How to map fields during an import
Mapping is the step where you tell the import which column goes where. It looks mechanical but is where most silent import errors originate.
Careful mapping plus a test batch is the whole difference between a clean load and thousands of misplaced values.
Short answer
Map fields during an import by matching each source column to the correct CRM field, translating picklist values to the CRM's accepted options, handling fields that need to be split or combined, deciding where unmapped columns go, and testing the mapping on a sample. Verify the mapping on a test batch, because mapping errors load silently without any error.
Step by step
Match columns to fields
Pair each source column with the correct destination field, watching for near-matches that are actually different (billing vs shipping address).
Translate picklist values
Convert the source's picklist values to the CRM's accepted options, so "Closed Won" maps to your equivalent stage rather than failing.
Handle splits and combines
Split combined fields (full name into first and last) or combine as needed so values land in the CRM's structure.
Decide on unmapped columns
Choose whether to ignore, store, or create a field for columns with no obvious home, rather than dropping data silently.
Test the mapping
Run a sample and verify each value landed correctly before the full load.
Watch for near-match fields
The subtle mapping trap is fields that look like matches but are not: billing versus shipping address, company name versus account name, created date versus close date. These map cleanly by name but load wrong data. Read field meanings, not just labels.
How Ardovo helps
Ardovo auto-maps obvious columns, prompts you on ambiguous ones, and translates picklist values between systems. Rook previews the mapped result on a sample and flags near-match mistakes, so values land in the field you actually meant.
Frequently asked questions
What is the hardest part of field mapping?
Near-match fields that look like a clean pairing but mean different things - billing versus shipping address, company versus account name, created versus close date. They map by label but load wrong data. Reading what each field means, not just its name, is what avoids this.
How do you map picklist values on import?
Translate each source value to the CRM's accepted option, so the source's stage or status names map to your equivalents. Unmapped picklist values either fail or land as blanks, so the value translation matters as much as pairing the fields.
What do you do with columns that do not match a field?
Decide deliberately: ignore them, store them in a notes or custom field, or create a new field if the data matters. The mistake is letting unmapped columns silently drop - if the data is worth importing, give it a home.