Email Signature Parser: Extract Contact Details From Email Signatures to Excel and CSV
Parse every email signature into a clean contact list
Your inbox is full of contact details you already have permission to use: the signature block on every reply. MailParse reads the body and HTML of each message, pulls the name, title, company, phone, and email out of the signature, and gives you back a spreadsheet or JSON, one contact per row.
Last updated July 2026
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An email signature parser reads the signature block at the bottom of an email and pulls the contact fields out of it, so a name, job title, company, phone number, email, and website land in their own spreadsheet columns instead of one text blob. MailParse reads the message body and its HTML, so you map each signature value to a named field and export the result as Excel, CSV, or JSON. It records any attached vCard by filename, type, and size; the fields it parses come from the signature text in the body.
- Named fields
- Not one text blob
- Body & HTML
- Reads both signature formats
- No copy-paste
- Skip retyping every card
- 3 formats
- CSV, Excel, JSON output
Almost every business email ends with a signature: a name, a job title, a company, a direct line, an email address, sometimes a website and a LinkedIn URL. It is the cleanest contact record a person will ever hand you, and it arrives in your inbox already. The problem is that it sits at the bottom of a message as free text, so building a contact list out of a few hundred emails usually means opening each one and copying the details across by hand.
An email signature parser removes that step. Instead of reading the email, it looks at the signature block, identifies the fields inside it, and writes each one to its own column. Ten replies become ten rows with a name, title, company, phone, and email in each. The list is ready to import into a CRM, hand to a sales rep, or keep as a spreadsheet.
MailParse does this by reading the message body and its HTML, which is where a signature lives. You tell it which values to capture, such as full name, job title, company, phone, and email, and map them to named fields once. From then on, every message you run through it produces the same clean columns. When a signature includes an attached vCard (.vcf) file, MailParse records that attachment by filename, type, and size; the contact fields it extracts come from the signature text in the message itself. To pull data out of the contents of an attached file, run it through a document data extraction tool separately.
What the converter does
Everything you need to turn a pile of email files into a spreadsheet your team can actually use.
Pulls the standard signature fields
Name, job title, company, phone, email, and website each become their own column, not a single cell of run-together text.
Reads HTML and plain-text signatures
Whether the signature is styled HTML with a logo or a few plain lines, MailParse reads the underlying text and maps the values you name.
One contact per row
Every message becomes a row, so a folder of replies turns straight into a contact list you can sort, dedupe, and import.
Map once, reuse everywhere
Save the field mapping and apply it to any inbox or folder, so a recurring contact-capture job never has to be rebuilt.
Export ready for your CRM
Download CSV, Excel, or JSON in the column shape your CRM import expects, or send the fields straight through the API.
Keeps the source email fields too
Sender address, subject, and received date come along with the parsed signature, so each contact is tied to the message it came from.
How to convert EML and MSG files to a spreadsheet
Four steps from raw email files to a clean CSV, Excel, or JSON file.
Connect the mailbox or upload emails
Point MailParse at a Gmail, Outlook, or IMAP folder, or upload .eml and .msg files that contain the signatures you want.
Name your contact fields
Add fields such as full_name, job_title, company, phone, email, and website, matching the details your signatures contain.
Let it read the signatures
MailParse reads each message body and HTML, pulls the mapped values from the signature block, and writes one row per email.
Export or push to your CRM
Download Excel, CSV, or JSON, or send the parsed contacts to your CRM through the API. Re-run the same mapping whenever new mail arrives.
Who converts EML and MSG files to CSV
Teams that deal with email in volume and need it as structured data.
Sales and business development
Turn a folder of inbound replies and introductions into a contact list with names, titles, companies, and direct numbers, ready for the CRM.
Recruiting and talent teams
Capture candidate and referral contact details from the signatures on application and networking emails without retyping each one.
Event and webinar follow-up
After an event, parse the signatures from every reply and registration confirmation into a single spreadsheet for follow-up.
Account and support teams
Keep an up-to-date record of who at each customer emails you, pulling the current title and phone number straight from their latest signature.
Why parse signatures instead of copying them by hand
No retyping
Copying a signature into a spreadsheet is a few seconds each, until it is five hundred emails. A parser writes every field at once.
Consistent columns
Manual entry produces mixed formats and typos in phone numbers. A mapped field puts the same value in the same column every time.
Repeatable
Save the mapping and next month's emails parse the same way, so contact capture becomes a run rather than an afternoon.
Tied to the source
Each contact keeps the sender, subject, and date of the email it came from, so you know where every record originated.
Ways to get contacts out of email signatures, compared
There are three common ways to turn email signatures into a contact list, and each fits a different volume of mail. Here is an honest comparison. If you want the full inbox in a sheet rather than just the signature, the email to Excel and email to CSV pages cover that, and the email to CRM page covers pushing parsed fields into a CRM.
| What matters | MailParse (signature parsing) | Copy and paste by hand | Contact-scraping tool |
|---|---|---|---|
| What it does | Reads the signature in each email and maps its fields to columns | A person opens each email and types the details into a sheet | Harvests addresses from the web or a whole mailbox in bulk |
| Where the data comes from | The signature block in emails you received | The signature block, read by a human | Public pages or every message, often without context |
| Effort at 500 emails | Map the fields once, then run the folder | Hours of reading and typing | Fast, but noisy and hard to keep clean |
| Field accuracy | Consistent columns from the mapped fields | Depends on the person and their patience | Often just an email address, little structure |
| Best for | Building a structured contact list from real correspondence | A handful of important contacts | Wide, low-precision lead lists |
Capabilities described as of July 2026. MailParse reads the message itself: headers, body text, and HTML, which is where the signature lives. Attached vCard files are recorded by filename, type, and size; reading the contents of an attached file is a separate document extraction job.
Frequently asked questions
What is an email signature parser?
An email signature parser is a tool that reads the signature block at the bottom of an email and pulls the contact fields out of it. Instead of leaving the name, title, company, phone, and email as one block of text, it maps each value to a named field so you get a structured contact record you can export as a spreadsheet or JSON.
How do I extract contact details from an email signature?
Connect the mailbox or upload the emails, then name the fields you want, such as full name, job title, company, phone, and email. MailParse reads each message body and HTML, pulls those values from the signature block, and writes one contact per row. Export the result to Excel, CSV, or JSON, or push it to your CRM.
Can I turn email signatures into a spreadsheet?
Yes. Once your fields are mapped, MailParse writes every message in the folder as a row, with the signature values in their own columns, and exports an .xlsx workbook, a CSV, or JSON. Because the mapping is saved, you re-run the same export whenever new replies arrive rather than starting over.
Does it read HTML signatures with logos and links?
Yes. Many signatures are HTML with styling, a logo image, and linked icons. MailParse reads the underlying text and HTML of the message, so it can pull the name, title, phone, and email out of a styled signature the same way it does from a plain-text one. The logo image itself is treated as content, not a field.
How accurate is signature parsing?
Accuracy is highest when signatures follow a consistent layout, which is common within one company or a set of similar senders. Where formats vary widely, you refine the field rules to match the patterns you see. It is field mapping, not guesswork, so a signature that lists a title and phone in a predictable place parses reliably once the mapping fits it.
Can it pull contacts from a vCard attachment?
MailParse reads the email itself, meaning the headers, body, and HTML, and records each attachment, including a vCard (.vcf) file, by filename, type, and size. The contact fields it extracts come from the signature text in the message body. To read the fields inside an attached vCard or other file, run that file through a document extraction tool separately.
Is it different from an email scraper?
Yes. A scraper harvests addresses in bulk from web pages or an entire mailbox, often with little structure and no context. A signature parser works on emails you received and maps the real fields in each signature into columns, so you get a structured, sourced contact record rather than a raw list of addresses.
Can I send the parsed contacts to my CRM?
Yes. Export a CSV or Excel file in the column layout your CRM import expects, or send the parsed fields to your CRM through the MailParse API as each email is processed. Because you control the field names, the contact record matches your CRM schema rather than forcing a cleanup step after import.
Turn your inbox signatures into a contact list
Map the fields once and export a clean spreadsheet of names, titles, companies, phones, and emails, one contact per email.