Customer import check
Check customer imports for missing email columns, duplicate customer IDs, invalid country codes, and row count format issues.
Your file is read in your browser. It is never uploaded.
Every default rule on this page
These run as soon as you drop a file in. Nothing is uploaded - the whole check happens in this browser tab. Edit or remove any of them in the rule editor above before you run it.
| Column | What it checks | What fails | Reported as |
|---|---|---|---|
| Customer ID | must have a value in every row | the cell is empty, or the column is missing entirely | Error |
| Customer ID | every value appears once, ignoring capitalisation | the same value appears in two or more rows | Error |
| must have a value in every row | the cell is empty, or the column is missing entirely | Error | |
| Country Code | one of US, CA, GB or AU, ignoring capitalisation | any other value, including a blank cell | Warning |
| Phone | matches the pattern 999-999-9999 | the value does not match that pattern | Warning |
| Customer Since Date | a date between 1980-01-01 and 2099-12-31 | the cell is not a date, or falls outside that window | Error |
| the whole sheet | at least 1 data row | the sheet has fewer than that | Warning |
An error is a row you almost certainly need to fix before sending the file on. A warning is a row worth looking at, most often a value that is legitimate in your process but unusual in this one - change the rule to match how your team works rather than editing the data to satisfy it.
Customer ID column missing or renamed
Customer exports may use 'Account Number', 'CustomerNo', or 'ID' instead of 'Customer ID'. Without a stable customer identifier, duplicate checks and row matching cannot run reliably. The header must be present and consistent before checking.
Email column missing or blank
Some rows have no email address, or the email header is missing. A required check flags those rows so you can complete contact details before importing the customer list. If email is optional, edit the rule to reduce errors.
Duplicate customer ID values
The same customer ID appears more than once, often when a CRM export combines records from multiple sources. Duplicate rows make it difficult to know which record is current. Keep only one row per customer ID.
Country code values vary
One row uses 'US', another 'USA', and a third 'United States'. Inconsistent country codes cause downstream validation issues. Allowed value checks highlight values that fall outside the expected code set. Map all codes to a single two-letter list.
How to fix
- Rename customer ID header variations to exactly 'Customer ID' before upload.
- Fill in missing email addresses or adjust the required rule if email is optional for some rows.
- Remove duplicate customer ID rows or keep only the most recent record for each customer.
- Map all country code values to the same allowed set such as US, CA, GB, and AU.
- Delete summary rows, blank rows, and notes rows from the data area so only detail rows remain.
FAQ
What columns are checked by default?
The default rules check Customer ID, Email, Country Code, Phone and Customer Since Date. The rules run against exact headers, so check that those exist and use consistent names. Customer Name is not checked by default, because a missing name is usually a record to review rather than a failed import.
Why is a missing email address flagged?
Email is a required field in the default check. If your import does not require email, edit the rule to a warning or remove it. Missing emails can otherwise cause avoidable errors before import.
Can I use full country names instead of codes?
The default allowed values use two-letter country codes such as US, CA, GB, and AU. If your file uses full names, update the allowed value list to match your import format. Consistency helps downstream validation.
What date format should I use for customer since dates?
Use ISO yyyy-mm-dd values for customer since dates. That avoids regional ambiguity and lets date range checks compare values correctly. Convert text or regional dates before running the checker.
What phone number format is expected?
The default pattern expects digits and dashes only, such as 555-123-4567. Remove spaces, parentheses, and letters before running the check. Consistent phone formats help keep customer contact data uniform.