[GTM glossary](/glossary.txt) Email validation

Data & Enrichment

# Email validation

Also known as: validate email address

In short

Email validation is the process of checking that an email address is well formed and technically plausible. It confirms basic rules such as the presence of an @ symbol, a valid domain, and working mail records for that domain.

Validation is usually the first and cheapest check. It catches typos and fake entries at the point of capture. It does not prove that a specific mailbox exists or belongs to the person, which is the job of email verification.

## What validation checks

* **Syntax:** Correct structure: one @, allowed characters, no spaces.
* **Domain:** The domain exists and has mail exchange records.
* **Known bad patterns:** Disposable address services, role accounts such as info@, and common typos like gmial.com.

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## Validation vs. verification

Validation answers "could this be an email address?" while verification answers "will this one deliver?" Use validation early and verification before sending.

* **Validation:** Checks format and domain. Fast, can run in the browser on a form.
* **Verification:** Checks whether the specific mailbox is likely to accept mail, usually by contacting the mail server.

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## Where to apply it

### On forms

Validate in real time so visitors can fix typos on the spot. A typo caught here is a lead saved.

### On imports

Run every uploaded list through validation before it enters the CRM so bad rows never create records.

### In enrichment workflows

Validate addresses returned from any data source before writing them to contact fields.

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## Common mistakes

* **Stopping at validation:** A valid-looking address can still bounce.
* **Rejecting valid but unusual addresses:** Overly strict rules can block real users, such as plus addressing.
* **Silent failure:** Show a clear message when a form rejects an address.

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## Email validation FAQs

**What is email validation?**

Email validation checks that an address is correctly formatted and that its domain can receive mail. It catches typos, invalid characters, and fake domains, but it does not confirm that a particular mailbox exists.

**What does email validation check?**

It checks syntax, such as a single @ symbol and allowed characters, confirms the domain exists with mail records, and often flags disposable domains, role accounts, and common typos. It stops short of testing the individual mailbox.

**Is email validation the same as email verification?**

No. Validation checks format and domain, so it is fast and can run on a form. Verification goes further and tests whether the specific mailbox is likely to accept mail. Most teams use validation first and verification before sending.

**Where should you use email validation?**

Use it on web forms to catch typos as people type, on list imports before records enter the CRM, and on any enrichment output before it is saved. Checking early prevents bad data from spreading across your systems.

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## More related phrases

* [Confirmed opt-in](/glossary/confirmed-opt-in.txt)
* [Data accuracy](/glossary/data-accuracy.txt)
* [Suppression list](/glossary/suppression-list.txt)

## Similar phrases

* [Email finder](/glossary/email-finder.txt)  
An email finder is a tool that locates a professional email address for a named person at a company, usually from a name and domain, and ideally checks that it works.
* [Email verification](/glossary/email-verification.txt)  
Email verification tests whether a specific email address is likely to accept mail, helping teams cut bounces, protect sender reputation, and avoid wasting outreach on dead addresses.
* [Catch-all email](/glossary/catch-all-email.txt)  
A catch-all email address is on a domain configured to accept mail for any address, which makes it impossible to confirm that a specific mailbox exists.
* [Data hygiene](/glossary/data-hygiene.txt)  
Data hygiene is the ongoing practice of keeping CRM and marketing data clean, accurate, consistent, and usable through regular checks, standards, and ownership.
* [Data quality](/glossary/data-quality.txt)  
Data quality describes how well a dataset is fit for its intended use, measured by traits such as accuracy, completeness, consistency, timeliness, and uniqueness.