Custom Company Counts

Custom Company Counts

Structured market sizing aligned with your taxonomy — transparent, explainable, and activation-ready.

Structured market sizing aligned with your taxonomy — transparent, explainable, and activation-ready.

Why this matters

Why this matters

Strategy teams and consultants need to know how many organizations exist in a given market, broken down by size and sector. But existing sources fail them:

  • Generic reports (Eurostat, Statista, Gartner) provide static, one-size-fits-all numbers that don’t match the client’s segmentation.

  • Internal estimates are quick but inconsistent, hard to replicate, and rarely aligned across markets.

This leaves consultants wasting time reconciling numbers, RevOps making prioritization decisions on shaky assumptions, and strategy teams unable to link results back to their taxonomy.

Starzdata solves this by delivering a top-down company count: every row is Country × Sector × Sizeband, aligned with the client’s taxonomy. Each number is confidence-scored based on source availability, freshness, and estimation method. The result: transparent, traceable, and ready to plug into GTM or foresight dashboards.

How Starzdata solves this

How Starzdata solves this

  • Aligns inputs with client taxonomy for sectors and sizebands (headcount or revenue).

  • Aggregates available registries, datasets, and web intelligence signals at country level.

  • Estimates 2024 company counts and 2024→2027 CAGR with transparent confidence scoring.

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

User Input

AI reasoning

Web intelligence

What you get:

What you get:

A structured dataset providing company counts by market segment, aligned with your taxonomy.

Each record includes:

  • Country (human-readable + ISO code).

  • Sector and sizeband as defined by your taxonomy.

  • Company count for 2024, with confidence score.

  • CAGR 2024→2027, with confidence score and methodology traceability.

This dataset gives you a market opportunity matrix you can trust for market sizing, benchmarking, and GTM prioritization.

Sample data for this segment

#country(input)country iso codesizeband(input)sector(input)org count 2024org count 2024 confidenceorg count cagr 2024 2027org count cagr 2024 2027 confidence
1FranceFRMedium 250–999 employeesSoftware & IT Services138484%2.878%
2GermanyDESmall 50–249 employeesFood Manufacturing254083%1.676%
3SpainESMicro 1–49 employeesLand Transport1123080%0.973%
Showing 1 to 3 of 3 entries • Click row for details

Each row represents an aggregate of Country × Sizeband × Sector. For each combination, the dataset provides:

  • The country name and ISO code.

  • The sector and sizeband labels from your taxonomy.

  • The number of organizations in 2024.

  • The projected CAGR for 2024–2027.

  • A confidence score for both indicators, based on source availability, freshness, and estimation methods.

This structure makes the dataset easy to interpret and compare across markets: you immediately see how many companies exist in a given segment, its projected growth, and the reliability of the estimate.

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

How do strategy teams and consultants integrate this into their workflows?

How do strategy teams and consultants integrate this into their workflows?

How often can these counts be refreshed, and what does that mean for foresight?

How often can these counts be refreshed, and what does that mean for foresight?

How does this segment help prioritize GTM resource allocation?

How does this segment help prioritize GTM resource allocation?

How is this different from generic reports or consultant guesstimates?

How is this different from generic reports or consultant guesstimates?

Why does flexibility in sizebands (headcount or revenue) matter?

Why does flexibility in sizebands (headcount or revenue) matter?

What do confidence scores mean, and how do they guide decisions?

What do confidence scores mean, and how do they guide decisions?

How is my taxonomy applied, and why is that better than generic reports?

How is my taxonomy applied, and why is that better than generic reports?

What does each row represent, and how do I read it?

What does each row represent, and how do I read it?