saas-valuation-compression

Name: saas-valuation-compression
Author: himself65

1.2kinstalls351forks

How to install saas-valuation-compression?

himself65/finance-skills/saas-valuation-compression

$npx -y skills add himself65/finance-skills --skill saas-valuation-compression

Installs into the current project.

Summary

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,

Files · 1

View on GitHub

SKILL.md

1	# SaaS Valuation Compression Analyzer
2
3	## What This Skill Does
4
5	For a given SaaS company, research its funding history and compute ARR-based valuation
6	multiples at each round. Then explain the compression (or expansion) using a structured
7	framework that covers macro rates, growth trajectory, narrative shifts, and comparables.
8
9	Always render the output as an inline visualization (using the Visualizer tool) plus a
10	concise prose explanation. Do not just return a wall of numbers.
11
12	---
13
14	## Step-by-Step Workflow
15
16	### 1. Gather Data via Web Search
17
18	Search for each of the following. Run searches in parallel where possible.
19
20	For the target company:
21	- `[company] funding rounds valuation ARR revenue`
22	- `[company] Series [X] raised valuation` for each round
23	- `[company] annual recurring revenue ARR [year]` for each round date
24	- `[company] investors lead investor [round]`
25
26	For macro context:
27	- `SaaS ARR valuation multiples [year] private market`
28	- Use the known benchmark table below as fallback if search is thin.
29
30	For narrative context:
31	- `[company] AI customers product announcement [year]` — AI narrative premium?
32	- `[company] growth rate churn NRR [year]` — fundamentals shift?
33
34	### 2. Build the Data Model
35
36	For each funding round, extract or estimate:
37
38	\| Field \| How to get it \|
39	\|---\|---\|
40	\| Round name \| Direct from search \|
41	\| Date \| Direct from search \|
42	\| Amount raised \| Direct from search \|
43	\| Post-money valuation \| Direct or compute from ownership %; if unavailable, note as estimated \|
44	\| ARR at round date \| Search explicitly; if not found, estimate from customer count x ARPC or interpolate \|
45	\| ARR multiple \| `valuation / ARR` \|
46	\| Lead investor \| Direct \|
47
48	ARR estimation heuristics (when not public):
49	- Seed/Series A: ARR often $500K–$3M
50	- Series B: typically $5M–$20M
51	- Series C: typically $20M–$60M
52	- Cross-check against customer count x average deal size if available
53
54	### 3. Compute Compression Metrics
55
56	For each consecutive round pair (e.g., B → C):
57
58	```
59	multiple_compression_pct = (later_multiple - earlier_multiple) / earlier_multiple × 100
60	valuation_growth_pct = (later_val - earlier_val) / earlier_val × 100
61	arr_growth_pct = (later_arr - earlier_arr) / earlier_arr × 100
62	```
63
64	Key insight: `valuation_growth = arr_growth + multiple_change`
65	If ARR grows faster than the multiple compresses, absolute valuation still rises.
66
67	### 4. Attribute Compression to Causes
68
69	Use this checklist. For each cause, rate it: Primary / Contributing / Not applicable.
70
71	Macro / Rate Environment
72	- Was the earlier round during 2020–2021 ZIRP bubble? (adds ~2–5x artificial premium)
73	- Was the later round during 2022–2023 rate hikes? (removes bubble premium)
74	- 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%)
75	- Reference: SaaS private market median multiples by period:
76
77	\| Period \| Approx Median ARR Multiple (private) \| Context \|
78	\|---\|---\|---\|
79	\| 2019 \| ~8–12x \| Pre-pandemic baseline \|
80	\| 2020 \| ~12–18x \| ZIRP begins, multiple expansion \|
81	\| 2021 Q1–Q3 peak \| ~35–45x \| Peak bubble \|
82	\| 2022 H2 \| ~15–20x \| Rate hikes begin, first compression wave \|
83	\| 2023 trough \| ~8–12x \| Rate plateau, valuation reset \|
84	\| 2024 \| ~12–18x \| AI narrative recovery, selective re-rating \|
85	\| 2025 H1 \| ~16–22x \| Continued AI-driven recovery \|
86	\| 2025 H2–2026 Q1 \| ~10–16x \| Tariff shock / trade-war selloff begins \|
87	\| 2026 Q2 (Apr meltdown) \| ~6–10x \| Software Meltdown — broad sector crash, public SaaS down 40–86% from 52w highs \|
88
89	(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.)
90
91	Growth Deceleration
92	- Did YoY ARR growth rate slow materially between rounds? (most common cause)
93	- Did NRR/net retention drop?
94
95	Narrative Shift
96	- Did the company lose a major product story (e.g., lost PLG thesis, missed category leadership)?
97	- Did competitors emerge or incumbents catch up?
98
99	AI Premium (positive or negative)
100	- Does the company serve AI-native companies (OpenAI, Anthropic, etc.) as customers? → premium
101	-

People who liked this also viewed

6 picks

Type

saas-valuation-compression

1.2kinstalls351forks

View on GitHub

How to install saas-valuation-compression?

himself65/finance-skills/saas-valuation-compression

$npx -y skills add himself65/finance-skills --skill saas-valuation-compression

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# SaaS Valuation Compression Analyzer
2
3	## What This Skill Does
4
5	For a given SaaS company, research its funding history and compute ARR-based valuation
6	multiples at each round. Then explain the compression (or expansion) using a structured
7	framework that covers macro rates, growth trajectory, narrative shifts, and comparables.
8
9	Always render the output as an inline visualization (using the Visualizer tool) plus a
10	concise prose explanation. Do not just return a wall of numbers.
11
12	---
13
14	## Step-by-Step Workflow
15
16	### 1. Gather Data via Web Search
17
18	Search for each of the following. Run searches in parallel where possible.
19
20	For the target company:
21	- `[company] funding rounds valuation ARR revenue`
22	- `[company] Series [X] raised valuation` for each round
23	- `[company] annual recurring revenue ARR [year]` for each round date
24	- `[company] investors lead investor [round]`
25
26	For macro context:
27	- `SaaS ARR valuation multiples [year] private market`
28	- Use the known benchmark table below as fallback if search is thin.
29
30	For narrative context:
31	- `[company] AI customers product announcement [year]` — AI narrative premium?
32	- `[company] growth rate churn NRR [year]` — fundamentals shift?
33
34	### 2. Build the Data Model
35
36	For each funding round, extract or estimate:
37
38	\| Field \| How to get it \|
39	\|---\|---\|
40	\| Round name \| Direct from search \|
41	\| Date \| Direct from search \|
42	\| Amount raised \| Direct from search \|
43	\| Post-money valuation \| Direct or compute from ownership %; if unavailable, note as estimated \|
44	\| ARR at round date \| Search explicitly; if not found, estimate from customer count x ARPC or interpolate \|
45	\| ARR multiple \| `valuation / ARR` \|
46	\| Lead investor \| Direct \|
47
48	ARR estimation heuristics (when not public):
49	- Seed/Series A: ARR often $500K–$3M
50	- Series B: typically $5M–$20M
51	- Series C: typically $20M–$60M
52	- Cross-check against customer count x average deal size if available
53
54	### 3. Compute Compression Metrics
55
56	For each consecutive round pair (e.g., B → C):
57
58	```
59	multiple_compression_pct = (later_multiple - earlier_multiple) / earlier_multiple × 100
60	valuation_growth_pct = (later_val - earlier_val) / earlier_val × 100
61	arr_growth_pct = (later_arr - earlier_arr) / earlier_arr × 100
62	```
63
64	Key insight: `valuation_growth = arr_growth + multiple_change`
65	If ARR grows faster than the multiple compresses, absolute valuation still rises.
66
67	### 4. Attribute Compression to Causes
68
69	Use this checklist. For each cause, rate it: Primary / Contributing / Not applicable.
70
71	Macro / Rate Environment
72	- Was the earlier round during 2020–2021 ZIRP bubble? (adds ~2–5x artificial premium)
73	- Was the later round during 2022–2023 rate hikes? (removes bubble premium)
74	- 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%)
75	- Reference: SaaS private market median multiples by period:
76
77	\| Period \| Approx Median ARR Multiple (private) \| Context \|
78	\|---\|---\|---\|
79	\| 2019 \| ~8–12x \| Pre-pandemic baseline \|
80	\| 2020 \| ~12–18x \| ZIRP begins, multiple expansion \|
81	\| 2021 Q1–Q3 peak \| ~35–45x \| Peak bubble \|
82	\| 2022 H2 \| ~15–20x \| Rate hikes begin, first compression wave \|
83	\| 2023 trough \| ~8–12x \| Rate plateau, valuation reset \|
84	\| 2024 \| ~12–18x \| AI narrative recovery, selective re-rating \|
85	\| 2025 H1 \| ~16–22x \| Continued AI-driven recovery \|
86	\| 2025 H2–2026 Q1 \| ~10–16x \| Tariff shock / trade-war selloff begins \|
87	\| 2026 Q2 (Apr meltdown) \| ~6–10x \| Software Meltdown — broad sector crash, public SaaS down 40–86% from 52w highs \|
88
89	(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.)
90
91	Growth Deceleration
92	- Did YoY ARR growth rate slow materially between rounds? (most common cause)
93	- Did NRR/net retention drop?
94
95	Narrative Shift
96	- Did the company lose a major product story (e.g., lost PLG thesis, missed category leadership)?
97	- Did competitors emerge or incumbents catch up?
98
99	AI Premium (positive or negative)
100	- Does the company serve AI-native companies (OpenAI, Anthropic, etc.) as customers? → premium
101	-

People who liked this also viewed

6 picks

Type