Parse emails, phone numbers, or URLs embedded in free-text CSV cells
Extract email addresses, phone numbers, or URLs embedded in free-text CSV cells into separate columns.
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Why Onfyt: Extracts all matches per cell (not just first), outputting a comma-separated list when multiple emails or phones appear in one cell.
Interactive Sample Data
| id | notes |
|---|---|
| 1 | Contact Grace at grace@corp.com or call +254712345678 |
| 2 | See https://example.com for details. Email: peter@corp.com |
How this tool works
Free-text notes fields in CRMs often contain structured data embedded in prose: 'Contact Grace at grace@corp.com or call +254 712 345 678'. Extracting these values into proper columns makes the data queryable and usable for automated outreach.
DataFix applies regex patterns for email addresses (RFC 5322-compliant), phone numbers (international E.164-like patterns), and URLs (http/https) to extract the first match (or all matches) from the selected column into a new separate column.
Scanning unstructured text columns with regex pattern extractors isolates embedded email addresses, phone numbers, and web links into clean, dedicated fields.
Automated entity extraction parses messy text columns using hardened regex patterns for international phone numbers, RFC-compliant email addresses, and secure web URLs, creating dedicated columns for lead enrichment. Extracted contact details are cleanly isolated into standardized output columns, facilitating immediate lead enrichment, deduplication, and CRM list updates. Extracted contact elements populate dedicated schema fields, ready for direct ingestion into outreach automation platforms and customer relationship databases.
Parsing unstructured text fields with regex extractors isolates contact identifiers into dedicated attributes. Clean extracted contacts facilitate streamlined lead enrichment and CRM hygiene. Duplicate extraction deduplication prevents recording the same email or phone number multiple times when multiple instances appear in a single cell. Multiple matches per cell are parsed into separate comma-delimited entries or individual columns automatically.
Things that usually go wrong
- Multiple matches per cell (e.g. two email addresses in one notes field) are joined by a pipe (|) by default.
- Phone numbers in different formats (international vs local) may not all match the same pattern.
Edge cases we tested
🧪 Cell with two email addresses
Both extracted as comma-separated list in the email column.
🧪 URL with query string containing @ symbol
URL pattern matched first; @ in query string not misidentified as email.
Frequently Asked Questions
What patterns are recognised for phone numbers?
+1 (212) 555-0100, 0722112233, 254722112233, and similar E.164/local formats.
Can I extract all three types at once?
Yes — enable all three and three separate extraction columns are added.
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