/saas-valuation-compression

name: saas-valuation-compression
description: >
  Analyze SaaS company valuation compression between funding rounds. Use this skill
  whenever the user asks about: how much a SaaS company's valuation multiple changed
  between rounds, why the ARR multiple compressed or expanded, comparing a company's
  compression to macro benchmarks, or explaining what drove valuation changes for
  any VC-backed software company. Trigger on phrases like "valuation compression",
  "ARR multiple", "round-to-round valuation", "multiple change", or when
  the user asks to compare a company's funding rounds. Always use this skill for
  any multi-round SaaS valuation analysis — do not try to answer from memory alone.

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** — v