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CHECKLIST

A Practical CRM Data Cleanup Checklist

Most CRM cleanups stall because they start in the wrong place. De-duplicating before standardising means the matching runs against inconsistent values and misses half the pairs; re-verifying before de-duplicating means paying to verify the same person three times. This is the order that avoids repeating work, with the checks that belong at each pass.

2 min read

Before you change anything

  • Take a full export. Every step below is reversible only if you can compare against the starting state.
  • Agree what a duplicate is for your business: same company domain, same contact email, or a fuzzier match on name plus employer.
  • Agree which record wins a merge, and write it down. Most-recently-modified is a common default and a poor one.
  • Decide what happens to records that fail every check, rather than deciding it individually five hundred times.

Pass one: standardise fields

  • Normalise company names: legal suffixes, punctuation, casing, trading names.
  • Normalise domains to a single form, stripping protocol and subdomain where appropriate.
  • Standardise country, region and state values against one list.
  • Reduce free-text roles and industries to your agreed picklists, and record the original value somewhere.
  • Fix phone and postcode formats so they sort and match predictably.

Pass two: remove duplicates

  • De-duplicate companies before contacts, or the same contact merges under two parents.
  • Merge rather than delete, so activity history and ownership survive.
  • Resolve conflicting field values using the rule you agreed, not case by case.
  • Re-run the match after merging: merges create new duplicates when two parents held near-identical children.

Pass three: fill the gaps

  • Identify the fields that are actually required for segmentation and routing, and ignore the rest.
  • Research missing values for accounts that matter rather than for everything.
  • Mark unresolvable gaps explicitly. An empty field and a confirmed-unavailable field are different facts.

Pass four: verify contact data

  • Re-verify email addresses last, once each contact appears exactly once.
  • Carry the result as a status on the record: Verified, Risky or Not Found.
  • Keep reviewed catch-all records separate from standard valid data rather than merging the counts.
  • Do not delete Not Found contacts automatically. Flag them, and keep the account context they sit on.

Pass five: check it against how the team works

  • Run your three most-used segments and confirm the counts are plausible.
  • Check ownership and routing rules still resolve after merges.
  • Confirm reporting that depended on the old field values has been updated.
  • Agree the maintenance cadence now, while the database is clean, rather than after it drifts again.

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