SaaS Resilience Signals

Detect which SaaS players can scale, convert, and survive — benchmarked globally and aligned to your taxonomy.

See who scales

Why this matters

SaaS benchmarks like Bessemer or OpenView publish useful indices, but they force outdated NAICS codes or broad “software” categories, making peer comparisons irrelevant.

Our segment goes deeper: every row is a company × country × subscription tier, capturing pricing, trials, onboarding friction, ARPU, subscribers, financial resilience, and people growth. We link subscription mechanics with headcount and financial strength, then tag everything with your taxonomy, making the benchmarks directly actionable.

Cloud & SaaS

Advisory Services

Strategy teams

Product teams

Marketing teams

RevOps teams

Go-To-Market Activation

Market Foresight

How Starzdata solves this

We scan SaaS pricing, trials, and onboarding friction across geographies. We link adoption economics with people scaling (headcount, senior hires) and financial resilience (solvency, cash runway, debt ratio). We apply your taxonomy at scale, delivering benchmarks that match your strategy.

This segment is activated with a blend of trusted sources and your own inputs

AI reasoning

Curated APIs

Open Datasets

Web intelligence

What you get:

This sample illustrates how each row combines company × country × subscription tier. You can see tier-level pricing, trial strategy, onboarding friction scores with explanatory comments, as well as ARPU and subscriber estimates flagged as reported, inferred, or benchmarked.

The dataset also includes financial resilience indicators such as solvency risk, debt ratio, and cash runway, together with people signals like headcount growth and senior hires. Growth indicators (ARR growth %, NRR proxy) are included for investor use cases. All rows are confidence-scored and tagged to your custom taxonomy, making the benchmarks directly actionable.

Sample data for this segment

#	company name	company name confidence	primary domain	primary domain confidence	geo country	geo country confidence	subscription currency	subscription currency confidence	tier name	tier name confidence	tier price min local	tier price min local confidence	tier price max local	tier price max local confidence	tier price median usd	tier price median usd confidence	trial strategy	trial strategy confidence	credit card required	credit card required confidence	signup steps count	signup steps count confidence	onboarding call required	onboarding call required confidence	conversion friction score	conversion friction score confidence	conversion friction comment	conversion friction comment confidence	estimated active subscribers	estimated active subscribers confidence	arpu usd	arpu usd confidence	arpu methodology flag	arpu methodology flag confidence	estimated revenues usd	estimated revenues usd confidence	nrr proxy score	nrr proxy score confidence	growth rate arr percent	growth rate arr percent confidence	financial strength score	financial strength score confidence	solvency risk	solvency risk confidence	debt ratio	debt ratio confidence	cash runway months	cash runway months confidence	employee count	employee count confidence	headcount growth percent	headcount growth percent confidence	senior hires last12m	senior hires last12m confidence	custom taxonomy label	custom taxonomy label confidence
1	Zendesk	99%	zendesk.com	99%	US	99%	USD	99%	pro	98%	49	96%	59	96%	55	95%	14days	93%	true	94%	6	90%		92%	35	90%	Card required; multi-step email verification.	86%	450000	82%	52.5	84%	inferred	88%	23625000	82%	72	78%	18.5	76%	76	85%	low	86%	35	80%	14	74%	1200	88%	22.3	80%	7	78%	Customer Support SaaS	100%
2	Freshworks	99%	freshworks.com	99%	IN	99%	INR	99%	starter	97%	799	95%	999	95%	11.5	94%	14days	92%		93%	4	90%		92%	20	88%	Easy signup; no card required.	86%	210000	78%	12	82%	inferred	88%	2520000	78%	68	76%	32.1	74%	70	84%	medium	82%	42	78%	10	72%	800	86%	28.4	78%	5	76%	Customer Support SaaS	100%
3	HubSpot	99%	hubspot.com	99%	US	99%	USD	99%	enterprise	98%	3200	95%	3600	95%	3400	94%	30days	93%	true	94%	8	90%	true	93%	65	88%	High-touch onboarding and required sales call.	86%	90000	78%	320	84%	reported	95%	28800000	80%	78	80%	21.7	78%	84	88%	low	90%	29	80%	20	76%	6000	90%	15.4	80%	18	80%	CRM & Marketing SaaS	100%
4	Atlassian	99%	atlassian.com	99%	AU	99%	AUD	99%	standard	98%	13.5	95%	15.5	95%	10.2	94%	7days	92%		94%	5	90%		92%	18	88%	Self-serve friendly; fast activation.	86%	1200000	82%	11	84%	inferred	88%	13200000	80%	81	80%	24.9	78%	88	90%	low	90%	18	80%	28	78%	9000	90%	12.1	80%	22	80%	DevOps & Collaboration	100%
5	Monday.com	99%	monday.com	99%	IL	99%	USD	99%	standard	98%	8	95%	12	95%	10	94%	14days	92%		94%	4	90%		92%	22	88%	Smooth onboarding; short trial.	86%	350000	78%	15	82%	inferred	88%	5250000	78%	70	76%	29.3	74%	73	86%	medium	82%	41	78%	11	72%	1500	88%	27.5	78%	8	76%	Work Management	100%
6	Salesforce	99%	salesforce.com	99%	US	99%	USD	99%	enterprise	98%	120	95%	300	95%	210	94%	30days	93%	true	94%	10	90%	true	93%	72	88%	Enterprise focus; requires card and sales team.	86%	1200000	82%	225	90%	reported	96%	270000000	84%	85	82%	19	80%	90	92%	low	92%	22	82%	36	80%	73000	92%	8.2	82%	55	82%	Enterprise CRM	100%
7	ServiceNow	99%	servicenow.com	99%	US	99%	USD	99%	enterprise	98%	100	95%	250	95%	175	94%	none	92%	true	93%	9	90%	true	93%	70	88%	Enterprise motion; procurement-heavy onboarding.	86%	650000	80%	240	88%	benchmarked	88%	156000000	84%	87	82%	23.4	80%	92	92%	low	92%	20	82%	34	80%	22000	92%	11.6	82%	40	82%	ITSM	100%
8	Asana	99%	asana.com	99%	US	99%	USD	99%	pro	98%	10.99	95%	13.49	95%	11.99	94%	30days	92%		94%	5	90%		92%	24	88%	PLG flow; generous trial; no card.	86%	420000	78%	13.5	82%	inferred	88%	5670000	78%	69	76%	26.8	74%	71	86%	medium	82%	44	78%	12	72%	1700	88%	19.3	78%	9	76%	Work Management	100%
9	Dropbox	99%	dropbox.com	99%	DE	99%	EUR	99%	standard	98%	9.99	95%	12.99	95%	12.8	94%	14days	92%		94%	4	90%		92%	19	88%	Friction-light PLG; quick activation.	86%	1500000	80%	13	84%	inferred	88%	19500000	82%	67	76%	12.4	74%	78	88%	low	88%	26	80%	22	78%	2800	90%	6.8	78%	11	76%	Cloud Storage	100%
10	Slack	99%	slack.com	99%	UK	99%	GBP	99%	standard	98%	6.25	95%	7.5	95%	8.6	94%	30days	92%		94%	5	90%		92%	21	88%	PLG motion; smooth SSO; generous trial.	86%	2300000	80%	9.2	84%	inferred	88%	21160000	82%	73	78%	17.9	76%	83	90%	low	90%	24	80%	24	78%	3500	90%	9.7	78%	13	76%	Team Collaboration	100%

Showing 1 to 10 of 11 entries • Click row for details

A premium SaaS intelligence pack enabling:

Peer benchmarking by client-defined taxonomy (vertical, model, customer segment).
Tier-level pricing and trial mechanics with explainable friction drivers.
ARPU, subscribers, and revenue estimates flagged as reported/inferred.
Financial resilience (solvency, cash runway) and people signals (growth, hires).
Delivered as structured tables, tailored to Excel/PowerBI workflows.

All variables are confidence-scored and include short explanatory comments on pricing and friction.

See who scales

Explore the segment structure 🔎

Your questions on this segment, answered

How transparent and explainable are the confidence scores?

What does “reported / inferred / benchmarked” mean in the data?

How do consultants and investors use this dataset in real projects?

How does the taxonomy alignment work in practice?

How is this different from public SaaS benchmarks like Bessemer or OpenView?

Your questions on this segment, answered

How transparent and explainable are the confidence scores?

Each metric comes with a confidence score and a short explanatory comment. This makes it clear why an ARPU, friction score, or resilience indicator takes a given value, allowing you to use the dataset with confidence.

What does “reported / inferred / benchmarked” mean in the data?

Each value is flagged as reported (from public disclosures), inferred (calculated from signals), or benchmarked (estimated from peers). This transparency shows exactly how each metric was derived.

How do consultants and investors use this dataset in real projects?

Consultants use it to build custom benchmarks for clients in days instead of weeks. Investors rely on it to assess scalability and resilience of SaaS operators, analyzing adoption mechanics, ARR growth, and financial strength indicators.

How does the taxonomy alignment work in practice?

You provide your own categories (vertical, model, customer segment) and each row of the dataset is mapped to that taxonomy. This enables comparisons against truly relevant peers instead of broad “software” groupings.

How is this different from public SaaS benchmarks like Bessemer or OpenView?

Indices published by Bessemer or OpenView are useful for macro trends, but they remain aggregated and static. Starzdata delivers a granular dataset refreshed in 72h, covering tier-level pricing, ARPU, financial resilience, and people signals, all aligned with your custom taxonomy.

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multi-step email verification." }, { "Variable": "estimated_active_subscribers", "Description": "Estimated active paid users (company & country)", "Business_Rules": "INTEGER >= 0", "Source_System": "AI Reasoning + Web Intelligence", "Data_Type": "INTEGER", "Sample_Value": "450000" }, { "Variable": "arpu_usd", "Description": "Average revenue per user per month (USD)", "Business_Rules": "Seat-weighted if multi-tier", "Source_System": "AI Reasoning", "Data_Type": "DECIMAL", "Sample_Value": "52.5" }, { "Variable": "arpu_methodology_flag", "Description": "How ARPU was derived", "Business_Rules": "ENUM: reported|inferred|benchmarked", "Source_System": "AI Reasoning", "Data_Type": "ENUM", "Sample_Value": "inferred" }, { "Variable": "estimated_revenues_usd", "Description": "Estimated monthly revenues in USD", "Business_Rules": "estimated_active_subscribers × arpu_usd", "Source_System": "AI Reasoning", "Data_Type": "DECIMAL", "Sample_Value": "23625000.0" }, { "Variable": "nrr_proxy_score", "Description": "Net revenue retention proxy (0–100)", "Business_Rules": "Estimated from public signals; not official", "Source_System": "AI Reasoning + Web Intelligence", "Data_Type": "INTEGER", "Sample_Value": "72" }, { "Variable": "growth_rate_arr_percent", "Description": "Annual recurring revenue growth rate (%)", "Business_Rules": "DECIMAL; YoY", "Source_System": "AI Reasoning + Web Intelligence", "Data_Type": "DECIMAL", "Sample_Value": "18.5" }, { "Variable": "financial_strength_score", "Description": "Overall financial resilience score (0–100)", "Business_Rules": "Higher = stronger", "Source_System": "Financial Intelligence", "Data_Type": "INTEGER", "Sample_Value": "76" }, { "Variable": "solvency_risk", "Description": "Qualitative solvency indicator", "Business_Rules": "ENUM: low|medium|high", "Source_System": "Financial Intelligence", "Data_Type": "ENUM", "Sample_Value": "low" }, { "Variable": "debt_ratio", "Description": "Debt-to-equity ratio (%)", "Business_Rules": "DECIMAL 0–500", "Source_System": "Financial Intelligence", "Data_Type": "DECIMAL", "Sample_Value": "35.0" }, { "Variable": "cash_runway_months", "Description": "Estimated months of cash runway", "Business_Rules": "INTEGER; 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