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.

Render the output as an inline visualization (using the Visualizer tool) plus a concise prose explanation, rather than a wall of numbers.

Workflow

1. Gather Data via Web Search

Research these, running independent searches in parallel:

• Each funding round of the target company — round name, date, amount raised, post-money valuation, and lead investor.
• ARR at or near each round date — from press coverage, founder interviews, or investor posts; note when a figure is estimated.
• Growth and retention around each round — ARR growth rate, NRR, churn, notable customers.
• Narrative context — AI positioning and product launches, category leadership, competitive moves.
• Private-market SaaS multiples at each round date — fall back on the dated tables in references/benchmarks.md when search is thin.

2. Build the Data Model

For each funding round, extract or estimate:

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

The three changes multiply rather than add: valuation multiplier = ARR multiplier × multiple multiplier, i.e. (1 + valuation_growth) = (1 + arr_growth) × (1 + multiple_change). They are additive only in log terms, so use log changes wherever the decomposition needs to sum (for example, stacked bars). 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. references/benchmarks.md has dated private-market median multiples by period, public-software drawdowns, and known round-pair comparables for context.

Macro / Rate Environment

• Was the earlier round priced during the 2020–2021 ZIRP bubble? (typically a ~2–5x artificial premium)
• Was the later round priced during the 2022–2023 rate hikes? (removes the bubble premium)
• Was the later round priced during or just after a sector-wide public-software selloff, such as the April 2026 meltdown? Private marks typically lag public ones by 1–2 quarters.
• How does each round's multiple compare with the private-market median for its date?

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
• In a macro-driven selloff an AI premium may be necessary but not sufficient — the April 2026 drawdowns in references/benchmarks.md show strong AI names falling with the sector.

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