customer-success-manager

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Customer Success Manager

客户成功经理

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.

面向生产环境的客户成功分析工具，支持多维度健康评分、流失风险预测及业务扩张机会识别。包含三款Python CLI工具，仅使用Python标准库即可提供确定性、可重复的分析结果——无需外部依赖、无需调用API、无需机器学习模型。

Capabilities

功能特性

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

客户健康评分：基于使用情况、互动程度、支持服务及客户关系多维度的加权评分体系，采用红/黄/绿三色分类标识
流失风险分析：检测客户行为信号，结合分层干预手册及续约倒计时权重系数评估流失风险
扩张机会评分：分析产品使用深度、挖掘空白业务场景、估算营收机会，并按投入产出比优先级排序
细分客群基准对比：针对企业级、中大型、SMB客群配置个性化阈值
趋势分析：通过跨周期对比识别客户健康状况的改善或下滑趋势
高管汇报支持：提供QBR模板、客户成功计划及高管业务复盘模板

Input Requirements

输入要求

All scripts accept a JSON file as positional input argument. See

assets/sample_customer_data.json

for complete examples.

所有脚本均接受JSON文件作为位置输入参数。完整示例可参考

assets/sample_customer_data.json

。

Health Score Calculator

健康评分计算器

json

{
  "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
      }
    }
  ]
}

json

{
  "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

流失风险分析器

json

{
  "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
      }
    }
  ]
}

json

{
  "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

扩张机会评分器

json

{
  "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"]
      }
    }
  ]
}

json

{
  "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

输出格式

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

所有脚本通过

--format

参数支持两种输出格式：

text
（默认）：适合终端查看的人类可读格式化输出
json
：适合系统集成与数据流水线的机器可读JSON输出

How to Use

使用方法

Quick Start

快速开始

bash

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bash

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Health scoring

计算客户健康评分

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

分析流失风险

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

评估扩张机会

python scripts/expansion_opportunity_scorer.py assets/sample_customer_data.json python scripts/expansion_opportunity_scorer.py assets/sample_customer_data.json --format json

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python scripts/expansion_opportunity_scorer.py assets/sample_customer_data.json python scripts/expansion_opportunity_scorer.py assets/sample_customer_data.json --format json

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Workflow Integration

工作流集成

bash

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bash

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1. Score customer health across portfolio

1. 对全量客户计算健康评分

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

2. Identify at-risk accounts

2. 识别高风险客户

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

3. Find expansion opportunities in healthy accounts

3. 在健康客户中挖掘扩张机会

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

4. Prepare QBR using templates

4. 使用模板准备QBR汇报

Reference: assets/qbr_template.md

参考：assets/qbr_template.md

---

---

Scripts

脚本说明

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:

bash

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

用途：提供多维度客户健康评分、趋势分析及细分客群基准对比。

维度与权重：

维度	权重	指标
使用情况	30%	登录频率、功能使用率、DAU/MAU 比值
互动程度	25%	支持工单量、会议出席率、NPS/CSAT 评分
支持服务	20%	未结工单数量、升级工单占比、平均解决时长
客户关系	25%	高管对接人互动频率、多线程对接深度、续约意向

分类标准：

绿色（75-100）：健康状态——客户已实现产品价值
黄色（50-74）：需关注——需密切监控
红色（0-49）：高风险——需立即干预

使用方式：

bash

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:

bash

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

用途：识别高风险客户，结合行为信号检测及分层干预建议。

风险信号权重：

信号类别	权重	指标
使用频次下滑	30%	登录趋势、功能使用率变化、DAU/MAU 比值变化
互动程度下降	25%	会议取消次数、响应时长、NPS 评分变化
支持服务问题	20%	未结升级工单、未解决关键问题、满意度趋势
客户关系信号	15%	关键对接人离职、对接人变更、竞品提及次数
商业因素	10%	合同类型、价格投诉、预算削减提及

风险等级：

极高风险（80-100）：需立即上报高管
高风险（60-79）：需客户成功经理紧急干预
中风险（40-59）：需主动沟通
低风险（0-39）：常规监控即可

使用方式：

bash

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:

bash

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

用途：识别向上销售、交叉销售及业务扩张机会，估算营收潜力并排序优先级。

扩张类型：

向上销售：升级至更高版本或增加现有产品用量
交叉销售：新增产品模块
业务扩张：增加授权席位或覆盖新部门

使用方式：

bash

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

Reference Guides

参考指南

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

参考文档	描述
`references/health-scoring-framework.md`	完整的健康评分方法论、维度定义、权重依据及阈值校准说明
`references/cs-playbooks.md`	各风险等级的干预手册、新客户上线、续约、扩张及升级流程
`references/cs-metrics-benchmarks.md`	行业基准数据，包含NRR、GRR、流失率、健康评分、扩张率等按细分客群及行业划分的指标

Templates

模板资源

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

模板	用途
`assets/qbr_template.md`	季度业务汇报（QBR）演示结构
`assets/success_plan_template.md`	包含目标、里程碑及指标的客户成功计划
`assets/onboarding_checklist_template.md`	90天新客户上线 checklist，含阶段验收节点
`assets/executive_business_review_template.md`	面向战略客户的高管业务复盘模板

Best Practices

最佳实践

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

定期评分：企业级客户每周计算健康评分，中大型客户每两周一次，SMB客户每月一次
关注趋势而非快照：持续下滑的绿色客户比稳定的黄色客户更需关注
组合使用工具：同时运行三款脚本以获取完整的客户画像
校准阈值：根据自身产品及行业特性调整细分客群的基准阈值
记录干预行动：跟踪所采取的行动及结果，优化干预手册
数据驱动汇报：在QBR及高管会议前运行脚本准备数据

Limitations

局限性

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

无实时数据支持：脚本仅分析JSON输入文件中的时点快照数据
无CRM集成：需手动从CRM/客户成功平台导出数据
仅确定性分析：无预测性机器学习模型，评分基于加权算法
阈值需调整：默认阈值为行业通用标准，可能需根据业务校准
营收估算为近似值：扩张营收估算基于使用模式推导，存在误差

最后更新时间：2026年2月 工具集：3款Python CLI工具 依赖要求：仅需Python 3.7+标准库

customer-success-manager

Original

Translation

Customer Success Manager

客户成功经理

Table of Contents

目录

Capabilities

功能特性

Input Requirements

输入要求

Health Score Calculator

健康评分计算器

Churn Risk Analyzer

流失风险分析器

Expansion Opportunity Scorer

扩张机会评分器

Output Formats

输出格式

How to Use

使用方法

Quick Start

快速开始

Health scoring

计算客户健康评分

Churn risk analysis

分析流失风险

Expansion opportunity scoring

评估扩张机会

Workflow Integration

工作流集成

1. Score customer health across portfolio

1. 对全量客户计算健康评分

2. Identify at-risk accounts

2. 识别高风险客户

3. Find expansion opportunities in healthy accounts

3. 在健康客户中挖掘扩张机会

4. Prepare QBR using templates

4. 使用模板准备QBR汇报

Reference: assets/qbr_template.md

参考：assets/qbr_template.md

Scripts

脚本说明

1. health_score_calculator.py

1. health_score_calculator.py

2. churn_risk_analyzer.py

2. churn_risk_analyzer.py

3. expansion_opportunity_scorer.py

3. expansion_opportunity_scorer.py

Reference Guides

参考指南

Templates

模板资源

Best Practices

最佳实践

Limitations

局限性