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.

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

# 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

Workflow Integration

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:

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:

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:

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

Combine signals: Use all three scripts together for a complete customer picture
Act on trends, not snapshots: A declining Green is more urgent than a stable Yellow
Calibrate thresholds: Adjust segment benchmarks based on your product and industry per references/health-scoring-framework.md
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