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How do I remove duplicate customers from my data?
Standardise the fields first, such as trimming spaces, lowercasing emails and normalising phone formats, then match on strong identifiers like email or tax number. For fuzzy matches on names and addresses, flag likely duplicates for human review before merging, and keep a record of every merge decision.
Steps that work in practice:
- Clean: trim, case-fold, remove punctuation, standardise abbreviations.
- Match: exact on unique IDs, then fuzzy on name plus address or postcode.
- Review: automated merging of uncertain matches creates new errors.
- Merge: keep a surviving record and a mapping from old IDs to the new one.
Do not delete the originals in source systems without understanding the downstream effects on invoices and history. Often it is safer to deduplicate in the reporting layer and leave source records untouched.
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