mcpField
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china-unit-economics

china-unit-economics

github.com/jwangkunData & databases87570 installsUpdated 6w ago
Repository

About china-unit-economics

A unit economics analysis skill for A-share and China-market companies, covering per-unit returns, customer economics, and cohort performance. It combines CAC, LTV, and retention metrics with China-specific channels and consumer context for benchmarking and sensitivity analysis.

Key features

  1. 01Define units across business models
  2. 02Calculate CAC, LTV, and payback
  3. 03Build retention and revenue cohorts
  4. 04Compare China industry benchmarks
  5. 05Run sensitivity analysis on key variables

Use cases

  1. 01Assess acquisition cost and repeat purchases for consumer brands
  2. 02Analyze SaaS customer value and retention
  3. 03Compare e-commerce order economics
  4. 04Review unit economics before scaling

MCP support

The skill specifies iFind MCP or AkShare MCP for listed-company financial data, with company disclosures, management guidance, and industry sources as additional references.

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/jwangkun/claude-for-financial-services-cn --skill china-unit-economics

After installing, type/china-unit-economicsin Claude Code to confirm it loaded.

Details

From the project README

china-unit-economics

Purpose

Analyze A股及中国市场单元经济模型 — per-unit economics, customer economics, and cohort analysis for portfolio companies.

Key Metrics

Core Framework (Same as Global)
MetricFormulaTarget
LTV (Customer Lifetime Value)ARPU × Lifetime × Gross Margin>3x CAC
CAC (Customer Acquisition Cost)Sales & Marketing / New Customers<12-month payback
LTV/CACLTV / CAC>3.0x
CAC Payback (months)CAC / (ARPU × Gross Margin %)<12 months
NRR (Net Revenue Retention)(Start + Expansion - Churn) / Start>100%
GRR (Gross Revenue Retention)(Start - Churn) / Start>85%
ARRAnnual Recurring RevenueGrowing
Churn RateLost customers / Total<5% monthly
China-Specific Metrics
MetricDescriptionChina Context
获客成本CAC in Chinese context线上投放 + 线下渠道
用户生命周期价值LTV for Chinese consumersHigher for tier-1
复购率Repeat purchase rateCritical for DTC brands
客单价Average order value价格敏感 market
渠道成本Channel economics经销商 vs DTC
补贴依赖Subsidy / discount dependenceCommon in China

Data Sources

Primary: iFind MCP (Tier-1 付费) / AkShare MCP (Tier-2 免费备选)
# For public companies, extract proxies
get_financials(ticker, "income")    → Revenue, opex
get_financials(ticker, "balance")   → Customer-related assets
get_financials(ticker, "cashflow")  → Cash flow
Secondary Sources
  • 巨潮 — company filings with segment data
  • 管理层指引 — management guidance
  • 券商研报 — unit economics estimates
  • 行业数据 — industry benchmarks

Workflow

Step 1: Define Unit Economics Model

Business model identification:

Business ModelUnit DefinitionRevenue Model
消费品每单 / 每用户Transaction
SaaS每客户 (年)Subscription
电商每订单 / 每用户Transaction
制造业每件 / 每吨Unit sales
平台每GMV / 每交易Take rate
广告每曝光 / 每点击CPM / CPC
Step 2: Build Unit Economics Model

Standard model:

# Unit economics calculation
Unit_Price = XX元
Unit_Cost = XX元 (COGS)
Unit_Contribution = Unit_Price - Unit_Cost

# Customer economics (for subscription/DTC)
CAC = XX元 (acquisition cost per customer)
ARPU = XX元/年 (average revenue per user)
Gross_Margin = XX%
Customer_Lifetime = X年 (or 1/churn rate)
LTV = ARPU × Lifetime × Gross_Margin
LTV_CAC = LTV / CAC
Payback_Months = CAC / (ARPU × Gross_Margin / 12)
Step 3: Cohort Analysis

Cohort table (retention):

CohortMonth 0Month 1Month 2Month 3Month 6Month 12
Jan100%XX%XX%XX%XX%XX%
Feb100%XX%XX%XX%XX%XX%
Mar100%XX%XX%XX%XX%XX%

Cohort revenue:

CohortMonth 0Month 1Month 2Month 3Month 6Month 12
Jan¥X¥X¥X¥X¥X¥X
Feb¥X¥X¥X¥X¥X¥X
Step 4: Benchmarking

China-specific benchmarks by industry:

IndustryCAC PaybackLTV/CACTypical ARPU
消费品牌6-12 months3-5x¥200-500
SaaS (ToB)12-18 months3-5x¥5,000-50,000
电商1-3 months2-4x¥100-300
游戏1-2 months4-10x¥50-200
在线教育3-6 months2-4x¥1,000-5,000
金融科技6-12 months3-5x¥500-2,000
Step 5: Sensitivity Analysis

Key sensitivities:

Variable-20%-10%Base+10%+20%
ARPU
Gross margin
Customer lifetime
CAC
Step 6: Recommendations

Based on unit economics:

LTV/CACAssessmentRecommendation
>5xExcellentScale aggressively
3-5xGoodScale with monitoring
2-3xAcceptableOptimize CAC/margin
1-2xPoorFix model before scaling
<1xBrokenFundamental rethink

China-Specific Considerations

Customer Acquisition in China
ChannelCACQualityNotes
线上投放 (Online ads)MediumVariable抖音, 小红书, 百度
社交裂变 (Social referral)LowMedium拼多多 model
经销商 (Distribution)LowDepends渠道下沉
线下门店 (Offline stores)HighHighPremium experience
直播带货 (Livestream)MediumVariable抖音, 淘宝直播
私域运营 (Private traffic)LowHighWeChat ecosystem
China Consumer Behavior
BehaviorImpact on Unit Economics
价格敏感Lower ARPU, higher churn on price increases
社交驱动Viral referral reduces CAC
品牌忠诚度Higher LTV for established brands
渠道偏好Multi-channel complexity
促销依赖Revenue quality concerns
E-commerce Unit Economics

Key metrics for China e-commerce:

MetricFormulaBenchmark
GMVGross merchandise valueTop-line
Take rateRevenue / GMV3-10%
Customer acquisitionCAC / order¥20-200
Return rateReturns / orders5-20% (sector varies)
AOVRevenue / orders¥50-500
Fulfillment costLogistics / order¥5-30

Quality Checks

Before delivering:

  • Unit defined clearly
  • Revenue model correct
  • CAC/LTV calculated correctly
  • Cohort data (if available) incorporated
  • Benchmarking relevant
  • Sensitivities run
  • Recommendations actionable

Data Source Mode Switch: Set env var IFIND_DATA_SOURCE_MODE to control data source preference.

  • ifind-only (strict): Use iFind only, error if unavailable
  • ifind-fallback (default): iFind preferred, fallback to AkShare
  • akshare-only, wind-only (Wind only), wind-fallback (Wind first, fallback to iFind → AkShare): Skip iFind, use AkShare only

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