saas-valuation-compression

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.

Always render the output as an inline visualization (using the Visualizer tool) plus a concise prose explanation. Do not just return a wall of numbers.

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Step-by-Step Workflow

1. Gather Data via Web Search

Search for each of the following. Run searches in parallel where possible.

For the target company:

• [company] funding rounds valuation ARR revenue
• [company] Series [X] raised valuation for each round
• [company] annual recurring revenue ARR [year] for each round date
• [company] investors lead investor [round]

For macro context:

• SaaS ARR valuation multiples [year] private market
• Use the known benchmark table below as fallback if search is thin.

For narrative context:

• [company] AI customers product announcement [year] — AI narrative premium?
• [company] growth rate churn NRR [year] — fundamentals shift?

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):

multiple_compression_pct = (later_multiple - earlier_multiple) / earlier_multiple × 100
valuation_growth_pct = (later_val - earlier_val) / earlier_val × 100
arr_growth_pct = (later_arr - earlier_arr) / earlier_arr × 100

Key insight: valuation_growth = arr_growth + multiple_change If ARR grows faster than the multiple compresses, absolute valuation still rises.

4. Attribute Compression to Causes

Use this checklist. For each cause, rate it: Primary / Contributing / Not applicable.

Macro / Rate Environment

• Was the earlier round during 2020–2021 ZIRP bubble? (adds ~2–5x artificial premium)
• Was the later round during 2022–2023 rate hikes? (removes bubble premium)
• Was the later round during or after the April 2026 Software Meltdown? (public SaaS down 40–86% from 52w highs; tariff/trade-war driven selloff crushed multiples sector-wide — even high-growth names like Figma -87%, monday.com -80%, HubSpot -70%, ServiceNow -58%)
• Reference: SaaS private market median multiples by period:

Period	Approx Median ARR Multiple (private)	Context
2019	~8–12x	Pre-pandemic baseline
2020	~12–18x	ZIRP begins, multiple expansion
2021 Q1–Q3 peak	~35–45x	Peak bubble
2022 H2	~15–20x	Rate hikes begin, first compression wave
2023 trough	~8–12x	Rate plateau, valuation reset
2024	~12–18x	AI narrative recovery, selective re-rating
2025 H1	~16–22x	Continued AI-driven recovery
2025 H2–2026 Q1	~10–16x	Tariff shock / trade-war selloff begins
2026 Q2 (Apr meltdown)	~6–10x	Software Meltdown — broad sector crash, public SaaS down 40–86% from 52w highs

(These are rough private market estimates. Public SaaS multiples are ~30–50% lower. The April 2026 figures reflect the acute selloff; private marks typically lag public by 1–2 quarters.)

Growth Deceleration

• Did YoY ARR growth rate slow materially between rounds? (most common cause)
• Did NRR/net retention drop?

Narrative Shift

• Did the company lose a major product story (e.g., lost PLG thesis, missed category leadership)?
• Did competitors emerge or incumbents catch up?

AI Premium (positive or negative)

• Does the company serve AI-native companies (OpenAI, Anthropic, etc.) as customers? → premium
• Did the company pivot to AI narrative credibly? → premium
• Did the company fail to articulate AI story? → discount vs peers
• Note: In the Apr 2026 meltdown, even strong AI narratives did not protect multiples — Snowflake (-53%), Datadog (-46%), MongoDB (-48%) all cratered despite AI tailwinds. AI premium may be necessary but not sufficient in a macro-driven selloff.

Competitive / Market

• Market saturation signal (e.g., Okta pressure on WorkOS, Auth0 competition)
• Customer concentration risk revealed

Investor Supply / Demand

• Was the later round smaller and more selective? → price discipline
• New tier of lead investor (e.g., Tier 1 growth fund vs seed fund)? → may signal higher or lower conviction

5. Build the Visualization

Use the Visualizer tool to render:

1. Metric cards row — valuation at each round, ARR at each round, multiple at each round, compression %
2. Line chart — ARR multiple over time for the company vs macro SaaS median
3. Bar chart — valuation growth vs ARR growth vs multiple change (decomposition)
4. Comparison bar — company compression vs 2–3 peer comparables (Vercel, Netlify, Fastly, or sector peers)
5. Cause attribution table inline in prose (Primary / Contributing / N/A per factor)

See design guidance: use teal for positive/growth, coral for compression/negative, gray for macro baseline, blue for valuation figures. Follow the CSS variable system throughout.

6. Write the Prose Summary

Structure as:

1. One-sentence verdict — e.g., "Multiple compressed 36% but ARR grew 5x, so absolute valuation rose 3.8x."
2. Primary cause — the #1 factor explaining compression
3. Narrative premium/discount — AI story, category leadership, or lack thereof
4. Comparable context — how does this company's compression compare to peers?
5. Forward implication — what would need to be true for the multiple to expand at next round?

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Output Format

Always produce:

• Inline visualization (Visualizer tool) — comes first
• Prose summary (5–8 sentences) — follows the visualization
• Optional: flag data confidence level if ARR had to be estimated

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Known Benchmarks & Comparables (pre-loaded)

Use these as context when search results are thin or for the comparison chart.

Company	Round pair	Earlier multiple	Later multiple	Compression %	Primary cause
Vercel	D → E (2021→2024)	~140x	~32x	-77%	ZIRP unwind + growth decel
WorkOS	B → C (2022→2026)	~105x	~67x	-36%	Partial ZIRP unwind; defended by AI narrative
Netlify	B → stalled (2021→?)	~90x	N/A	N/A	No new round; AI narrative absent
Fastly	Public (2021 peak→2024)	~35x rev	~3x rev	-91%	No AI pivot, growth decel
Stripe	—	—	—	—	Private; est. flat/compressed 2021→2023 down round
HashiCorp	Acquired by IBM 2024	—	—	—	Acq at ~8x ARR vs ~40x peak

April 2026 Software Meltdown — Public SaaS Drawdowns

As of April 9, 2026, a broad tariff/trade-war driven selloff crushed public software valuations. Use these as reference for how private multiples will lag-compress over the following 1–2 quarters.

Ticker	Company	Δ from 52w High	Sector relevance
FIG	Figma	-86.7%	Design/dev tools — worst hit
MNDY	monday.com	-80.2%	Work management SaaS
TEAM	Atlassian	-75.7%	Dev tools / collaboration
HUBS	HubSpot	-69.9%	Marketing/CRM SaaS
WIX	WIX	-65.1%	Website builder
GTLB	GitLab	-63.6%	DevOps
CVLT	Commvault	-61.7%	Data protection
WDAY	Workday	-59.1%	HR/Finance SaaS
NOW	ServiceNow	-57.8%	Enterprise IT workflows
INTU	Intuit	-56.0%	FinTech/SMB SaaS
SNOW	Snowflake	-52.8%	Data cloud
KVYO	Klaviyo	-52.9%	Marketing automation
DOCU	DocuSign	-52.3%	eSignature
MDB	MongoDB	-47.9%	Database
SAP	SAP	-47.6%	Enterprise ERP
DDOG	Datadog	-45.7%	Observability
APP	AppLovin	-47.6%	AdTech/mobile
CRM	Salesforce	-42.5%	CRM market leader
ADBE	Adobe	-34.6%	Creative/doc SaaS
ZM	Zoom	-13.9%	Video/collab (already de-rated)

Source: @speculator_io, April 9, 2026. Average drawdown across tracked software names: ~50–55%.

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Edge Cases

• Down round: Multiple and absolute valuation both dropped. Note dilution implications.
• No public ARR: Use customer count x estimated ARPC, and label as estimate with +/- range.
• Single round only: Compute multiple vs sector median for that date; can't do compression analysis. Explain this.
• Pre-revenue: Use forward ARR or GMV multiple if applicable; note the different basis.
• Acqui-hire / strategic acquisition: Acquisition price often reflects strategic premium or distress, not pure ARR multiple — flag this.

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saas-valuation-compression

Analyze SaaS company valuation compression between funding rounds.

What it does

This skill researches a SaaS company's funding history and computes ARR-based valuation multiples at each round, then explains the compression (or expansion) using a structured framework:

• Data gathering — funding rounds, valuations, ARR, lead investors via web search
• Compression metrics — ARR multiple change, valuation growth decomposition
• Cause attribution — macro/ZIRP, growth deceleration, narrative shifts, AI premium, competitive dynamics
• Visualization — metric cards, line charts, bar charts, and peer comparisons
• Prose summary — one-sentence verdict, primary cause, comparable context, forward implications

Triggers

• "valuation compression" or "ARR multiple" analysis
• "round-to-round valuation" comparisons
• "why did the multiple compress/expand"
• Comparing a company's funding rounds
• Any multi-round SaaS valuation analysis

Known benchmarks

Includes pre-loaded comparables for Vercel, WorkOS, Netlify, Fastly, Stripe, and HashiCorp with compression percentages and primary causes.

Platform

Works on All platforms (Claude.ai, Claude Code, and other supported agents). Uses web search for data gathering and the Visualizer tool for inline charts.

Setup

# As a plugin (recommended — installs all skills)
npx plugins add himself65/finance-skills --plugin finance-market-analysis

# Or install just this skill
npx skills add himself65/finance-skills --skill saas-valuation-compression

See the main README for more installation options.