saas-valuation-compression

SKILL.md

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.

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:

Metric cards row — valuation at each round, ARR at each round, multiple at each round, compression %

Line chart — ARR multiple over time for the company vs macro SaaS median

Bar chart — valuation growth vs ARR growth vs multiple change (decomposition)

Comparison bar — company compression vs 2–3 peer comparables (Vercel, Netlify, Fastly, or sector peers)

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:

One-sentence verdict — e.g., "Multiple compressed 36% but ARR grew 5x, so absolute valuation rose 3.8x."

Primary cause — the #1 factor explaining compression

Narrative premium/discount — AI story, category leadership, or lack thereof

Comparable context — how does this company's compression compare to peers?

Forward implication — what would need to be true for the multiple to expand at next round?

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

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%.

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.