Book a demo →
How-to

Import entries from CSV

To import entries from a CSV, upload the file to the collection, map each column to a field or ignore it, choose what happens to rows that match existing entries, and run a dry run before the real import. To update entries rather than create them, export first, edit the file, and import it back with the record id column.

To import entries from a CSV, upload the file to the collection, map each column to a field or ignore it, choose what happens to rows that match existing entries, and run a dry run before the real import. To update entries rather than create them, export first, edit the file, and import it back with the record id column.

Requirements

  • A role that can create entries in the collection.
  • The collection has the fields your columns will go into. Add any that are missing first; see Add or change a field.
  • A CSV file with a header row.

Steps

  1. Upload the file to the collection. A brokerage moving from spreadsheets uploads its listings file to the Listings collection.
  2. Map the columns. Match each column to a field. Grouped fields are offered part by part, such as an address's city or a phone's number. Mark columns you do not want as ignored.
  3. Set column options. Tell the import how to read dates. Without a date format, 01/02/2026 is read as January 2, so a file with day-first dates imports wrong dates that still look plausible.
  4. Choose what happens to matches. A row matches an existing entry through a unique field, such as an email or a listing reference number, or through the record id. For matching rows, choose to skip them or update the entry.
  5. Run a dry run. Check what would be created, updated, skipped, and which rows would fail, before anything is written.
  6. Import.

Updating entries in bulk

  1. Export the collection. Each row carries the entry's record id.
  2. Edit the file. Keep the record id column and the columns you changed; you can drop the rest.
  3. Import it back, mapping the record id column to the entry id. Each row updates the entry it names. A row whose id names no entry fails; it never creates a duplicate.

Reuse a mapping

Save the mapping as a preset. The next time you upload a file with the same header row, the preset is offered, with its column options, so a weekly import is set up once.

Notes and limitations

  • Matching needs a key. Skipping or updating matches works only when the mapping includes a unique field or the record id. Without one, nothing matches and every row is created as a new entry.
  • What cannot be imported. Rollups and lookups are computed and refuse imported values. Secret fields are not import targets.
  • Imports are run by a person. An agent can prepare the mapping preset for you, but uploading the file and running the import happen in the product.
  • Automations. An automation on new entries fires for imported entries only if it listens to the import source; see Automation triggers.
  • Checks still apply. Each row goes through the same checks as an entry created by hand: required fields, unique values, and field rules. See My entry will not save.

For one entry at a time, see Create an entry.

Frequently asked questions

How do I update existing entries with a CSV?
Map the record id column from an export to the entry id. Each row then updates the entry it names instead of creating a new one.
Why were my dates imported wrong?
Without a date format set, dates are read month-first. Set the day-first format for the date column.
Why did my import create duplicates?
The mapping had no unique field or record id to match on, so every row was created as new. Include one and choose skip or update.