customer-success-manager

Files (13)

• SKILL.md
• executive_business_review_template.md · assets/
• expected_output.json · assets/
• onboarding_checklist_template.md · assets/
• qbr_template.md · assets/
• sample_customer_data.json · assets/
• success_plan_template.md · assets/
• cs-metrics-benchmarks.md · references/
• cs-playbooks.md · references/
• health-scoring-framework.md · references/
• churn_risk_analyzer.py · scripts/
• expansion_opportunity_scorer.py · scripts/
• health_score_calculator.py · scripts/

SKILL.md (first lines)

# 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.

---

## Table of Contents

- [Capabilities](#capabilities)
- [Input Requirements](#input-requirements)
- [Output Formats](#output-formats)
- [How to Use](#how-to-use)
- [Scripts](#scripts)
- [Reference Guides](#reference-guides)
- [Templates](#templates)
- [Best Practices](#best-practices)
- [Limitations](#limitations)

---

## 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

---

## Input Requirements

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

### Health Score Calculator

```json
{
  "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

```json
{
  "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

From aAAaqwq/AGI-Super-Team, MIT.

More from aAAaqwq/AGI-Super-Team