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Customer Success Manager TEXT

Calculates deterministic customer health scores, churn risk tiers, and expansion opportunity rankings for SaaS accounts using weighted scoring models. It processes JSON input files to generate prioritized retention and revenue growth recommendations across Enterprise, Mid-Market, and SMB segments.

7 uses this weeksource alirezarezvanilicence MIT
Quality gate: FAIL · better answers · measured 2026-10-23
Blind-judged against the bare model on 2 requests: average lift -4.0 points (specificity, structure, method, fit).
won 0/2, lift -4.0, tokens ×115.27
Security scan: clean · 2026-10-24 · 13 files · ran in sandbox
no network calls, shell tricks, credential access or instruction tampering found in its files.

Try asking

Say it in your own words; the assistant finds this recipe by itself. Examples:

“Kør sundhedsscore-beregneren på den vedhæftede fil customer_data.json og vis resultatet som tekst.”
“Hvilke af vores kunder er i høj risiko for at sige op, baseret på deres faldende logindfrekvens og NPS-score?”
“Analyser ekspansionsmulighederne for vores SMB-segment og find kunder, der kan opgraderes til et højere plan.”
“Run the health score calculator on the attached customer_portfolio.json and output the results as JSON.”
“I need to identify which of our Enterprise accounts are at critical risk of churning before the next renewal cycle.”
“Analyze the usage decline and engagement drop signals in this dataset to flag accounts for immediate CSM intervention.”
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Good for

customer-successchurn-predictionhealth-scoresaas-analyticsexpansion-revenueretentionupsellpython-cliaccount-management

Not for

Do not use this skill for real-time customer support ticket triage or immediate incident resolution, as it is an analytical tool rather than an operational one. It is not suitable for general marketing campaign analysis or lead generation, which would require different data structures and scoring models. Avoid using it if you lack the specific JSON schema fields required for health, churn, or expansion scoring.

Details

When it is the right choice

Use this skill when you need to analyze a portfolio of customer accounts to determine their current health status and likelihood of churning. It is ideal for generating prioritized lists of at-risk customers requiring intervention or identifying specific upsell and cross-sell opportunities based on usage and contract data. This skill is appropriate when you have structured JSON data containing usage, engagement, support, and relationship metrics for your customer base.

The full recipe the assistant reads

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

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

# 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
# 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:

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:

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:

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

Files in this skill (14)
  • LICENSE
  • SKILL.md
  • assets/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
  • references/cs-metrics-benchmarks.md
  • references/cs-playbooks.md
  • references/health-scoring-framework.md
  • scripts/churn_risk_analyzer.py
  • scripts/expansion_opportunity_scorer.py
  • scripts/health_score_calculator.py
Where it comes from

Vendored from alirezarezvani/claude-skills, commit 27cfcd0fb0a4, path business-growth/skills/customer-success-manager, licence MIT. The upstream licence file ships inside the skill.