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.