[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6D99H6EC02CXKSCZTY1J14A":3,"skill-same:01M6D99H6EC02CXKSCZTY1J14A":121},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":32,"linkOnly":33,"markdownContent":34,"markdownBody":35,"frontMatter":36,"refName":44,"commitSha":45,"skillDirectory":46,"forksCount":47,"repositoryHtmlUrl":48,"repositoryHomepageUrl":6,"ownerHtmlUrl":49,"ownerBlogUrl":50,"ownerDescription":51,"updatedAt":52,"install":53,"resources":66},"01M6D99H6EC02CXKSCZTY1J14A","customer-success-manager",null,"Monitors customer health, predicts churn risk, and identifies expansion opportunities using weighted scoring models for SaaS customer success","oabdelmaksoud\u002FAgention","oabdelmaksoud","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F50699149?v=4","01M6D5CVVN778ZCDP1WHS816SP","Omar Abdelmaksoud","01M6D8A4TE76V3DCSH3D94VG1V",2,"MIT","https:\u002F\u002Fgithub.com\u002Foabdelmaksoud\u002FAgention\u002Fblob\u002Fmain\u002Fupstream-import\u002Fclaude-skills\u002Freferences\u002Fbusiness-growth\u002Fcustomer-success-manager\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31],"customer","customer success manager","metrics","risk","saas","health","calculator","business","customer-success","growth","models","scoring","opportunity","2026-11-10 10:12:30",false,"---\nname: customer-success-manager\ndescription: Monitors customer health, predicts churn risk, and identifies expansion opportunities using weighted scoring models for SaaS customer success\nlicense: MIT\nmetadata:\n  version: 1.0.0\n  author: Alireza Rezvani\n  category: business-growth\n  domain: customer-success\n  updated: 2026-02-06\n  python-tools: health_score_calculator.py, churn_risk_analyzer.py, expansion_opportunity_scorer.py\n  tech-stack: customer-success, saas-metrics, health-scoring\n---\n\n# Customer Success Manager\n\nProduction-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.\n\n---\n\n## Table of Contents\n\n- [Capabilities](#capabilities)\n- [Input Requirements](#input-requirements)\n- [Output Formats](#output-formats)\n- [How to Use](#how-to-use)\n- [Scripts](#scripts)\n- [Reference Guides](#reference-guides)\n- [Templates](#templates)\n- [Best Practices](#best-practices)\n- [Limitations](#limitations)\n\n---\n\n## Capabilities\n\n- **Customer Health Scoring**: Multi-dimensional weighted scoring across usage, engagement, support, and relationship dimensions with Red\u002FYellow\u002FGreen classification\n- **Churn Risk Analysis**: Behavioral signal detection with tier-based intervention playbooks and time-to-renewal urgency multipliers\n- **Expansion Opportunity Scoring**: Adoption depth analysis, whitespace mapping, and revenue opportunity estimation with effort-vs-impact prioritization\n- **Segment-Aware Benchmarking**: Configurable thresholds for Enterprise, Mid-Market, and SMB customer segments\n- **Trend Analysis**: Period-over-period comparison to detect improving or declining trajectories\n- **Executive Reporting**: QBR templates, success plans, and executive business review templates\n\n---\n\n## Input Requirements\n\nAll scripts accept a JSON file as positional input argument. See `assets\u002Fsample_customer_data.json` for complete examples.\n\n### Health Score Calculator\n\n```json\n{\n  \"customers\": [\n    {\n      \"customer_id\": \"CUST-001\",\n      \"name\": \"Acme Corp\",\n      \"segment\": \"enterprise\",\n      \"arr\": 120000,\n      \"usage\": {\n        \"login_frequency\": 85,\n        \"feature_adoption\": 72,\n        \"dau_mau_ratio\": 0.45\n      },\n      \"engagement\": {\n        \"support_ticket_volume\": 3,\n        \"meeting_attendance\": 90,\n        \"nps_score\": 8,\n        \"csat_score\": 4.2\n      },\n      \"support\": {\n        \"open_tickets\": 2,\n        \"escalation_rate\": 0.05,\n        \"avg_resolution_hours\": 18\n      },\n      \"relationship\": {\n        \"executive_sponsor_engagement\": 80,\n        \"multi_threading_depth\": 4,\n        \"renewal_sentiment\": \"positive\"\n      },\n      \"previous_period\": {\n        \"usage_score\": 70,\n        \"engagement_score\": 65,\n        \"support_score\": 75,\n        \"relationship_score\": 60\n      }\n    }\n  ]\n}\n```\n\n### Churn Risk Analyzer\n\n```json\n{\n  \"customers\": [\n    {\n      \"customer_id\": \"CUST-001\",\n      \"name\": \"Acme Corp\",\n      \"segment\": \"enterprise\",\n      \"arr\": 120000,\n      \"contract_end_date\": \"2026-06-30\",\n      \"usage_decline\": {\n        \"login_trend\": -15,\n        \"feature_adoption_change\": -10,\n        \"dau_mau_change\": -0.08\n      },\n      \"engagement_drop\": {\n        \"meeting_cancellations\": 2,\n        \"response_time_days\": 5,\n        \"nps_change\": -3\n      },\n      \"support_issues\": {\n        \"open_escalations\": 1,\n        \"unresolved_critical\": 0,\n        \"satisfaction_trend\": \"declining\"\n      },\n      \"relationship_signals\": {\n        \"champion_left\": false,\n        \"sponsor_change\": false,\n        \"competitor_mentions\": 1\n      },\n      \"commercial_factors\": {\n        \"contract_type\": \"annual\",\n        \"pricing_complaints\": false,\n        \"budget_cuts_mentioned\": false\n      }\n    }\n  ]\n}\n```\n\n### Expansion Opportunity Scorer\n\n```json\n{\n  \"customers\": [\n    {\n      \"customer_id\": \"CUST-001\",\n      \"name\": \"Acme Corp\",\n      \"segment\": \"enterprise\",\n      \"arr\": 120000,\n      \"contract\": {\n        \"licensed_seats\": 100,\n        \"active_seats\": 95,\n        \"plan_tier\": \"professional\",\n        \"available_tiers\": [\"professional\", \"enterprise\", \"enterprise_plus\"]\n      },\n      \"product_usage\": {\n        \"core_platform\": {\"adopted\": true, \"usage_pct\": 85},\n        \"analytics_module\": {\"adopted\": true, \"usage_pct\": 60},\n        \"integrations_module\": {\"adopted\": false, \"usage_pct\": 0},\n        \"api_access\": {\"adopted\": true, \"usage_pct\": 40},\n        \"advanced_reporting\": {\"adopted\": false, \"usage_pct\": 0}\n      },\n      \"departments\": {\n        \"current\": [\"engineering\", \"product\"],\n        \"potential\": [\"marketing\", \"sales\", \"support\"]\n      }\n    }\n  ]\n}\n```\n\n---\n\n## Output Formats\n\nAll scripts support two output formats via the `--format` flag:\n\n- **`text`** (default): Human-readable formatted output for terminal viewing\n- **`json`**: Machine-readable JSON output for integrations and pipelines\n\n---\n\n## How to Use\n\n### Quick Start\n\n```bash\n# Health scoring\npython scripts\u002Fhealth_score_calculator.py assets\u002Fsample_customer_data.json\npython scripts\u002Fhealth_score_calculator.py assets\u002Fsample_customer_data.json --format json\n\n# Churn risk analysis\npython scripts\u002Fchurn_risk_analyzer.py assets\u002Fsample_customer_data.json\npython scripts\u002Fchurn_risk_analyzer.py assets\u002Fsample_customer_data.json --format json\n\n# Expansion opportunity scoring\npython scripts\u002Fexpansion_opportunity_scorer.py assets\u002Fsample_customer_data.json\npython scripts\u002Fexpansion_opportunity_scorer.py assets\u002Fsample_customer_data.json --format json\n```\n\n### Workflow Integration\n\n```bash\n# 1. Score customer health across portfolio\npython scripts\u002Fhealth_score_calculator.py customer_portfolio.json --format json > health_results.json\n\n# 2. Identify at-risk accounts\npython scripts\u002Fchurn_risk_analyzer.py customer_portfolio.json --format json > risk_results.json\n\n# 3. Find expansion opportunities in healthy accounts\npython scripts\u002Fexpansion_opportunity_scorer.py customer_portfolio.json --format json > expansion_results.json\n\n# 4. Prepare QBR using templates\n# Reference: assets\u002Fqbr_template.md\n```\n\n---\n\n## Scripts\n\n### 1. health_score_calculator.py\n\n**Purpose:** Multi-dimensional customer health scoring with trend analysis and segment-aware benchmarking.\n\n**Dimensions and Weights:**\n| Dimension | Weight | Metrics |\n|-----------|--------|---------|\n| Usage | 30% | Login frequency, feature adoption, DAU\u002FMAU ratio |\n| Engagement | 25% | Support ticket volume, meeting attendance, NPS\u002FCSAT |\n| Support | 20% | Open tickets, escalation rate, avg resolution time |\n| Relationship | 25% | Executive sponsor engagement, multi-threading depth, renewal sentiment |\n\n**Classification:**\n- Green (75-100): Healthy -- customer achieving value\n- Yellow (50-74): Needs attention -- monitor closely\n- Red (0-49): At risk -- immediate intervention required\n\n**Usage:**\n```bash\npython scripts\u002Fhealth_score_calculator.py customer_data.json\npython scripts\u002Fhealth_score_calculator.py customer_data.json --format json\n```\n\n### 2. churn_risk_analyzer.py\n\n**Purpose:** Identify at-risk accounts with behavioral signal detection and tier-based intervention recommendations.\n\n**Risk Signal Weights:**\n| Signal Category | Weight | Indicators |\n|----------------|--------|------------|\n| Usage Decline | 30% | Login trend, feature adoption change, DAU\u002FMAU change |\n| Engagement Drop | 25% | Meeting cancellations, response time, NPS change |\n| Support Issues | 20% | Open escalations, unresolved critical, satisfaction trend |\n| Relationship Signals | 15% | Champion left, sponsor change, competitor mentions |\n| Commercial Factors | 10% | Contract type, pricing complaints, budget cuts |\n\n**Risk Tiers:**\n- Critical (80-100): Immediate executive escalation\n- High (60-79): Urgent CSM intervention\n- Medium (40-59): Proactive outreach\n- Low (0-39): Standard monitoring\n\n**Usage:**\n```bash\npython scripts\u002Fchurn_risk_analyzer.py customer_data.json\npython scripts\u002Fchurn_risk_analyzer.py customer_data.json --format json\n```\n\n### 3. expansion_opportunity_scorer.py\n\n**Purpose:** Identify upsell, cross-sell, and expansion opportunities with revenue estimation and priority ranking.\n\n**Expansion Types:**\n- **Upsell**: Upgrade to higher tier or more of existing product\n- **Cross-sell**: Add new product modules\n- **Expansion**: Additional seats or departments\n\n**Usage:**\n```bash\npython scripts\u002Fexpansion_opportunity_scorer.py customer_data.json\npython scripts\u002Fexpansion_opportunity_scorer.py customer_data.json --format json\n```\n\n---\n\n## Reference Guides\n\n| Reference | Description |\n|-----------|-------------|\n| `references\u002Fhealth-scoring-framework.md` | Complete health scoring methodology, dimension definitions, weighting rationale, threshold calibration |\n| `references\u002Fcs-playbooks.md` | Intervention playbooks for each risk tier, onboarding, renewal, expansion, and escalation procedures |\n| `references\u002Fcs-metrics-benchmarks.md` | Industry benchmarks for NRR, GRR, churn rates, health scores, expansion rates by segment and industry |\n\n---\n\n## Templates\n\n| Template | Purpose |\n|----------|---------|\n| `assets\u002Fqbr_template.md` | Quarterly Business Review presentation structure |\n| `assets\u002Fsuccess_plan_template.md` | Customer success plan with goals, milestones, and metrics |\n| `assets\u002Fonboarding_checklist_template.md` | 90-day onboarding checklist with phase gates |\n| `assets\u002Fexecutive_business_review_template.md` | Executive stakeholder review for strategic accounts |\n\n---\n\n## Best Practices\n\n1. **Score regularly**: Run health scoring weekly for Enterprise, bi-weekly for Mid-Market, monthly for SMB\n2. **Act on trends, not snapshots**: A declining Green is more urgent than a stable Yellow\n3. **Combine signals**: Use all three scripts together for a complete customer picture\n4. **Calibrate thresholds**: Adjust segment benchmarks based on your product and industry\n5. **Document interventions**: Track what actions you took and outcomes for playbook refinement\n6. **Prepare with data**: Run scripts before every QBR and executive meeting\n\n---\n\n## Limitations\n\n- **No real-time data**: Scripts analyze point-in-time snapshots from JSON input files\n- **No CRM integration**: Data must be exported manually from your CRM\u002FCS platform\n- **Deterministic only**: No predictive ML -- scoring is algorithmic based on weighted signals\n- **Threshold tuning**: Default thresholds are industry-standard but may need calibration for your business\n- **Revenue estimates**: Expansion revenue estimates are approximations based on usage patterns\n\n---\n\n**Last Updated:** February 2026\n**Tools:** 3 Python CLI tools\n**Dependencies:** Python 3.7+ standard library only\n","# Customer Success Manager\n\nProduction-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.\n\n---\n\n## Table of Contents\n\n- [Capabilities](#capabilities)\n- [Input Requirements](#input-requirements)\n- [Output Formats](#output-formats)\n- [How to Use](#how-to-use)\n- [Scripts](#scripts)\n- [Reference Guides](#reference-guides)\n- [Templates](#templates)\n- [Best Practices](#best-practices)\n- [Limitations](#limitations)\n\n---\n\n## Capabilities\n\n- **Customer Health Scoring**: Multi-dimensional weighted scoring across usage, engagement, support, and relationship dimensions with Red\u002FYellow\u002FGreen classification\n- **Churn Risk Analysis**: Behavioral signal detection with tier-based intervention playbooks and time-to-renewal urgency multipliers\n- **Expansion Opportunity Scoring**: Adoption depth analysis, whitespace mapping, and revenue opportunity estimation with effort-vs-impact prioritization\n- **Segment-Aware Benchmarking**: Configurable thresholds for Enterprise, Mid-Market, and SMB customer segments\n- **Trend Analysis**: Period-over-period comparison to detect improving or declining trajectories\n- **Executive Reporting**: QBR templates, success plans, and executive business review templates\n\n---\n\n## Input Requirements\n\nAll scripts accept a JSON file as positional input argument. See `assets\u002Fsample_customer_data.json` for complete examples.\n\n### Health Score Calculator\n\n```json\n{\n  \"customers\": [\n    {\n      \"customer_id\": \"CUST-001\",\n      \"name\": \"Acme Corp\",\n      \"segment\": \"enterprise\",\n      \"arr\": 120000,\n      \"usage\": {\n        \"login_frequency\": 85,\n        \"feature_adoption\": 72,\n        \"dau_mau_ratio\": 0.45\n      },\n      \"engagement\": {\n        \"support_ticket_volume\": 3,\n        \"meeting_attendance\": 90,\n        \"nps_score\": 8,\n        \"csat_score\": 4.2\n      },\n      \"support\": {\n        \"open_tickets\": 2,\n        \"escalation_rate\": 0.05,\n        \"avg_resolution_hours\": 18\n      },\n      \"relationship\": {\n        \"executive_sponsor_engagement\": 80,\n        \"multi_threading_depth\": 4,\n        \"renewal_sentiment\": \"positive\"\n      },\n      \"previous_period\": {\n        \"usage_score\": 70,\n        \"engagement_score\": 65,\n        \"support_score\": 75,\n        \"relationship_score\": 60\n      }\n    }\n  ]\n}\n```\n\n### Churn Risk Analyzer\n\n```json\n{\n  \"customers\": [\n    {\n      \"customer_id\": \"CUST-001\",\n      \"name\": \"Acme Corp\",\n      \"segment\": \"enterprise\",\n      \"arr\": 120000,\n      \"contract_end_date\": \"2026-06-30\",\n      \"usage_decline\": {\n        \"login_trend\": -15,\n        \"feature_adoption_change\": -10,\n        \"dau_mau_change\": -0.08\n      },\n      \"engagement_drop\": {\n        \"meeting_cancellations\": 2,\n        \"response_time_days\": 5,\n        \"nps_change\": -3\n      },\n      \"support_issues\": {\n        \"open_escalations\": 1,\n        \"unresolved_critical\": 0,\n        \"satisfaction_trend\": \"declining\"\n      },\n      \"relationship_signals\": {\n        \"champion_left\": false,\n        \"sponsor_change\": false,\n        \"competitor_mentions\": 1\n      },\n      \"commercial_factors\": {\n        \"contract_type\": \"annual\",\n        \"pricing_complaints\": false,\n        \"budget_cuts_mentioned\": false\n      }\n    }\n  ]\n}\n```\n\n### Expansion Opportunity Scorer\n\n```json\n{\n  \"customers\": [\n    {\n      \"customer_id\": \"CUST-001\",\n      \"name\": \"Acme Corp\",\n      \"segment\": \"enterprise\",\n      \"arr\": 120000,\n      \"contract\": {\n        \"licensed_seats\": 100,\n        \"active_seats\": 95,\n        \"plan_tier\": \"professional\",\n        \"available_tiers\": [\"professional\", \"enterprise\", \"enterprise_plus\"]\n      },\n      \"product_usage\": {\n        \"core_platform\": {\"adopted\": true, \"usage_pct\": 85},\n        \"analytics_module\": {\"adopted\": true, \"usage_pct\": 60},\n        \"integrations_module\": {\"adopted\": false, \"usage_pct\": 0},\n        \"api_access\": {\"adopted\": true, \"usage_pct\": 40},\n        \"advanced_reporting\": {\"adopted\": false, \"usage_pct\": 0}\n      },\n      \"departments\": {\n        \"current\": [\"engineering\", \"product\"],\n        \"potential\": [\"marketing\", \"sales\", \"support\"]\n      }\n    }\n  ]\n}\n```\n\n---\n\n## Output Formats\n\nAll scripts support two output formats via the `--format` flag:\n\n- **`text`** (default): Human-readable formatted output for terminal viewing\n- **`json`**: Machine-readable JSON output for integrations and pipelines\n\n---\n\n## How to Use\n\n### Quick Start\n\n```bash\n# Health scoring\npython scripts\u002Fhealth_score_calculator.py assets\u002Fsample_customer_data.json\npython scripts\u002Fhealth_score_calculator.py assets\u002Fsample_customer_data.json --format json\n\n# Churn risk analysis\npython scripts\u002Fchurn_risk_analyzer.py assets\u002Fsample_customer_data.json\npython scripts\u002Fchurn_risk_analyzer.py assets\u002Fsample_customer_data.json --format json\n\n# Expansion opportunity scoring\npython scripts\u002Fexpansion_opportunity_scorer.py assets\u002Fsample_customer_data.json\npython scripts\u002Fexpansion_opportunity_scorer.py assets\u002Fsample_customer_data.json --format json\n```\n\n### Workflow Integration\n\n```bash\n# 1. Score customer health across portfolio\npython scripts\u002Fhealth_score_calculator.py customer_portfolio.json --format json > health_results.json\n\n# 2. Identify at-risk accounts\npython scripts\u002Fchurn_risk_analyzer.py customer_portfolio.json --format json > risk_results.json\n\n# 3. Find expansion opportunities in healthy accounts\npython scripts\u002Fexpansion_opportunity_scorer.py customer_portfolio.json --format json > expansion_results.json\n\n# 4. Prepare QBR using templates\n# Reference: assets\u002Fqbr_template.md\n```\n\n---\n\n## Scripts\n\n### 1. health_score_calculator.py\n\n**Purpose:** Multi-dimensional customer health scoring with trend analysis and segment-aware benchmarking.\n\n**Dimensions and Weights:**\n| Dimension | Weight | Metrics |\n|-----------|--------|---------|\n| Usage | 30% | Login frequency, feature adoption, DAU\u002FMAU ratio |\n| Engagement | 25% | Support ticket volume, meeting attendance, NPS\u002FCSAT |\n| Support | 20% | Open tickets, escalation rate, avg resolution time |\n| Relationship | 25% | Executive sponsor engagement, multi-threading depth, renewal sentiment |\n\n**Classification:**\n- Green (75-100): Healthy -- customer achieving value\n- Yellow (50-74): Needs attention -- monitor closely\n- Red (0-49): At risk -- immediate intervention required\n\n**Usage:**\n```bash\npython scripts\u002Fhealth_score_calculator.py customer_data.json\npython scripts\u002Fhealth_score_calculator.py customer_data.json --format json\n```\n\n### 2. churn_risk_analyzer.py\n\n**Purpose:** Identify at-risk accounts with behavioral signal detection and tier-based intervention recommendations.\n\n**Risk Signal Weights:**\n| Signal Category | Weight | Indicators |\n|----------------|--------|------------|\n| Usage Decline | 30% | Login trend, feature adoption change, DAU\u002FMAU change |\n| Engagement Drop | 25% | Meeting cancellations, response time, NPS change |\n| Support Issues | 20% | Open escalations, unresolved critical, satisfaction trend |\n| Relationship Signals | 15% | Champion left, sponsor change, competitor mentions |\n| Commercial Factors | 10% | Contract type, pricing complaints, budget cuts |\n\n**Risk Tiers:**\n- Critical (80-100): Immediate executive escalation\n- High (60-79): Urgent CSM intervention\n- Medium (40-59): Proactive outreach\n- Low (0-39): Standard monitoring\n\n**Usage:**\n```bash\npython scripts\u002Fchurn_risk_analyzer.py customer_data.json\npython scripts\u002Fchurn_risk_analyzer.py customer_data.json --format json\n```\n\n### 3. expansion_opportunity_scorer.py\n\n**Purpose:** Identify upsell, cross-sell, and expansion opportunities with revenue estimation and priority ranking.\n\n**Expansion Types:**\n- **Upsell**: Upgrade to higher tier or more of existing product\n- **Cross-sell**: Add new product modules\n- **Expansion**: Additional seats or departments\n\n**Usage:**\n```bash\npython scripts\u002Fexpansion_opportunity_scorer.py customer_data.json\npython scripts\u002Fexpansion_opportunity_scorer.py customer_data.json --format json\n```\n\n---\n\n## Reference Guides\n\n| Reference | Description |\n|-----------|-------------|\n| `references\u002Fhealth-scoring-framework.md` | Complete health scoring methodology, dimension definitions, weighting rationale, threshold calibration |\n| `references\u002Fcs-playbooks.md` | Intervention playbooks for each risk tier, onboarding, renewal, expansion, and escalation procedures |\n| `references\u002Fcs-metrics-benchmarks.md` | Industry benchmarks for NRR, GRR, churn rates, health scores, expansion rates by segment and industry |\n\n---\n\n## Templates\n\n| Template | Purpose |\n|----------|---------|\n| `assets\u002Fqbr_template.md` | Quarterly Business Review presentation structure |\n| `assets\u002Fsuccess_plan_template.md` | Customer success plan with goals, milestones, and metrics |\n| `assets\u002Fonboarding_checklist_template.md` | 90-day onboarding checklist with phase gates |\n| `assets\u002Fexecutive_business_review_template.md` | Executive stakeholder review for strategic accounts |\n\n---\n\n## Best Practices\n\n1. **Score regularly**: Run health scoring weekly for Enterprise, bi-weekly for Mid-Market, monthly for SMB\n2. **Act on trends, not snapshots**: A declining Green is more urgent than a stable Yellow\n3. **Combine signals**: Use all three scripts together for a complete customer picture\n4. **Calibrate thresholds**: Adjust segment benchmarks based on your product and industry\n5. **Document interventions**: Track what actions you took and outcomes for playbook refinement\n6. **Prepare with data**: Run scripts before every QBR and executive meeting\n\n---\n\n## Limitations\n\n- **No real-time data**: Scripts analyze point-in-time snapshots from JSON input files\n- **No CRM integration**: Data must be exported manually from your CRM\u002FCS platform\n- **Deterministic only**: No predictive ML -- scoring is algorithmic based on weighted signals\n- **Threshold tuning**: Default thresholds are industry-standard but may need calibration for your business\n- **Revenue estimates**: Expansion revenue estimates are approximations based on usage patterns\n\n---\n\n**Last Updated:** February 2026\n**Tools:** 3 Python CLI tools\n**Dependencies:** Python 3.7+ standard library 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