Signaux de Résilience SaaS

Identifiez les SaaS capables de croître, convertir et tenir dans la durée — alignés à votre taxonomie, benchmarkés globalement.

Voyez qui peut scaler

Pourquoi c’est important

Les benchmarks SaaS comme ceux de Bessemer ou d’OpenView publient des indices utiles, mais ils imposent des codes NAICS obsolètes ou de larges catégories “software”, ce qui rend les comparaisons entre pairs peu pertinentes.

Notre dataset va plus loin : chaque ligne combine une entreprise × un pays × un niveau d’abonnement, avec les données de tarification, essais, friction d’onboarding, ARPU, abonnés, résilience financière et croissance des effectifs. Nous relions les mécaniques d’abonnement à la croissance RH et à la solidité financière, puis nous rattachons le tout à votre taxonomie, rendant le benchmark immédiatement exploitable.

Cloud & SaaS

Conseil

Equipes Stratégie

Equipes Produit

Equipes Marketing

Equipes RevOps

Activation Go-to-Market

Dimensionnement de marché

Comment Starzdata répond à ce besoin

Nous collectons les prix SaaS, les offres d’essai et la friction d’onboarding à travers plusieurs zones géographiques. Nous relions les données d’adoption aux dynamiques RH (effectifs, recrutements seniors) et aux indicateurs financiers (solvabilité, cash runway, ratio d’endettement). Nous appliquons votre taxonomie à grande échelle pour livrer des benchmarks alignés sur votre stratégie.

Ce segment est activé grâce à un mélange de sources fiables et de vos propres données.

AI reasoning

APIs qualifiées

Open Datasets

Web intelligence

Ce que vous obtenez :

Cet extrait illustre la structure de chaque ligne : entreprise × pays × niveau d’abonnement. On y retrouve la tarification par palier, la stratégie d’essai, les scores de friction d’onboarding avec commentaires explicatifs, ainsi que les estimations d’ARPU et d’abonnés, signalées comme rapportées, inférées ou benchmarkées.

Le dataset inclut aussi des indicateurs de résilience financière (risque de solvabilité, ratio d’endettement, cash runway) ainsi que des signaux RH (croissance des effectifs, recrutements seniors). Des indicateurs de croissance (ARR growth %, proxy NRR) sont intégrés pour les cas d’usage investisseurs. Chaque ligne est scorée en confiance et rattachée à votre taxonomie, ce qui rend le benchmark directement exploitable.

Exemple de données (fictives) pour ce 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

Un pack premium d’intelligence SaaS qui permet :

Un benchmark entre pairs basé sur votre taxonomie (verticale, modèle, segment client).
Des prix et mécaniques d’essai par niveau, avec des scores de friction et des explications.
ARPU, abonnés et revenus, signalés comme rapportés, inférés ou benchmarkés.
Indicateurs de résilience financière : solvabilité, cash runway, ratio d’endettement, score global.
Signaux RH : effectifs, croissance, recrutements seniors.

Livré sous forme de tableaux structurés, prêts pour Excel/PowerBI.

Toutes les variables sont scorées en confiance et accompagnées de courts commentaires explicatifs sur la tarification et la friction.

Voyez qui peut scaler

Explore the segment structure 🔎

Les réponses à vos questions sur ce segment

Quelle est la transparence des scores de confiance et des commentaires explicatifs ?

Que signifient les labels “reported / inferred / benchmarked” dans les données ?

Comment les consultants et investisseurs utilisent-ils ce dataset dans leurs projets ?

Comment fonctionne concrètement l’alignement sur la taxonomie du client ?

En quoi ce benchmark est-il différent de ceux publiés par Bessemer ou OpenView ?

Les réponses à vos questions sur ce segment

Quelle est la transparence des scores de confiance et des commentaires explicatifs ?

Chaque métrique est accompagnée d’un score de confiance et d’un commentaire explicatif court. Cela permet de comprendre pourquoi un ARPU, un score de friction ou un indicateur de résilience prend une certaine valeur, et d’utiliser le dataset en toute confiance.

Que signifient les labels “reported / inferred / benchmarked” dans les données ?

Chaque valeur est marquée comme rapportée (issue de données publiques), inférée (calculée à partir de signaux) ou benchmarkée (estimation basée sur comparables). Cette transparence permet de savoir immédiatement comment une métrique a été obtenue.

Comment les consultants et investisseurs utilisent-ils ce dataset dans leurs projets ?

Les consultants s’en servent pour bâtir rapidement des benchmarks personnalisés pour leurs clients. Les investisseurs l’utilisent pour évaluer la scalabilité et la résilience d’un acteur SaaS, en analysant les mécaniques d’adoption, la croissance ARR et les indicateurs de solidité financière.

Comment fonctionne concrètement l’alignement sur la taxonomie du client ?

Vous fournissez vos propres catégories (verticale, modèle, segment client) et chaque ligne du dataset est rattachée à cette taxonomie. Cela permet de comparer vos performances à des pairs réellement comparables, plutôt qu’à des regroupements génériques de type “software”.

En quoi ce benchmark est-il différent de ceux publiés par Bessemer ou OpenView ?

Les indices publiés par Bessemer ou OpenView donnent une vision utile du marché, mais ils restent globaux et statiques. Starzdata fournit un dataset granulaire, actualisé en 72h, couvrant la tarification par palier, l’ARPU, la résilience financière et les signaux RH, avec une segmentation alignée sur votre propre taxonomie.

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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; null if not estimable", "Source_System": "Financial Intelligence", "Data_Type": "INTEGER", "Sample_Value": "14" }, { "Variable": "employee_count", "Description": "Current headcount (FTEs)", "Business_Rules": "INTEGER >= 0", "Source_System": "People Intelligence", "Data_Type": "INTEGER", "Sample_Value": "1200" }, { "Variable": "headcount_growth_percent", "Description": "Headcount growth YoY (%)", "Business_Rules": "DECIMAL, signed", "Source_System": "People Intelligence", "Data_Type": "DECIMAL", "Sample_Value": "22.3" }, { "Variable": "senior_hires_last12m", "Description": "Count of senior hires in last 12 months", "Business_Rules": "INTEGER >= 0", "Source_System": "People Intelligence", "Data_Type": "INTEGER", "Sample_Value": "7" }, { "Variable": "custom_taxonomy_label", "Description": "Client-provided taxonomy tag for this company/tier", "Business_Rules": "Free text or ENUM per client taxonomy", "Source_System": "Client Taxonomy", "Data_Type": "VARCHAR", "Sample_Value": "Customer Support SaaS" } ] }