When you upload a contact list and see an ‘errors log’ message in the Actions log, it means something with your contact list is incorrect.
Reasons that can cause an error log:
• Incorrectly formatted contact (CSV) file *
• Incorrect number of header columns
• Too few columns on a certain line (or missing field separators)
• One or more email addresses are not in the format: name@website.com
• Duplicate email addresses (the duplicated address will be discarded)
• Could not convert value (incorrect date formats)
• Import aborted by the system due to a “critical” error
To download the error log, click the ‘Download error log’ button. Review the error messages within the log file, and correct your contact list. Note: if there are no errors in your next upload, the errors log will disappear.
* For information on how to create a CSV file to upload your contacts, please read our FAQ.
Error logs explained
Number of contact properties exceeds the limit of X
Your CSV import is trying to create/map more contact properties (custom fields) than the system allows. 200 is the maximum limit (including the email field).
Typical example
Limit is 200 properties, but your file has 260 custom columns:
email,field_01,field_02,field_03,...,field_260
john@example.com,a,b,c,...,z
→ The importer would need 260 properties (one per column), which is above the limit → Number of contact properties exceeds the limit of 200.
Fix: Remove/merge columns or perform multiple uploads, specifying different properties in each file:
- CSV 1 - properties 1-199
- CSV 2 - properties 200-260
Email column not found
It means the import system couldn’t identify any column in your CSV as the Email field, so it doesn’t know which value to use as the contact’s unique identifier.
This usually happens in one of these cases:
1) The file has no email column at all
Header:
firstname,lastname,city
There is simply no column that contains emails → Email column not found.
Fix: Add an email column:
email,firstname,lastname,city
2) The file has emails, but the header name isn’t recognized
Example header uses a non-standard name:
contact_address,firstname,lastname
john@example.com,John,Doe
The importer only auto-detects headers like email / Email → Email column not found.
Fix: Rename the header to email.
3) The file has no header row (first row is data)
john@example.com,John,Doe
jane@example.com,Jane,Doe
If the importer expects a header row, it can’t “see” an Email column → Email column not found.
Fix: Add a header row:
email,firstname,lastname
john@example.com,John,Doe
4) Wrong delimiter makes the header unreadable
Header is semicolon-separated, but the rest of the CSV uses comma-separated (or vice versa):
email;firstname;lastname
john@example.com,John,Doe
This makes rows parse differently from the header and leads to an error.
Fix: Use only one delimiter across the entire file (header + all rows).
Import aborted
Typically, when too many errors are encountered (1000 errors or 10% of the list imported, whichever is higher).
Invalid UTF-8 character at line X
The file isn’t valid UTF-8 (wrong encoding or broken characters) on that line.
Example 1 (typical): file saved in Windows-1252, contains “smart quotes”
Your CSV line might look fine in Excel, but the quotes are “curly”:
john@example.com,John,“Marketing team”
Those curly quotes (“ ”) are common when text is copied from Word/Outlook and the file is not saved as UTF-8. The importer then flags: Invalid UTF-8 character at line X.
Fix: Re-export as CSV UTF-8 (or open in a text editor and save as UTF-8).
Example 2 (common with Cyrillic): file saved in Windows-1251, contains Cyrillic
ivan@example.com,Иван,София
If the file is Windows-1251 but the importer expects UTF-8, the bytes for Иван / София won’t decode correctly, and you get: Invalid UTF-8 character at line X.
Fix: Save/export the CSV as UTF-8.
Example 3 (corrupted byte): “replacement character” or weird symbol appears
john@example.com,John,ACME � Ltd
The � often indicates corrupted/invalid bytes during conversion. That row can trigger the UTF-8 error.
Fix: Remove/replace the bad symbol and save as UTF-8.
Too many columns at line X
That line has more values than the header requires, usually because of missing delimiters or broken quotes/newlines.
What that looks like in practice
Header (expects 3 columns, comma-separated):
email,firstname,lastname
Bad row (extra , → 4 columns):
john@example.com,John,,Doe
If the middle value is not intended, fix it:
john@example.com,John,Doe
If it’s an empty middle field that is intended, then your header/mapping must include that column too.
Too few columns at line X
That line has fewer values than the header requires, usually because of missing delimiters or broken quotes/newlines.
What that looks like in practice
Header (expects 4 columns):
email,firstname,lastname,city
Bad row (only 3 columns → “too few columns”):
john@example.com,John,Sofia
Here, the lastname value is missing (or a delimiter is missing), so the row has only 3 fields instead of 4.
Wrong email format (field: email)
The email value in that row isn’t a valid email address (missing @, spaces, invalid characters, multiple emails, etc.).
Common examples that trigger it
Missing @:
john.example.com
Missing domain part:
john@
Spaces (often from copy/paste):
john @example.com
Trailing/leading spaces (may be invisible):
" john@example.com "
Invalid characters:
john<>@example.com
Two emails in one cell:
john@example.com; jane@example.com
Display name instead of raw address:
John Doe <john@example.com>
How to fix
Make sure the cell contains only one email in the standard format:
john@example.com
(Trim spaces, remove names/brackets, and split multiple emails into separate rows.)
Duplicated email (field: email)
The same email appears more than once in the uploaded file.
Local part too long (field: email)
The part before @ is longer than allowed, often caused by wrong column mapping or invalid data in the email column. Map the correct email column and fix the value.
Domain too long (field: email)
The part after @ is longer than allowed, often caused by wrong mapping or malformed email values. Map correctly and fix the email.
Could not convert value (FieldName)
These errors mean the property exists, but the value in the row can’t be stored because it doesn’t match the property’s type in the database. Format the value correctly (e.g., valid dates, numeric-only numbers).
Example (integer property)
Property: age is integer
CSV row contains:
email,age
john@example.com,abc
→ conversion fails because abc isn’t an integer.
Fix:
john@example.com,43
Example (date property)
Property: birthdate expects a date
email,birthdate
john@example.com,today
→ invalid date format/value.
Fix (use expected format):
john@example.com,2026-03-06
Property validation error (FieldName)
This error means the contact property name taken from your CSV header is not allowed, so the system can’t create/map that property.
1) Invalid character in name at pos N: X
Meaning: The property name (column header) contains a forbidden character at position N.
Example (bad header):
email,first-name
john@example.com,John
If - isn’t allowed → invalid character.
Fix: Rename the header using allowed characters (typically letters/underscore):
email,first_name
2) Numbers cannot be used as names: X
Meaning: The property name can’t be numeric-only (or can’t start with a number, depending on rules).
Example (bad header):
email,123
john@example.com,test
Fix:
email,field_123
3) Invalid name: X is a reserved word.
Meaning: The header matches a reserved keyword the system won’t allow as a property name.
Example (bad header):
email,class
john@example.com,A
Fix: Rename the header:
email,customer_class
Mapped header has less columns than header in file
When you import a CSV file, the system first reads the header row - the column names at the top of the file. In the import screen, it then tries to match each column to an existing contact property, or suggests creating a new one with an appropriate data type.
This process is called the header mapping. It defines how the columns in your CSV file correspond to the fields Mailjet will use during the import.
If the mapped header does not match the actual header in the file, the import cannot continue and will stop immediately.
If you encounter this error, please contact our Support team for further assistance.
Mapped header has more columns than header in file
When you import a CSV file, the system first reads the header row - the column names at the top of the file. In the import screen, it then tries to match each column to an existing contact property, or suggests creating a new one with an appropriate data type.
This process is called the header mapping. It defines how the columns in your CSV file correspond to the fields Mailjet will use during the import.
If the mapped header does not match the actual header in the file, the import cannot continue and will stop immediately.
If you encounter this error, please contact our Support team for further assistance.