customer-success-manager

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---
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 retention metrics, scoring at-risk customers, or when the user mentions churn, customer health scores, upsell opportunities, expansion revenue, retention analysis, or customer analytics. Runs three Python CLI tools to produce deterministic health scores, churn risk tiers, and prioritized expansion recommendations across Enterprise, Mid-Market, and SMB segments.
license: MIT
metadata:
  version: 1.0.0
  author: Alireza Rezvani
  category: business-growth
  domain: customer-success
  updated: 2026-02-06
  python-tools: health_score_calculator.py, churn_risk_analyzer.py, expansion_opportunity_scorer.py
  tech-stack: customer-success, saas-metrics, health-scoring
---

# 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_res