# Saas Valuation Compression

Analyze how a private SaaS company's ARR valuation multiple changed across funding...

- Repository: https://github.com/himself65/finance-skills (3487 stars)
- Category: Marketing
- License: MIT
- Last updated: 2026-10-25
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add himself65/finance-skills --skill saas-valuation-compression -g -a claude-code -y`
Codex: `npx skills add himself65/finance-skills --skill saas-valuation-compression -g -a codex -y`
Cursor: `npx skills add himself65/finance-skills --skill saas-valuation-compression -g -a cursor -y`
Gemini CLI: `npx skills add himself65/finance-skills --skill saas-valuation-compression -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add himself65/finance-skills`

## Description

Analyze how a private SaaS company's ARR valuation multiple changed across funding rounds, and attribute the compression or expansion to rate cycles and macro selloffs, growth deceleration, narrative shifts (including an AI premium), competition, and investor demand, benchmarked against private-market medians and peers. Use this skill whenever the user asks about valuation compression, ARR multiples, round-to-round valuation or multiple changes, down rounds, or wants to compare a VC-backed software company's funding rounds. Research the rounds rather than answering from memory.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# SaaS Valuation Compression Analyzer

## What This Skill Does

For a given SaaS company, research its funding history and compute ARR-based valuation
multiples at each round. Then explain the compression (or expansion) using a structured
framework that covers macro rates, growth trajectory, narrative shifts, and comparables.

Render the output as an inline visualization (using the Visualizer tool) plus a concise
prose explanation, rather than a wall of numbers.

---

## Workflow

### 1. Gather Data via Web Search

Research these, running independent searches in parallel:

- **Each funding round of the target company**, round name, date, amount raised, post-money valuation, and lead investor.
- **ARR at or near each round date**, from press coverage, founder interviews, or investor posts; note when a figure is estimated.
- **Growth and retention around each round**, ARR growth rate, NRR, churn, notable customers.
- **Narrative context**, AI positioning and product launches, category leadership, competitive moves.
- **Private-market SaaS multiples at each round date**, fall back on the dated tables in `references/benchmarks.md` when search is thin.

### 2. Build the Data Model

For each funding round, extract or estimate:

| Field | How to get it |
|---|---|
| Round name | Direct from search |
| Date | Direct from search |
| Amount raised | Direct from search |
| Post-money valuation | Direct or compute from ownership %; if unavailable, note as estimated |
| ARR at round date | Search explicitly; if not found, estimate from customer count x ARPC or interpolate |
| ARR multiple | `valuation / ARR` |
| Lead investor | Direct |

**ARR estimation heuristics (when not public):**
- Seed/Series A: ARR often $500K, $3M
- Series B: typically $5M, $20M
- Series C: typically $20M, $60M
- Cross-check against customer count x average deal size if available

### 3. Compute Compression Metrics

For each consecutive round pair (e.g., B → C):

The rest: https://github.com/himself65/finance-skills/blob/HEAD/plugins/market-analysis/skills/saas-valuation-compression/SKILL.md