What is actually inside the Austria lead file

The shape of the fileEvery record is a person, built from a LinkedIn profile

This is a People Data Labs–style export. The spine of each row is a name and a LinkedIn URL — those are present on essentially every record. Everything else is enrichment layered on top, and the enrichment thins out fast.

Usability tiers across all 713,839 records

Tiers A and B together — 441,972 records, 61.9% — are the working list.

Industry mapWhere these 713,839 people work

I mapped the 147 raw industry labels in the file onto 14 working sectors. Where the industry field was blank I fell back to the company's own industry, then to keyword inference from the job title, employer name and profile summary — which recovered another 47,003 records. Just under 30% carry no occupational signal at all and cannot be placed.

Seniority, and where the decision-makers sit

157,393 records — 22% of the file — hold an owner, C-level, director or manager title. That is the part of the list worth working. Industrial & Manufacturing alone contributes 25,875 of them, ahead of Technology & Software at 18,069 and Marketing, Media & Creative at 15,766. A further 31.9% of the file has no job title at all, so their seniority is simply unknown rather than junior.

Batch 1The first 100 leads, profiled

Rows 1–100 of the file, run through the same pipeline: German text repaired, sector assigned, seniority and job function derived, completeness scored. This batch runs close to the file average, which suggests the file is not sorted by quality — batch 2 should look much the same.

Decision-makers are highlighted. @ email present · co company size known · sk skills listed. Score is the share of ten core fields populated. Email addresses are held in the local CSV, not shown here.

Sector spread within the batch

Industrial & Manufacturing 13, Public Sector / Education 11, Technology & Software 11, Marketing & Media 7, Professional Services 7, Finance 6, Healthcare 6, Retail 4, Energy 3, Agriculture 2, Construction 2, Logistics 1 — and 27 that could not be placed. The employers named are real Austrian anchors: voestalpine, Infineon, Magna International, Erste Bank, Kapsch, Österreichische Post, SAP, IAEA.

What a complete record actually contains

Five of the 100 scored 100% completeness. This is the ceiling of what the file gives you on one person — 25 populated fields, enough to write a personalised opener without any external lookup.

FindingsThree problems to handle before you use this list

NextWhere I would take this

1. Decide the target sector, then I run that slice rather than more sequential batches.

   Batch 1 was rows 1–100 in file order, which is a fair sample but a poor prospect list. Filtering to, say, decision-makers in Industrial & Manufacturing yields 25,875 records in one pass.

2. Suppress the dead weight first.

   Drop the 171,948 name-only records, the 5,443 retirees and the 2,769 duplicates. That alone takes the file from 713,839 to roughly 533,000 without losing a single workable lead.

3. Re-verify before any outreach.

   Given the 2018–2020 stamps, run the shortlist through a current enrichment or verification service. Verify emails; re-check titles against the LinkedIn URL, which is the one field that ages well.

4. Get the GDPR basis settled before the first send.

   This is EU personal data obtained from a third party, and Austrian outreach rules are stricter than the EU baseline. The personal-versus-corporate email split above is the first thing to look at.

What is on disk now