Saas Valuation Compression
Analyze how a private SaaS company's ARR valuation multiple changed across funding...
Install
npx skills add himself65/finance-skills --skill saas-valuation-compression -g -a claude-code -y- Repository
- himself65/finance-skills3.5k
- Category
- Marketing
- Updated
- 10d ago
- License
- MIT
- Context cost
- Mediumabout 1,643 tokens
- Runs scripts
- No, instructions only
What it does
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.
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.mdwhen 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):
