U
unit-economics
by @anthropicsv
4.3(120)
この Skill は、PE 投資先や経常収益型ビジネスのユニットエコノミクスを分析します。ARR ブリッジと年次コホート行列を構築し、CAC・LTV・LTV:CAC 比率・グロス/ネット継続率・チャーン・回収期間を算出し、売上から EBITDA までのマージンウォーターフォールを整理します。さらに Rule of 40 やマジックナンバーなどの SaaS ベンチマークと比較し、収益品質スコアと赤旗事項を提示します。デューデリジェンスで収益品質や顧客エコノミクスを評価したいときに使います。
インストール方法
npx skills add https://github.com/anthropics/financial-services --skill unit-economicscompare_arrows
Before / After 効果比較
2 组使用前
この Skill がない場合、複数システムの出力を手作業で突き合わせて ARR ブリッジと年次コホート行列を組み立て、数式を何度も検算するため、収益品質の基礎資料づくりに半日以上かかっていました。
使用後
この Skill は定型ワークフローに沿って ARR ブリッジ・コホート行列・ユニットエコノミクス・ダッシュボードをベンチマーク比較付きで出力するため、同じ分析を約 30 分で下書きでき、分析者は結論の検討に時間を割けます。
SKILL.md
Unit Economics Analysis
Workflow
Step 1: Identify Business Model
Determine the revenue model to tailor the analysis:
- SaaS / Subscription: ARR, net retention, cohorts
- Recurring services: Contract value, renewal rates, upsell
- Transaction / usage-based: Revenue per transaction, volume trends, take rate
- Hybrid: Break down by revenue stream
Step 2: Core Metrics
ARR / Revenue Quality
- ARR bridge: Beginning ARR → New → Expansion → Contraction → Churn → Ending ARR
- ARR by cohort: Vintage analysis — how does each annual cohort retain and grow?
- Revenue concentration: Top 10/20/50 customers as % of total
- Revenue by type: Recurring vs. non-recurring vs. professional services
- Contract structure: ACV distribution, multi-year %, auto-renewal %
Customer Economics
- CAC (Customer Acquisition Cost): Total S&M spend / new customers acquired
- LTV (Lifetime Value): (ARPU × Gross Margin) / Churn Rate
- LTV:CAC ratio: Target >3x for healthy businesses
- CAC payback period: Months to recover acquisition cost
- Blended vs. segmented: Break down by customer segment (enterprise vs. SMB vs. mid-market)
Retention & Expansion
- Gross retention: % of beginning ARR retained (excludes expansion)
- Net retention (NDR): % of beginning ARR retained including expansion
- Logo churn: % of customers lost
- Dollar churn: % of revenue lost (often different from logo churn)
- Expansion rate: Upsell + cross-sell as % of beginning ARR
Cohort Analysis
Build a cohort matrix showing:
| Cohort | Year 0 | Year 1 | Year 2 | Year 3 | Year 4 |
|---|---|---|---|---|---|
| 2020 | $1.0M | $1.1M | $1.2M | $1.1M | |
| 2021 | $1.5M | $1.7M | $1.8M | ||
| 2022 | $2.0M | $2.3M | |||
| 2023 | $3.0M |
Show both absolute $ and indexed (Year 0 = 100%) views.
Margin Waterfall
- Revenue → Gross Profit → Contribution Margin → EBITDA
- Fully loaded unit economics: what does it cost to acquire, serve, and retain a customer?
- Gross margin by revenue stream (subscription vs. services vs. other)
Step 3: Benchmarking
Compare unit economics to relevant benchmarks:
- SaaS Rule of 40: Growth rate + EBITDA margin > 40%
- SaaS Magic Number: Net new ARR / prior period S&M spend > 0.75x
- NDR benchmarks: Best-in-class >120%, good >110%, concerning <100%
- LTV:CAC: Best-in-class >5x, good >3x, concerning <2x
- Gross retention: Best-in-class >95%, good >90%, concerning <85%
- CAC payback: Best-in-class <12mo, good <18mo, concerning >24mo
Step 4: Revenue Quality Score
Synthesize into a revenue quality assessment:
| Factor | Score (1-5) | Notes |
|---|---|---|
| Recurring % | ||
| Net retention | ||
| Customer concentration | ||
| Cohort stability | ||
| Growth durability | ||
| Margin profile | ||
| Overall |
Step 5: Output
- Excel workbook with ARR bridge, cohort matrix, unit economics dashboard
- Summary slide with key metrics and benchmarks
- Red flags and areas for further diligence
Important Notes
- Always ask for raw customer-level data if available — aggregate metrics can hide problems
- NDR above 100% can mask high gross churn if expansion is strong enough — always show both
- Cohort analysis is the single most important view for revenue quality — push for this data
- Differentiate between contracted ARR and actual recognized revenue
- For usage-based models, focus on consumption trends and expansion patterns rather than traditional ARR metrics
- Professional services revenue should be evaluated separately — it's not recurring and margins are typically lower
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統計データ
インストール数1.3K
評価4.3 / 5.0
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更新日2026年10月19日
比較事例2 件
ユーザー評価
4.3(120)
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37%
4
43%
3
13%
2
5%
1
2%
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対応プラットフォーム
🤖claude-code
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作成2026年9月14日
最終更新2026年10月19日