Match your columns to fields

Mapping tells the import which column in your file belongs in which field on the record. DealJourney proposes a mapping for you, shown with an AI Confidence per column (AI suggestions applied).

Check every proposal, especially the confident ones. A column mapped to the wrong field imports cleanly and silently: no error, just data in the wrong place, found weeks later. The confidence score describes the guess, not whether it is right. A column whose name is exactly the field's name, in the language you use DealJourney in, is shown as a certain match.

Each field gets one column. When two columns would fit the same field, such as a full name next to separate first and last names, the proposal gives the field to the better fit and leaves the other unmapped or moves it to its next best field. The proposal is also double-checked: when the second check would pick a different field, the column is moved to the ones that need your attention, and its explanation names the alternative. Start your review there.

Fields not in your file. A field can take its value from a column, or you can give every row in the file the same value for a field the file does not have. Under the column list, choose Add a field, pick the field and its value: one customer type, one account manager, one source or campaign for the whole import, without adding a column to the spreadsheet. The value is treated exactly as if the file had a column holding it on every row, so it also applies to records the import updates, not only to new ones. A person is picked by name and matched by their email. Saved mappings remember these values too.

Custom fields are importable. Any field your company has added appears in the Database Field list alongside the built-in ones, so your own project numbers and categories arrive with everything else instead of being typed in afterwards.

Linking to customers. Customer Auto-Linking (Recommended): Link {{model}} to existing customers in your tenancy automatically. Pick which field on the customer to match against, then select the CSV column that holds that value.

You choose what to match on, and the choice decides how well it works:

• Customer Name: Select the CSV column containing customer/company names. We'll match them against existing customers in your system (case-insensitive).
• Organisation number, which is the more reliable option whenever you have it. Values are normalised to digits-only, so formatting differences are ignored. Names vary in punctuation and legal suffix; registration numbers do not.
• A custom field, when your own reference is what ties the two together. Matches customers whose "{{field}}" custom field equals this column's value.

Rows whose customer cannot be matched still import: they simply arrive unlinked, which is easy to miss because nothing fails. Check for records with no customer after an import rather than assuming everything connected.

Save a mapping and use it again. When the same export arrives every month, Save as template… keeps the whole set-up: which column goes where, the fixed values, the primary record type and the note you wrote about the file. Saved mappings belong to the workspace, so a colleague can use yours.

They are listed under Saved templates on the first import screen, before you choose a file, where you can also delete one you no longer need. Click one to choose it, then choose your file: the file goes straight to the mapping step with that saved mapping applied, without the AI matching. Click the chosen one again to go back to mapping without it. If you did not choose one, a saved mapping that matches the file's columns is offered straight away, and Load template on the mapping step lets you pick any of them by hand. Columns the file no longer has are dropped, and columns the saved mapping does not know about are left for you to map.

Clear all mappings starts the mapping over. Clear all mappings and start from scratch? Your file selection will be kept.