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Map CSV columns to fields during import

Match the columns in your file to the correct subscriber fields so data lands in the right place. Column mapping is the step where you tell the platform which column in your file holds the email address, which holds the first name, and so on — so every value ends up in the field you expect.

Before you begin​

  • A CSV (or similarly delimited) file with a header row naming each column. A clear header makes mapping far easier.
  • One column that uniquely identifies each subscriber — usually the email address.
  • A quick review of the subscriber fields already defined in your account, so you know what each column can map to. If a column has no matching field, create the field first, then re-run the import. (Confirm the exact field-management steps in your account.)
  • Remove blank columns and stray formatting from the file to avoid mismatched rows.

Steps​

  1. Start a new import of your subscriber list and select your file. (Confirm the exact menu path in your account.)
  2. When the mapping screen appears, review each source column shown alongside a target field selector.
  3. For every column you want to keep, choose the matching subscriber field from the dropdown. Set the email column to your unique-identifier field.
  4. Mark any columns you do not need as Do not import (or leave them unmapped) so unwanted data is skipped.
  5. Confirm the format of special columns — dates, numbers, and yes/no fields — matches what the target field expects.
  6. Use the preview of sample rows to check values are landing in the right fields, then confirm and run the import.

Result​

The import completes and each column's values appear in the mapped subscriber fields. Spot-check a few subscriber records to confirm names, dates, and custom values are correct. You can now use these fields as personalization tokens and as criteria when segmenting your audience.

Canonical terms: Author, Edition, Folder (Project Folder), Broadcast. See the Glossary.