+ months of success', }, } @staticmethod def identify_opportunity(customer: Dict) -> List[Dict]: """Identify expansion opportunities for a customer.""" opportunities = [] if customer.get('usage_pct', 0) >= 80: opportunities.append({ 'type': 'upsell', 'reason': f"Using {customer.get('usage_pct')}% of plan", 'playbook': ExpansionPlaybook.PLAYBOOKS['upsell'], }) if customer.get('nps_score', 0) >= 9: opportunities.append({ 'type': 'advocacy', 'reason': 'Promoter (NPS ≥ 9)', 'playbook': ExpansionPlaybook.PLAYBOOKS['advocacy'], }) return opportunities ``` ## Common Pitfalls 1. **Reactive vs proactive** — waiting for customers to complain instead of monitoring health scores 2. **No onboarding structure** — customers who don't see value in 30 days will churn 3. **Health score without action** — scoring is useless without automated triggers for intervention 4. **One-size-fits-all** — enterprise and SMB customers need different success approaches 5. **Success = support** — customer success is proactive, support is reactive; they're different functions 6. **Not measuring outcomes** — tracking activities (calls made) instead of outcomes (customer health improved) ## Verification Checklist - [ ] Onboarding program designed with time-to-value milestones - [ ] Customer health score model defined (product, support, engagement, outcomes, sentiment) - [ ] Health score thresholds set (green/yellow/red) - [ ] Churn risk predictors identified and monitored - [ ] Automated triggers for health score changes - [ ] Quarterly business review process established - [ ] Expansion playbooks defined (upsell, cross-sell, advocacy) - [ ] NPS/CSAT survey program active - [ ] Customer success platform configured ## See Also - crm-sales-pipeline — managing customer accounts - email-marketing-campaigns — automated customer communication - business-metrics-kpis — tracking retention and expansion metrics - customer-segmentation-analysis — segmenting customers by health