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

Input Requirements

Output Formats

How to Use

Scripts

Reference Guides

Templates

Best Practices

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
{
"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
{
"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
{
"customers"
:
[
{
"customer_id"
:
"CUST-001"
,
"name"
:
"Acme Corp"
,
"segment"
:
"enterprise"
,
"arr"
:
120000
,
"contract"
:
{
"licensed_seats"
:
100
,
"active_seats"
:
95
,
"plan_tier"
:
"professional"
,
"available_tiers"
:
[
"professional"
,
"enterprise"
,
"enterprise_plus"
]
}
,
"product_usage"
:
{
"core_platform"
:
{
"adopted"
:
true
,
"usage_pct"
:
85
}
,
"analytics_module"
:
{
"adopted"
:
true
,
"usage_pct"
:
60
}
,
"integrations_module"
:
{
"adopted"
:
false
,
"usage_pct"
:
0
}
,
"api_access"
:
{
"adopted"
:
true
,
"usage_pct"
:
40
}
,
"advanced_reporting"
:
{
"adopted"
:
false
,
"usage_pct"
:
0
}
}
,
"departments"
:
{
"current"
:
[
"engineering"
,
"product"
]
,
"potential"
:
[
"marketing"
,
"sales"
,
"support"
]
}
}
]
}
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

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

1. Score customer health across portfolio

python scripts/health_score_calculator.py customer_portfolio.json --format json

health_results.json

2. Identify at-risk accounts

python scripts/churn_risk_analyzer.py customer_portfolio.json --format json

risk_results.json

3. Find expansion opportunities in healthy accounts

python scripts/expansion_opportunity_scorer.py customer_portfolio.json --format json

expansion_results.json

4. Prepare QBR using templates

Reference: assets/qbr_template.md

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:

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:

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:

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

Score regularly

Run health scoring weekly for Enterprise, bi-weekly for Mid-Market, monthly for SMB

Act on trends, not snapshots

A declining Green is more urgent than a stable Yellow

Combine signals

Use all three scripts together for a complete customer picture

Calibrate thresholds

Adjust segment benchmarks based on your product and industry

Document interventions

Track what actions you took and outcomes for playbook refinement

Prepare with data

Run scripts before every QBR and executive meeting

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

customer-success-manager

安装

Health scoring

Churn risk analysis

Expansion opportunity scoring

1. Score customer health across portfolio

2. Identify at-risk accounts

3. Find expansion opportunities in healthy accounts

4. Prepare QBR using templates

Reference: assets/qbr_template.md