Lead Extractor — Extract Names, Emails, Phones, Companies
Extract name + email + phone + company combinations from text or pasted lists. Cleaned, ready for CRM import. Free.
About Lead Extractor
A lead extractor combines several extraction patterns into one workflow — pulling names, email addresses, phone numbers, company affiliations, and (where present) job titles from messy text and outputting a structured CSV ready for CRM import. The ZTools Lead Extractor runs entirely in the browser, applies email + phone + name heuristics together, attempts to associate each contact item with the same person via proximity rules, and exports clean rows — saving hours of manual cleanup on conference attendee lists, email-thread participants, or contact-form scrapes.
Use cases
- Conference attendee list cleanup. Public attendee directory pasted as messy text. Extract name + email + company per attendee into rows for CRM. Hours of manual data entry collapsed to seconds.
- Email thread participant capture. Long email reply chain. Extractor pulls each unique participant's name + email + signature info into one row per person.
- LinkedIn / Twitter export normalisation. Exported contact list with inconsistent formatting. Extractor standardises names + handles + company affiliations.
- Sales prospect-list cleanup. Bulk lead list from various sources. Extractor reformats into a single canonical structure for sales-tool import.
How it works
- Paste source text. Conference list, email thread, contact form dump, exported directory. Mixed formatting OK.
- Run individual extractors. Email, phone, hashtag, name, and company patterns identified separately.
- Associate by proximity. Tokens within N characters / lines of each other associate as one lead. Configurable proximity window.
- Validate + dedupe. Drop incomplete rows (no email AND no phone), dedupe by email (lowercased).
- Export CSV. Columns: name, email, phone, company, title, source-context. Ready for CRM mapping.
Examples
Input: "John Smith — VP Sales — Acme Corp — john@acme.com — (555) 123-4567"
Output: Single lead with all 5 fields populated.
Input: Email signatures from a thread
Output: One row per unique signer with as much data as the signature provided.
Input: Conference list with mixed formatting
Output: Each entry parsed independently; rows for each.
Frequently asked questions
How accurate is the association?
Depends heavily on input format. Tabular / consistent formats: ~95%. Free-form text: ~70-85%. Always review the CSV before import; common errors are mismatched name/email pairs in dense text.
How are names recognised?
Heuristics: capitalisation pattern (Title Case), proximity to other lead fields, common first/last name lists. Foreign names are harder; review necessary.
Can I extract roles / titles?
Yes — recognised patterns ("CEO", "VP", "Director", "Manager", "Engineer"). Optional column.
Is the input uploaded?
No — entirely client-side. Important when handling personal data.
Does this comply with GDPR / CCPA?
Extraction itself is data processing. Storing or using extracted PII for marketing requires lawful basis (consent, legitimate interest, etc.). Tool output ≠ permission to email/call.
How do I improve accuracy?
Pre-clean the source — consistent line breaks, one entry per row. Tabular sources extract more cleanly than narrative paragraphs.
Pro tips
- Always review the CSV before CRM import — automated extraction misses edge cases.
- Set proximity window based on input format. Tight (50 char) for tabular; wide (500 char) for free text.
- Honour data-protection regulations. Extract only with lawful basis; store securely; delete on request.
- For high-volume work, do extraction + manual spot-check rather than blind import.
- Combine with email + phone validators before outreach — extracted data may be stale or mistyped.
Reviewed by Ahsan Mahmood · Last updated 2026-05-05 · Part of ZTools.
For the full,
formatted version of this page, please enable JavaScript and reload
https://ztools.zaions.com/lead-extractor.