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, cat