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.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: himself65
• Source: himself65/finance-skills
• License: MIT
• Homepage: https://skills.himself65.com

Install and usage instructions live in the source repository linked above.