---
name: customer-success-manager
description: Monitors customer health, predicts churn risk, and identifies expansion opportunities using weighted scoring models for SaaS customer success. Use when analyzing customer accounts, reviewing retent...
---

# 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

- [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)

---

## Input Requirements

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

### Health Score Calculator

Required fields per customer object: `customer_id`, `name`, `segment`, `arr`, and nested objects `usage` (login_frequency, feature_adoption, dau_mau_ratio), `engagement` (support_ticket_volume, meeting_attendance, nps_score, csat_score), `support` (open_tickets, escalation_rate, avg_resolution_hours), `relationship` (executive_sponsor_engagement, multi_threading_depth, renewal_sentiment), and `previous_period` scores for trend analysis.

### Churn Risk Analyzer

Required fields per customer object: `customer_id`, `name`, `segment`, `arr`, `contract_end_date`, and nested objects `usage_decline`, `engagement_drop`, `support_issues`, `relationship_signals`, and `commercial_factors`.

### Expansion Opportunity Scorer

Required fields per customer object: `customer_id`, `name`, `segment`, `arr`, and nested objects `contract` (licensed_seats, active_seats, plan_tier, available_tiers), `product_usage` (per-module adoption flags and usage percentages), and `departments` (current and potential).

---

## 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

---

## How to Use

### Quick Start

```bash
# 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

```bash
# 1. Score customer health across portfolio
python scripts/health_score_calculator.py customer_portfolio.json --format json > health_results.json
# Verify: confirm health_results.json contains the expected number of customer records before continuing

# 2. Identify at-risk accounts
python scripts/churn_risk_analyzer.py customer_portfolio.json --format json > risk_results.json
# Verify: confirm risk_results.json is non-empty and risk tiers are present for each customer

# 3. Find expansion opportunities in healthy accounts
python scripts/expansion_opportunity_scorer.py customer_portfolio.json --format json > expansion_results.json
# Verify: confirm expansion_results.json lists opportunities ranked by priority

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

**Error handling:** If a script exits with an error, check that:
- The input JSON matches the required schema for that script (see Input Requirements above)
- All required fields are present and correctly typed
- Python 3.7+ is being used (`python --version`)
- Output files from prior steps are non-empty before piping into subsequent steps

---

## 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:**
```bash
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:**
```bash
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:**
```bash
python scripts/expansion_opportunity_scorer.py customer_data.json
python scripts/expansion_opportunity_scorer.py customer_data.json --format json
```

---

## 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 |

---

## 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 |

---

## Best Practices

1. **Combine signals**: Use all three scripts together for a complete customer picture
2. **Act on trends, not snapshots**: A declining Green is more urgent than a stable Yellow
3. **Calibrate thresholds**: Adjust segment benchmarks based on your product and industry per `references/health-scoring-framework.md`
4. **Prepare with data**: Run scripts before every QBR and executive meeting; reference `references/cs-playbooks.md` for intervention guidance

---

## 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

---

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