Analyze SaaS customer data with three Python tools that calculate account health, rank churn risk, and prioritize expansion opportunities. Use repeatable weighted scores to plan customer outreach and prepare quarterly business reviews.
中文简介正在准备中。当前显示英语简介。
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.
安装前可以查看交给 AI 代理的指令内容。
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.
All scripts accept a JSON file as positional input argument. See assets/sample_customer_data.json for complete schema examples and sample data.
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.
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.
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).
All scripts support two output formats via the --format flag:
text (default): Human-readable formatted output for terminal viewingjson: Machine-readable JSON output for integrations and pipelines# 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
# 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:
python --version)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:
Usage:
python scripts/health_score_calculator.py customer_data.json
python scripts/health_score_calculator.py customer_data.json --format json
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:
Usage:
python scripts/churn_risk_analyzer.py customer_data.json
python scripts/churn_risk_analyzer.py customer_data.json --format json
Purpose: Identify upsell, cross-sell, and expansion opportunities with revenue estimation and priority ranking.
Expansion Types:
Usage:
python scripts/expansion_opportunity_scorer.py customer_data.json
python scripts/expansion_opportunity_scorer.py customer_data.json --format json
| 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 |
| 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 |
references/health-scoring-framework.mdreferences/cs-playbooks.md for intervention guidanceLast Updated: February 2026 Tools: 3 Python CLI tools Dependencies: Python 3.7+ standard library only
还没有评价。欢迎分享你的使用感受。
无需登录即可发表。欢迎分享使用感受、提问，或说明在哪些环境下无法运行。
还没有评论。来写第一条吧。
Audit web applications for accessibility issues against WCAG 2.2 Level A and AA, the web accessibility standards. Get framework-specific code fixes, contrast checks, verification steps, and reports, with options for automated checks in development pipelines.
中文简介正在准备中。当前显示英语简介。
Plan A/B tests that compare page variations, from headlines to pricing layouts. Define a hypothesis, choose success metrics, calculate sample size and duration, and prepare launch checks and results reports to support decisions.
中文简介正在准备中。当前显示英语简介。
Create paid ad headlines and body copy tailored to each platform's character limits. Use existing performance data to develop variations, explore new messaging angles, and check copy for length, quality, and potential rejection risks.
中文简介正在准备中。当前显示英语简介。
Review code changes from three critical perspectives: production failures, maintainability, and security. Get a consolidated report that ranks findings by severity and provides merge guidance, helping examine assumptions that an ordinary self-review may overlook.
中文简介正在准备中。当前显示英语简介。
Audit and improve content intended for citation in AI answers. Review evidence, author information, and structure, generate revised content and structured metadata, and keep a local record of observed citations across AI services.
中文简介正在准备中。当前显示英语简介。
Create signed records for consequential agent actions such as deployments, deletions, and payments. Decide which actions need evidence, record the agent, action, target, and policy, then verify the resulting certificate offline to detect later changes.
中文简介正在准备中。当前显示英语简介。