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.
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
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.
Your questions on this segment, answered
Your questions on this segment, answered
How do strategy teams and consultants integrate this into their workflows?
The dataset is delivered as structured tables ready for Excel, PowerBI, or direct integration into consulting deliverables. This makes it plug-and-play for market sizing, benchmarks, or board-ready business cases.
How often can these counts be refreshed, and what does that mean for foresight?
Updates can be scheduled quarterly or annually, depending on needs and source freshness. This ensures foresight dashboards and strategic benchmarks always reflect the latest dynamics.
How does this segment help prioritize GTM resource allocation?
By combining company counts with growth rates and confidence scores, the segment highlights where the market is largest and growing fastest. RevOps and GTM teams use it to decide where to focus sales, marketing, or partnership resources.
How is this different from generic reports or consultant guesstimates?
Generic reports are static and broad. Internal estimates are quick but inconsistent. Starzdata delivers aligned counts, traceable confidence scores, and refreshable outputs — explainable, not one-size-fits-all.
Why does flexibility in sizebands (headcount or revenue) matter?
Public sector entities are best segmented by headcount, while private firms often align better with revenue. Flexibility means the dataset works equally well for government foresight, consulting projects, or commercial GTM planning.
What do confidence scores mean, and how do they guide decisions?
Confidence reflects the availability and freshness of sources and the robustness of estimation methods. High scores mean recent, directly observed data; lower scores signal more model-driven estimates. This lets you weigh decisions by reliability, not just numbers.
How is my taxonomy applied, and why is that better than generic reports?
The client defines the sectors and sizebands. Starzdata aligns its counts to your taxonomy, ensuring numbers match the segmentation that matters for your strategy. Unlike generic reports with fixed categories, this dataset adapts to your lens.
What does each row represent, and how do I read it?
Each row is an aggregate of Country × Sector × Sizeband. It shows the number of organizations in 2024, the projected CAGR through 2027, and confidence scores for both indicators. It’s a top-down market view — aligned to your taxonomy, not a list of companies.
{ "_meta": { "dictionaryColumns": ["Variable", "Data_Type", "Sample_Value", "Description"] }, "data": [ { "Variable": "country", "Description": "Human-readable country name", "Business_Rules": "Must be in client input list.", "Source_System": "Client Data", "Data_Type": "VARCHAR", "Sample_Value": "France" }, { "Variable": "country_iso_code", "Description": "ISO 3166-1 alpha-2 code for country", "Business_Rules": "Derived automatically from country input.", "Source_System": "Web+AI Reasoning", "Data_Type": "CHAR", "Sample_Value": "FR" }, { "Variable": "sizeband", "Description": "Human-readable sizeband label (by headcount or revenue)", "Business_Rules": "Defined by client taxonomy (e.g., 'Small 50–249 employees').", "Source_System": "Client Data", "Data_Type": "VARCHAR", "Sample_Value": "Medium 250–999 employees" }, { "Variable": "sector", "Description": "Human-readable sector label", "Business_Rules": "Defined by client taxonomy (e.g., 'Software & IT Services').", "Source_System": "Client Data", "Data_Type": "VARCHAR", "Sample_Value": "Software & IT Services" }, { "Variable": "org_count_2024", "Description": "Number of organizations in 2024", "Business_Rules": "INTEGER ≥ 0; deduped at decision level.", "Source_System": "Web+AI Reasoning", "Data_Type": "INTEGER", "Sample_Value": "1384" }, { "Variable": "org_count_cagr_2024_2027", "Description": "3-year CAGR for counts (2024→2027, %)", "Business_Rules": "DECIMAL between -50 and 50.", "Source_System": "Web+AI Reasoning", "Data_Type": "DECIMAL", "Sample_Value": "2.8" } ] }
