unit-economics

Compute ARR bridges, cohort matrices, LTV:CAC, and profit waterfalls from revenue data.

Updated Aug 23, 2026
One-click install
npx skills add https://github.com/brianping7/volc-financial-services-skill-sets --skill unit-economics-brianping7
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: unit-economics
Source: https://github.com/brianping7/volc-financial-services-skill-sets/tree/main/private-equity/unit-economics
Command: npx skills add https://github.com/brianping7/volc-financial-services-skill-sets --skill unit-economics-brianping7

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Evaluate and quantify the quality of recurring revenue and customer economics to support private equity diligence, portfolio monitoring, and investment decisions. The Skill consolidates ARR bridges, cohort retention, LTV:CAC, CAC payback, and profit waterfall analysis so analysts can identify growth durability, concentration risk, and margin drivers without manual spreadsheet assembly.

Core Features & Use Cases

  • ARR Bridge & Revenue Quality: Build period-to-period ARR bridges that decompose new business, expansion, contraction, and churn.
  • Cohort Analysis: Produce cohort matrices in absolute and index (base = 100%) form to reveal retention and expansion patterns.
  • Customer Economics: Calculate CAC, LTV, LTV:CAC ratios, CAC payback, and per-segment unit contribution margins.
  • Retention & Expansion Metrics: Report gross retention, net dollar retention (NDR), logo churn, and expansion rates with benchmark comparisons.
  • Profitability Waterfall & Outputs: Generate profit margin waterfalls and export results to an Excel workbook and summary slides for investment committees.
  • Use Case: Perform buy-side diligence on a SaaS target by ingesting customer-level or time-series revenue and churn data to surface red flags and benchmarked recommendations.

Quick Start

Analyze the company's customer-level revenue and churn data to produce an ARR bridge, cohort matrix, LTV:CAC, CAC payback, NDR, a profit waterfall, and an Excel dashboard with benchmark comparisons.

Frequently Asked Questions about unit-economics

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I calculate SaaS unit economics like LTV:CAC and CAC payback for diligence?

To calculate SaaS unit economics, ingest customer-level revenue and churn data to compute LTV:CAC ratios, CAC payback periods, and per-segment contribution margins, outputting benchmarked Excel workbooks and summary slides for investment committees.

What is the best way to build an ARR bridge and cohort matrix for a subscription business?

Building an ARR bridge and cohort matrix requires decomposing period-to-period revenue into new business, expansion, contraction, and churn, while producing cohort matrices in absolute and index form to reveal retention and expansion patterns.

How do I measure net dollar retention and gross retention from customer revenue data?

Measuring net dollar retention and gross retention involves analyzing time-series revenue data to report logo churn and expansion rates, generating cohort matrices that benchmark retention patterns against industry standards.

Can I assess revenue quality and concentration risk for private equity portfolio monitoring?

Assessing revenue quality and concentration risk for portfolio monitoring requires consolidating ARR bridges, cohort retention, and profit margin waterfalls to identify growth durability and margin drivers without manual spreadsheet assembly.

Do I need customer-level time-series revenue and cost inputs to evaluate recurring revenue businesses?

Evaluating recurring revenue businesses requires customer-level or time-series revenue and churn data as inputs to calculate unit economics, generate profit waterfalls, and export benchmarked Excel dashboards.

Why does my cohort analysis show inconsistent net dollar retention and expansion rates?

Inconsistent net dollar retention and expansion rates in cohort analysis often stem from unstructured customer-level revenue inputs, requiring standardized cohort matrices in index form to accurately isolate retention and expansion patterns.