Top Tech Adopters with Credit Strength

Top Tech Adopters with Credit Strength

Find the SaaS-forward firms that have budget and solvency to convert.

Find the SaaS-forward firms that have budget and solvency to convert.

Why this matters

Why this matters

Technographic datasets identify SaaS adopters, but ignore whether these firms can sustain spend. Financial datasets show solvency, but not technology readiness.

This segment unites both, and adds a conversion wallet estimation per account: turnover × sector ratio × digital signals × solvency filter. Each figure is transparent, benchmarked against peers, and explained with plain-language comments.

How Starzdata solves this

How Starzdata solves this

  • Detect SaaS, AI, and cloud tools on company sites.

  • Overlay solvency and turnover with sector IT spend ratios.

  • Calibrate spend potential with adoption multipliers and solvency filters.

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

AI reasoning

User Input

Curated APIs

Web intelligence

What you get:

What you get:

  • Turnover, solvency score, and credit percentile per account

  • Detected SaaS, AI, and cloud stack

  • Estimated targetable spend and realistic conversion wallet size

  • Confidence scores for every calculation and benchmark comparisons by sector/country

  • Plain-language comments to explain spend potential and adoption readiness

Sample data for this segment

#company namecountry isowebsite domainturnover latestsolvency scorecredit strength percentilesaas stack detectedsaas stack breadth scoresaas stack depth scoreai tools detectedcloud provider detectedtargetable spend estspend ratio appliedtargetable spend commentconversion potential estconversion commentestimation confidencesector targetable spend avgcustom taxonomy tagsoverall comment
1FinServe Nordic ABSEfinservenordic.se3200000008588["Salesforce","HubSpot","Workday"]7872["TensorFlow"]AWS960000033% IT ratio × €320M turnover; adjusted +20% for Sa...720000075% of targetable spend realistic given solvency 8...92%7200000["Financial Services","FinTech"]High solvency, strong SaaS breadth, wallet size €9...
2AgriChain France SAFRagrichain.fr950000007874["SAP","ZohoCRM"]6558[]Azure190000022% IT ratio × €95M turnover; SaaS breadth moderate...114000060% realistic given solvency 78 and SaaS breadth 6...85%1500000["AgriTech","Supply Chain"]Moderate solvency, wallet €1.9M with ~€1.1M realis...
3MedLife Diagnostics GmbHDEmedlife-diagnostics.de1450000008281["Salesforce","Slack","Tableau"]8070["IBM Watson"]AWS580000044% IT ratio × €145M turnover; adjusted +15% for Sa...464000080% realistic given solvency 82 and SaaS depth 70.90%4500000["MedTech","Diagnostics"]High solvency and diverse SaaS stack; ~€4.6M reali...
4ChemPro Polska Sp. z o.o.PLchempro.pl1200000007469["Microsoft Dynamics","HubSpot"]6055[]Azure240000022% IT ratio × €120M turnover; no AI adoption detec...96000040% realistic given solvency 74 and SaaS breadth 6...80%2200000["Chemicals","Industrial"]Solid but riskier account; realistic wallet €0.96M...
5BuildSmart Italia SRLITbuildsmart.it870000008077["Oracle NetSuite","Marketo"]6863["H2O.ai"]GCP260000033% IT ratio × €87M turnover; AI adoption detected.182000070% realistic given solvency 80 and SaaS adoption ...87%2000000["Construction","GreenTech"]Financially solid, AI-ready stack; €2.6M potential...
Showing 1 to 5 of 5 entries • Click row for details

Each row represents a company account enriched with technology adoption and financial strength signals.

  • Inputs: company name, website domain, and headquarters country (ISO code).

  • Enriched fields: most recent turnover, solvency score, credit strength percentile vs sector peers, detected SaaS stack (breadth & depth), AI tools, cloud provider.

  • Spend estimates: targetable spend (based on IT spend ratios), conversion potential after solvency/adoption filters, and sector benchmarks.

  • Comments & confidence: plain-language explanations of estimates and an overall account comment, with a confidence score for each calculation.

... or explore the segment structure:
... or explore the segment structure:
... or explore the segment structure:
Your questions on this segment, answered

Which industries or regions are best covered?

Which industries or regions are best covered?

Isn’t this just another descriptive dataset?

Isn’t this just another descriptive dataset?

How often are solvency and stack signals refreshed?

How often are solvency and stack signals refreshed?

How do I actually use these wallet estimates in GTM?

How do I actually use these wallet estimates in GTM?

How is this different from a simple technographic or financial dataset?

How is this different from a simple technographic or financial dataset?

How do I know the spend and wallet estimates are trustworthy?

How do I know the spend and wallet estimates are trustworthy?