How to clean an email list for free

To clean an email list for free, import it into Datablist, review duplicate contacts, run Free Email Address Validation, and exclude unsubscribed addresses from the sending list. Keep the original export so you can review changes before replacing data in your CRM or newsletter tool.

The free validator checks address syntax, disposable-domain signals, and optional domain MX records. It does not contact the recipient's mailbox or prove that an individual address exists. A passing address can still bounce, belong to the wrong person, or be ineligible for your campaign.

This workflow helps you:

• Merge duplicate contact rows without losing useful fields.
• Check syntax and domain-level problems.
• Apply the unsubscribe list before exporting.
• Append contact details without resetting subscription status.

You need an account to run the enrichment. Create a free Datablist account and import your file into a collection.

📌 Cleaning and mailbox verification are different checks. The free tool is useful for finding formatting and domain problems. If you need individual mailbox checks, use a dedicated verifier such as Waterfall Advanced Email Address Verification. Review its result categories rather than interpreting every free-validation pass as deliverable.

Step 1: Import email addresses

Create a collection

The first step in the email cleaning process is to create a collection on Datablist in which you will pour your email addresses.

In Datablist, click the + to create a new collection. Give it a name (and an icon 😍).

Import your lists of email addresses

Now you have a collection, it's time to import your email lists: whether you have only one email list or several that you want to merge!

Datablist offers two options to import your data:

• With CSV files
• Using copy/pasting from a spreadsheet

Option 1: Import from CSV files

The CSV format is a simple standard to transfer tabular data between software applications. Every newsletter tool and digital marketing solution offers exports of your contacts in CSV files. CSV files are first-class citizens in Datablist.

See also our How to join CSV files by a unique identifier and How to remove CSV duplicates guides.

In this example, we will use a demo contact CSV file with three columns: First Name, Last Name, Email. Download the demo file here.

To import your CSV file, click the "Import CSV" button and select your file.

Datablist reads CSV files and Excel files. Your first rows will be read to detect the encoding used. If you see weird characters on the detected headers or later when importing, create another collection and try to import your CSV using another encoding.

Datablist will read the columns and it will show you a mapping page. If your email addresses are valid, the data type will be Email. It will add some validation to your data edition later on Datablist.

Here is a video of the full process:

Option 2: Import with Copy/Pasting

Datablist is compatible with copy/pasting from any spreadsheet. Just select the cells from your spreadsheet, go to your Datablist collection, and use the Edit -> Paste from your browser or directly the Ctrl + v keyboard shortcut.

On pasting, Datablist will show you the columns and rows it has detected. To import a column, map it to an existing property or create a new property.

Warning: Only mapped columns will be imported!

Import other contact lists if needed

If you combine several sources, keep a source column and the original subscription status. Importing a new file should not replace an unsubscribe flag with an empty value or mark a previously suppressed contact as subscribed.

When importing another file, a mapping step will be shown. In this step, you will map your collection properties with your CSV columns. With this mapping, your new data will be added to the existing properties.

Step 2: Find and merge duplicates

Email lists accumulate duplicates when you combine CRM exports, forms, event registrations, and newsletter contacts. The repeated rows often contain different details, so deleting every extra row can remove a phone number, source, or note you still need.

Dedupe means identifying repeated records and removing or merging the extras. For a contact list, choose the identifier and review conflicts before deleting rows.

Before validating the addresses, dedupe the email addresses with Smart email matching. The workflow covers exact repeats, plus aliases, Gmail dot variations, gmail.com and googlemail.com, display name wrappers, and mailto: values.

The short version is:

1. Open Clean > Merge duplicates.
2. Choose Selected Properties and select the email property.
3. Configure Smart matching with the Email processor.
4. Review the proposed groups and shared inboxes.
5. Merge complementary contact rows or remove an extra row when it adds no useful data. Preserve unsubscribe and suppression flags across the group; a duplicate from another source does not reset the contact's status.

The duplicate check is read-only until you process the reviewed groups. The email deduplication guide includes the current screenshots, a downloadable 1,000-row test file, measured results, and the validation checklist.

Step 3: Free email list cleaning

Now you have a collection on Datablist with all the email addresses and no duplicates, it's time to clean it!

Notes
You must be registered to use the email verification service. Sign up (it's free). If you already have an anonymous collection, import it into your workspace.

What does the service check?

Free Email Address Validation provides five kinds of information:

• Whether the address passes the implemented syntax checks.
• Whether its domain appears on disposable or blacklisted domain lists.
• Domain DNS/MX checks when enabled.
• An identified MX provider when available.
• Whether its domain is classified as a business or generic-provider domain.

These are address-format and domain checks. They do not test a specific mailbox through SMTP and do not establish permission to send a campaign.

Email syntax analysis

The syntax check flags malformed addresses, such as missing @ signs or invalid address structure. It can help you find rows worth reviewing for a typo.

Do not guess corrections to a person's address. Check the original source, correct the stored value if you can confirm it, and validate the corrected address again.

Check domain MX records

With MX analysis enabled, the validator looks up domain DNS records and mail-exchange records. A completed check can flag a missing domain as DNS Error or missing MX records as Missing MX Records.

The check concerns the domain, not the individual recipient. alex@example.com and sam@example.com use the same domain records even though only one mailbox might exist.

If the DNS lookup cannot complete, the tool does not treat that failure as a confirmed invalid domain. A passing Valid Email result therefore does not guarantee that every domain check succeeded. Review inconclusive cases or use a dedicated mailbox verifier when deliverability matters.

MX checks are also the tool's validation rule, not a complete description of all email routing: SMTP specifies an implicit MX fallback in certain cases. Keep the error category available for review instead of deleting every flagged row without inspection.

Check disposable providers

The third check is to detect temporary emails. The service looks for domains belonging to Disposable Email Address (DEA) providers such as Mailinator, Temp-Mail, YopMail, etc.

Disposable-domain detection uses domain lists. Coverage can change and is not exhaustive; an unflagged domain is not proof that the address is permanent. Keep flagged contacts separate when deciding which addresses suit your list.

Return the name of the email provider

When you run Free Email Address Validation, one of the key results you'll see is the "MX Provider" output. This field tells you which email service is responsible for handling the emails for a given domain, based on the MX records.

Email provider can be: google, microsoft, amazon, etc.

Check the enrichment documentation to learn more: Free Email Validator.

Check if the email address is a business email

The Business Email output classifies the domain against a generic-provider list. It does not confirm that the person works at a company or is the intended recipient.

A business email address typically uses a company domain (such as alex@example-company.com). When segmenting your contacts to build your lead lists, spotting the business email addresses will help you in the lead scoring.

Datablist maintains a list of generic email providers. It compares each email domain with this list. The email address is labeled as "Business Email" when the domain doesn't belong to the list of generic email providers.

Perform cleaning on your collection

Open Enrich and select Free Email Address Validation. Older screenshots below use the shorter Email Address Validation name; use the current enrichment label in the app.

A configuration drawer opens on the right.

The configuration takes 2 steps:

• The configuration of Settings and Input Properties
• The configuration of Output Properties to define where to store the results from the enrichment

Settings and Input Properties

Settings

Leave Disable MX-records analysis unchecked to include domain MX checks. Check it only when you want to skip that analysis. The current setting is a disable switch, so its meaning is the reverse of the option shown in older screenshots.

Input Properties

Select the property from your collection that contains the email address. In this example, the collection has an "Email" property that will be matched.

👉 Click on "Continue to outputs configuration" to move to step 2.

Output Properties and Run Settings

Free Email Address Validation returns six outputs:

• Valid Email: whether the address passed the checks performed. This is not a mailbox-deliverability guarantee.
• Error status: the reason for a failing check, such as Invalid format, Disposable or blacklisted domain, DNS Error, or Missing MX Records.
• Business Email: the domain classification, not proof of employment or contact ownership.
• Domain: the part after @.
• MX Provider: an identified mail provider when available, such as Google or Microsoft. A blank value can be inconclusive.
• Processed Email: whether the address was processed. It is not another validity or deliverability result.

⚠️ You must configure the output properties to create new properties in your collection. Or map them to existing properties.

If you are running the enrichment for the first time, click on the + button on each output property to add the result properties to the collection.

Properties to be created are displayed on the right of your columns.

When the outputs are mapped with properties to store the results, click "Run on first 10 items". Before running the enrichment on all your items, Datablist processes the ten first items. Errors and invalid configurations are easier to detect from those first results.

If the results are correct, click "Run the enrichment on all items" to resume the process.

Here is a video of the process (this is an old Datablist version, the email verification enrichment is now accessible from the "Enrich button"):

Once the run finishes, review failed rows by Error status. Keep the source email and validation fields together. Correct only confirmed typos, and keep uncertain addresses out of the export until you have reviewed them.

Dedupe email aliases before validation

Plus aliases can point to the same mailbox while remaining valid addresses. Validation alone will not identify them as repeated contacts.

Use the email-aware deduplication workflow instead of rewriting the stored values with a regular expression. Smart matching can compare the aliases without changing the original email values, then lets you review the full contact rows before merging anything.

Keep role-based inboxes such as sales@company.com in manual review. One shared address can belong to several legitimate contacts.

Step 4: Remove unsubscribed emails

Before creating a sending list, apply your current unsubscribe and suppression data. An address can be syntactically valid and still need to stay excluded.

For example, an unsubscribe CSV can contain:

Join this status onto your contact collection using the email property. This join compares the join-key values; it does not apply the Duplicates Finder's Smart email matching. Review inconsistent formatting and aliases so a changed spelling does not bypass your suppression process.

To do so, import the second CSV file and perform a join operation into the same collection.

The common property is the Email property. You will perform a "join operation" on the Email property.

During the import of the CSV files with the unsubscribed email addresses, create a new property to store the "Unsubscribed" column (click on the "+" during the mapping).

Map the "Email" column with the "Email" property if not already done. Then enable "Join on property" on it.

In the next step, you have to configure how Datablist will perform the join operation on the CSV file.

• Import all rows and match when possible - If an email address from the Unsubscribed file doesn't exist in your collection, a new item will be created.
• Import only matching rows - This is the recommended option here. If an email address from the Unsubscribed file doesn't exist in your collection, the email address will be skipped during the import.

Review Merging Mode before importing. Soft Merge fills empty fields but preserves a populated value, including an existing Unsubscribed = no. If this authoritative file must change that value to yes, map only the email key and unsubscribe flag, then use Hard Merge to update that flag. Do not map unrelated contact columns for this status update.

After the import, check that known unsubscribed contacts have the flag. Exclude those rows from the sending export and keep the suppression record for later imports. Deleting the only record of an unsubscribe can let the same address return in a future file.

Extra Step: Automatic merge duplicates during import

To prevent and merge duplicates automatically during the import process. Check the "do not allow duplicate values" option on the Email property. With this property option, Datablist will automatically deduplicate and merge your contacts on data import.

During the import stepper, a merging option will be shown to configure how Datablist must deal with duplicates.

The merging option is important when your listing contains contact information in addition to the email address.

• With Soft Merge, an existing matching contact keeps populated fields while empty fields can receive the imported data. This is the default setting.
• With Hard Merge, imported data can replace populated fields on matching contacts. Review the mapped columns before using it, particularly subscription and suppression fields.

Clean and append an email list

Appending means adding details to existing contact rows, such as a company name or job title. It does not validate the address or change the contact's subscription status.

1. Clean and review the base list first. Keep a stable contact ID where available, plus the original email and source.
2. Keep the appended data in a separate file with the same reliable join key.
3. Use a CSV join and map only the fields you intend to add. Soft Merge can fill blanks without replacing populated contact fields.
4. Review unmatched and conflicting rows. A shared mailbox is not a unique person identifier, and a changed email can require matching on a contact ID instead.
5. Reapply current unsubscribe and suppression data before exporting the sending list. Appending a company or phone number never resets those flags.

Keep validation results distinct from campaign eligibility. Review the final export's row count and spot-check known suppressed contacts before importing it into your sending platform.

If you have any feedback on this guide or if you have questions, please contact us.