A duplicate merge rule defines how values are kept, replaced, combined, or ignored when duplicate records are merged.

Finding duplicates is only the first step. The second step is deciding what happens to the fields inside those duplicate records.

Common duplicate merge rules

Common merge rules include:

  • Keep the first non-empty value
  • Keep the newest value
  • Keep the longest value
  • Keep the value from a trusted source
  • Combine values with a separator
  • Keep both records for manual review when fields conflict

For example, two duplicate contacts might have different job titles. A merge rule decides whether to keep one title, combine both, or send the record to review.

⚠️ Do not merge blindly

A bad merge rule can overwrite useful CRM data. Review conflicting fields before running a bulk merge.

Merge rules and field types

Different fields need different rules.

Emails and phone numbers often need validation before merging. Notes and tags can often be combined. Company names might need company name normalization before matching. Dates often need a newest-value rule.

Good merge rules protect useful data while removing duplicate rows.

Example CRM merge workflow

A controlled duplicate merge workflow looks like this:

  1. Normalize values with data normalization.
  2. Find duplicate records with exact, fuzzy, or phonetic matching.
  3. Review groups with conflicting fields.
  4. Apply merge rules by field type.
  5. Export the merged records or update the CRM.

Datablist supports duplicate detection and merge workflows with the Duplicates Remover. For full examples, read Merge duplicate leads without losing data, merge duplicate rows in Excel, and remove CSV duplicates.