Peer benchmarking packs

Peer benchmarking packs

Packs uniting financials, ROCE, and headcount productivity for strategic clarity.

Packs uniting financials, ROCE, and headcount productivity for strategic clarity.

Why this matters

Why this matters

Peer benchmarking is no longer just about EBIT and margins. Investors and consultants need to see how headcount, sales productivity, and capital efficiency shape performance.

Yet most databases ignore workforce structure, and consultants spend weeks reconstructing it. With only company names and countries, we resolve registry IDs and websites, then deliver EBIT, ROCE decomposition, solvency, headcount growth, sales productivity, and foresight signals — normalized, quartiled, and commented in 72h.

How Starzdata solves this

How Starzdata solves this

  • Matches your peers from a client list or sector taxonomy.

  • Standardizes financials and computes ROCE, margins, and capital employed.

  • Harmonizes headcount and function mix under a unified taxonomy.

  • Combines financials, workforce, and strategic signals into one view.

  • Delivers quartiles, trends, and an explainable comment on performance drivers.

  • Provides results in hours, not weeks.

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:

A ready-to-use peer pack where each row is one company, aligned on the same fiscal year. You see financial drivers (EBIT, ROCE, capital employed), people data (headcount, function mix, productivity per sales FTE), and web signals (innovation, expansion, M&A mentions). Each row ends with a plain-language comment linking the ratios back to ROCE drivers. Confidence scores are shown field by field.

  • Headcount-powered peer packs with ROCE decomposition.

  • Workforce productivity: revenue per employee, turnover per sales FTE.

  • Peer medians & quartiles with explainable comments.

  • Exports ready for Sheets/CSV/API and BI.

Sample data for this segment

#company name input(input)country input(input)year referenceyear reference confidencecompany id externalcompany id external confidencecompany websitecompany website confidencerevenuerevenue confidenceebitebit confidenceebit marginebit margin confidencerevenue cagr 3yrevenue cagr 3y confidencecapital employedcapital employed confidenceasset turnoverasset turnover confidenceroceroce confidencesolvency scoresolvency score confidenceliquidity ratioliquidity ratio confidenceheadcountheadcount confidenceheadcount cagr 3yheadcount cagr 3y confidencerevenue per employeerevenue per employee confidencefunction mix engineering pctfunction mix engineering pct confidencefunction mix sales pctfunction mix sales pct confidenceturnover per sales fteturnover per sales fte confidenceinnovation signal scoreinnovation signal score confidenceexpansion signal countexpansion signal count confidencemna mentions 12mmna mentions 12m confidencepeer median ebit marginpeer median ebit margin confidencepeer quartile rankpeer quartile rank confidencecomment
1AlphaTech LtdDE2023100%CO_31045100%www.alphatech.de95%125000000100%18000000100%14.4100%7.2100%150000000100%0.83100%12100%82100%1.35100%52090%5.690%24038590%32.585%18.785%66800085%6880%380%280%11.5100%Q1100%Top‑quartile margin and solid turnover (0.83x) dri...
2BioHealth GmbHDE2023100%CO_31052100%www.biohealth.de94%98000000100%9500000100%9.7100%4.1100%130000000100%0.75100%7.3100%76100%1.2100%43090%390%22790790%28.185%17.485%56300085%5580%180%80%11.5100%Q3100%Below‑median margin and modest turnover (0.75x) li...
3MediCore SAFR2023100%CO_31059100%www.medicore.fr95%152000000100%12000000100%7.9100%2.8100%175000000100%0.87100%6.9100%64100%1.05100%69090%1.290%22029090%24.985%15.285%57800085%4780%80%180%11.5100%Q4100%Low margin drags ROCE (6.9%) despite decent turnov...
4NeoSurg PLCUK2023100%CO_31064100%www.neosurg.co.uk95%87000000100%14000000100%16.1100%9.4100%88000000100%0.99100%15.9100%89100%1.62100%41090%6.890%21219590%35.485%20.185%43500085%7980%480%280%11.5100%Q1100%High margin and near‑1.0x turnover produce strong ...
5VitalScan OyFI2023100%CO_31072100%www.vitalscan.fi94%64000000100%7200000100%11.3100%5100%70000000100%0.91100%10.3100%71100%1.28100%30090%4.490%21333390%3185%16.585%38800085%6280%180%180%11.5100%Q2100%Mid‑pack margin and solid turnover yield ROCE 10.3...
Showing 1 to 5 of 5 entries • Click row for details

Each row represents one peer company for the chosen fiscal year. Inputs are the company name and country. The outputs combine financials (revenue, EBIT, margins, capital employed, ROCE), people metrics (headcount, function mix, revenue per employee, turnover per sales FTE), and web signals (innovation score, expansion mentions, M&A). Peer medians and quartiles are calculated for comparability. Each line ends with a short, plain-language comment explaining whether performance is margin-driven or asset-driven. Every field includes a confidence score based on source reliability and freshness.

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

How can the packs be integrated into workflows (ERP, CRM, BI, consulting models)?

How can the packs be integrated into workflows (ERP, CRM, BI, consulting models)?

How does Starzdata compare to traditional databases like Orbis, Capital IQ, or credit bureaus?

How does Starzdata compare to traditional databases like Orbis, Capital IQ, or credit bureaus?

How are strategic signals (innovation, expansion, M&A) identified and used?

How are strategic signals (innovation, expansion, M&A) identified and used?

What makes your ROCE analysis more insightful than a standard margin/asset review?

What makes your ROCE analysis more insightful than a standard margin/asset review?

How do you calculate and validate productivity metrics like revenue per employee or turnover per sales FTE?

How do you calculate and validate productivity metrics like revenue per employee or turnover per sales FTE?

How reliable are the workforce and function mix data?

How reliable are the workforce and function mix data?

How do you ensure financials are comparable across countries and accounting standards?

How do you ensure financials are comparable across countries and accounting standards?

How do you define and select peer groups?

How do you define and select peer groups?