customer-success-manager

Customer Success Manager

Production-grade customer success analytics with multi-dimensional health scoring, churn risk prediction, and expansion opportunity identification. Three Python CLI tools provide deterministic, repeatable analysis using standard library only -- no external dependencies, no API calls, no ML models.

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Table of Contents

• Capabilities
• Input Requirements
• Output Formats
• How to Use
• Scripts
• Reference Guides
• Templates
• Best Practices
• Limitations

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Capabilities

• Customer Health Scoring: Multi-dimensional weighted scoring across usage, engagement, support, and relationship dimensions with Red/Yellow/Green classification
• Churn Risk Analysis: Behavioral signal detection with tier-based intervention playbooks and time-to-renewal urgency multipliers
• Expansion Opportunity Scoring: Adoption depth analysis, whitespace mapping, and revenue opportunity estimation with effort-vs-impact prioritization
• Segment-Aware Benchmarking: Configurable thresholds for Enterprise, Mid-Market, and SMB customer segments
• Trend Analysis: Period-over-period comparison to detect improving or declining trajectories
• Executive Reporting: QBR templates, success plans, and executive business review templates

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Input Requirements

All scripts accept a JSON file as positional input argument. See assets/sample_customer_data.json for complete examples.

Health Score Calculator

{
  "customers": [
    {
      "customer_id": "CUST-001",
      "name": "Acme Corp",
      "segment": "enterprise",
      "arr": 120000,
      "usage": {
        "login_frequency": 85,
        "feature_adoption": 72,
        "dau_mau_ratio": 0.45
      },
      "engagement": {
        "support_ticket_volume": 3,
        "meeting_attendance": 90,
        "nps_score": 8,
        "csat_score": 4.2
      },
      "support": {
        "open_tickets": 2,
        "escalation_rate": 0.05,
        "avg_resolution_hours": 18
      },
      "relationship": {
        "executive_sponsor_engagement": 80,
        "multi_threading_depth": 4,
        "renewal_sentiment": "positive"
      },
      "previous_period": {
        "usage_score": 70,
        "engagement_score": 65,
        "support_score": 75,
        "relationship_score": 60
      }
    }
  ]
}

Churn Risk Analyzer

{
  "customers": [
    {
      "customer_id": "CUST-001",
      "name": "Acme Corp",
      "segment": "enterprise",
      "arr": 120000,
      "contract_end_date": "2026-06-30",
      "usage_decline": {
        "login_trend": -15,
        "feature_adoption_change": -10,
        "dau_mau_change": -0.08
      },
      "engagement_drop": {
        "meeting_cancellations": 2,
        "response_time_days": 5,
        "nps_change": -3
      },
      "support_issues": {
        "open_escalations": 1,
        "unresolved_critical": 0,
        "satisfaction_trend": "declining"
      },
      "relationship_signals": {
        "champion_left": false,
        "sponsor_change": false,
        "competitor_mentions": 1
      },
      "commercial_factors": {
        "contract_type": "annual",
        "pricing_complaints": false,
        "budget_cuts_mentioned": false
      }
    }
  ]
}

Expansion Opportunity Scorer

{
  "customers": [
    {
      "customer_id": "CUST-001",
      "name": "Acme Corp",
      "segment": "enterprise",
      "arr": 120000,
      "contract": {
        "licensed_seats": 100,
        "active_seats": 95,
        "plan_tier": "professional",
        "available_tiers": ["professional", "enterprise", "enterprise_plus"]
      },
      "product_usage": {
        "core_platform": {"adopted": true, "usage_pct": 85},
        "analytics_module": {"adopted": true, "usage_pct": 60},
        "integrations_module": {"adopted": false, "usage_pct": 0},
        "api_access": {"adopted": true, "usage_pct": 40},
        "advanced_reporting": {"adopted": false, "usage_pct": 0}
      },
      "departments": {
        "current": ["engineering", "product"],
        "potential": ["marketing", "sales", "support"]
      }
    }
  ]
}

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Output Formats

All scripts support two output formats via the --format flag:

• text (default): Human-readable formatted output for terminal viewing
• json: Machine-readable JSON output for integrations and pipelines

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Clarify First

Before running the analysis, confirm these inputs. If any is unknown or vague, ASK — do not assume:

• Which analysis — health score, churn risk, or expansion opportunity (selects which of the three scripts and its input schema)
• Customer segment — Enterprise / Mid-Market / SMB (segment-aware thresholds change every Red/Yellow/Green and risk-tier cutoff)
• Previous-period data availability — without it, trend analysis (declining vs improving) cannot run
• Renewal date / contract end — drives the time-to-renewal urgency multiplier in churn scoring

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the output.

How to Use

Quick Start

# Health scoring
python scripts/health_score_calculator.py assets/sample_customer_data.json
python scripts/health_score_calculator.py assets/sample_customer_data.json --format json

# Churn risk analysis
python scripts/churn_risk_analyzer.py assets/sample_customer_data.json
python scripts/churn_risk_analyzer.py assets/sample_customer_data.json --format json

# Expansion opportunity scoring
python scripts/expansion_opportunity_scorer.py assets/sample_customer_data.json
python scripts/expansion_opportunity_scorer.py assets/sample_customer_data.json --format json

Workflow Integration

# 1. Score customer health across portfolio
python scripts/health_score_calculator.py customer_portfolio.json --format json > health_results.json

# 2. Identify at-risk accounts
python scripts/churn_risk_analyzer.py customer_portfolio.json --format json > risk_results.json

# 3. Find expansion opportunities in healthy accounts
python scripts/expansion_opportunity_scorer.py customer_portfolio.json --format json > expansion_results.json

# 4. Prepare QBR using templates
# Reference: assets/qbr_template.md

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Scripts

1. health_score_calculator.py

Purpose: Multi-dimensional customer health scoring with trend analysis and segment-aware benchmarking.

Dimensions and Weights:

Dimension	Weight	Metrics
Usage	30%	Login frequency, feature adoption, DAU/MAU ratio
Engagement	25%	Support ticket volume, meeting attendance, NPS/CSAT
Support	20%	Open tickets, escalation rate, avg resolution time
Relationship	25%	Executive sponsor engagement, multi-threading depth, renewal sentiment

Classification:

• Green (75-100): Healthy -- customer achieving value
• Yellow (50-74): Needs attention -- monitor closely
• Red (0-49): At risk -- immediate intervention required

Usage:

python scripts/health_score_calculator.py customer_data.json
python scripts/health_score_calculator.py customer_data.json --format json

2. churn_risk_analyzer.py

Purpose: Identify at-risk accounts with behavioral signal detection and tier-based intervention recommendations.

Risk Signal Weights:

Signal Category	Weight	Indicators
Usage Decline	30%	Login trend, feature adoption change, DAU/MAU change
Engagement Drop	25%	Meeting cancellations, response time, NPS change
Support Issues	20%	Open escalations, unresolved critical, satisfaction trend
Relationship Signals	15%	Champion left, sponsor change, competitor mentions
Commercial Factors	10%	Contract type, pricing complaints, budget cuts

Risk Tiers:

• Critical (80-100): Immediate executive escalation
• High (60-79): Urgent CSM intervention
• Medium (40-59): Proactive outreach
• Low (0-39): Standard monitoring

Usage:

python scripts/churn_risk_analyzer.py customer_data.json
python scripts/churn_risk_analyzer.py customer_data.json --format json

3. expansion_opportunity_scorer.py

Purpose: Identify upsell, cross-sell, and expansion opportunities with revenue estimation and priority ranking.

Expansion Types:

• Upsell: Upgrade to higher tier or more of existing product
• Cross-sell: Add new product modules
• Expansion: Additional seats or departments

Usage:

python scripts/expansion_opportunity_scorer.py customer_data.json
python scripts/expansion_opportunity_scorer.py customer_data.json --format json

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Reference Guides

Reference	Description
references/health-scoring-framework.md	Complete health scoring methodology, dimension definitions, weighting rationale, threshold calibration
references/cs-playbooks.md	Intervention playbooks for each risk tier, onboarding, renewal, expansion, and escalation procedures
references/cs-metrics-benchmarks.md	Industry benchmarks for NRR, GRR, churn rates, health scores, expansion rates by segment and industry

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Templates

Template	Purpose
assets/qbr_template.md	Quarterly Business Review presentation structure
assets/success_plan_template.md	Customer success plan with goals, milestones, and metrics
assets/onboarding_checklist_template.md	90-day onboarding checklist with phase gates
assets/executive_business_review_template.md	Executive stakeholder review for strategic accounts

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Best Practices

1. Score regularly: Run health scoring weekly for Enterprise, bi-weekly for Mid-Market, monthly for SMB
2. Act on trends, not snapshots: A declining Green is more urgent than a stable Yellow
3. Combine signals: Use all three scripts together for a complete customer picture
4. Calibrate thresholds: Adjust segment benchmarks based on your product and industry
5. Document interventions: Track what actions you took and outcomes for playbook refinement
6. Prepare with data: Run scripts before every QBR and executive meeting

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Limitations

• No real-time data: Scripts analyze point-in-time snapshots from JSON input files
• No CRM integration: Data must be exported manually from your CRM/CS platform
• Deterministic only: No predictive ML -- scoring is algorithmic based on weighted signals
• Threshold tuning: Default thresholds are industry-standard but may need calibration for your business
• Revenue estimates: Expansion revenue estimates are approximations based on usage patterns

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Tool Reference

1. health_score_calculator.py

Purpose: Multi-dimensional customer health scoring with trend analysis and segment-aware benchmarking.

python scripts/health_score_calculator.py customer_data.json
python scripts/health_score_calculator.py customer_data.json --format json

Flag	Required	Description
customer_data.json	Yes	JSON file with customer health data (usage, engagement, support, relationship metrics)
--format	No	Output format: text (default) or json

Dimensions and Weights: Usage (30%), Engagement (25%), Support (20%), Relationship (25%)

Classification: Green (75-100), Yellow (50-74), Red (0-49) -- thresholds adjust by segment (Enterprise, Mid-Market, SMB)

2. churn_risk_analyzer.py

Purpose: Identify at-risk accounts with behavioral signal detection and tier-based intervention recommendations.

python scripts/churn_risk_analyzer.py customer_data.json
python scripts/churn_risk_analyzer.py customer_data.json --format json

Flag	Required	Description
customer_data.json	Yes	JSON file with churn risk signals (usage decline, engagement drop, support issues, relationship signals, commercial factors)
--format	No	Output format: text (default) or json

Risk Tiers: Critical (80-100), High (60-79), Medium (40-59), Low (0-39)

Signal Weights: Usage Decline (30%), Engagement Drop (25%), Support Issues (20%), Relationship Signals (15%), Commercial Factors (10%)

3. expansion_opportunity_scorer.py

Purpose: Identify upsell, cross-sell, and expansion opportunities with revenue estimation and priority ranking.

python scripts/expansion_opportunity_scorer.py customer_data.json
python scripts/expansion_opportunity_scorer.py customer_data.json --format json

Flag	Required	Description
customer_data.json	Yes	JSON file with customer contract, product usage, and department data
--format	No	Output format: text (default) or json

Expansion Types: Upsell (tier upgrade), Cross-sell (new modules), Expansion (seats/departments)

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Troubleshooting

Problem	Likely Cause	Solution
Health scores do not correlate with actual churn	Default thresholds do not match your product	Calibrate segment thresholds using historical churn data; compare 90-day retained vs churned cohorts
All accounts show as Yellow	Thresholds too strict or data quality issues	Review input data completeness; adjust benchmarks in health_score_calculator.py constants for your industry
Churn risk scores are uniformly low	Missing key signals (champion left, competitor mentions)	Ensure all signal categories have data; missing data defaults to low risk, which understates actual risk
Expansion scores do not reflect reality	Product usage data is incomplete or stale	Verify product_usage fields cover all modules; run with fresh data exports from your product analytics
Scripts error on input data	JSON format does not match expected schema	Reference the Input Requirements section for exact JSON structure; validate JSON before running
Trend analysis shows no change	Previous period data not provided	Include the previous_period block in health score input for meaningful trend comparison
Intervention recommendations feel generic	Segment is not specified	Always include the segment field (enterprise, mid-market, smb) for segment-appropriate playbooks

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Success Criteria

• Health scores run weekly for Enterprise, bi-weekly for Mid-Market, monthly for SMB accounts
• Portfolio health distribution: 60%+ Green, less than 15% Red
• Churn risk critical accounts have executive escalation within 48 hours
• Expansion pipeline generated covers 20%+ of net retention target
• Health score trends (improving/declining) drive proactive outreach before renewal window
• QBR preparation includes health score, risk assessment, and expansion opportunities for every strategic account
• Intervention playbooks followed for all High and Critical risk accounts

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Scope & Limitations

• In scope: Customer health scoring, churn risk analysis, expansion opportunity identification, segment benchmarking, trend analysis, QBR preparation
• Out of scope: CRM integration, real-time monitoring, predictive ML modeling, automated outreach
• Data dependency: Scripts analyze point-in-time JSON snapshots; data must be exported manually from your CRM/CS platform
• Deterministic scoring: All analysis is algorithmic based on weighted signals -- no machine learning predictions
• Threshold tuning: Default thresholds are industry-standard benchmarks; calibrate for your specific product and customer base
• Revenue estimates: Expansion revenue estimates are approximations based on usage patterns, not binding forecasts

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Integration Points

• churn-prevention -- High-risk accounts from churn_risk_analyzer.py should trigger cancel flow optimization and save offer review
• revenue-operations -- Expansion opportunities feed into pipeline forecasting; health scores inform forecast confidence
• onboarding-cro -- When health scores show low usage in early lifecycle, the root cause is often poor activation
• pricing-strategy -- When expansion analysis reveals pricing as a barrier to upsell, feed into pricing-strategy for packaging review
• competitive-teardown -- When churn risk signals include competitor mentions, use teardown data to build counter-positioning

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Last Updated: March 2026 Tools: 3 Python CLI tools Dependencies: Python 3.7+ standard library only