How to Clean Lead Lists Before Importing Them Into a CRM

The fastest way to pollute a CRM is to import a raw lead list. Badge scanners, purchased lists, and web forms all produce records with inconsistent casing, malformed emails, unformatted phones, and duplicates. Clean the file once, before import, and you avoid a cleanup project later.

What's wrong with a typical lead file

• Names in ALL CAPS or with stray whitespace from copy-paste.
• Emails with mixed case and trailing spaces that break exact-match lookups.
• Phone numbers in whatever format the person typed.
• The same person captured two or three times.

One call to repair the whole batch

Repair reconciles field names, normalizes values, and merges high-confidence duplicates in a single request, so you don't have to chain operations yourself.

{ "name": "MIKE  PERKINS ", "email": "Mike@Example.COM ", "phone": "(704) 555-1234" }

{ "name": "Mike Perkins", "email": "mike@example.com", "phone": "+17045551234" }

Preview before you commit

Set options.dry_run to true to see every change Repair would make without altering the returned dataset. Review the changes array, then run for real once you trust it.

curl -X POST https://zapinner.com/api/v1/repair \
  -H "Authorization: Bearer $ZAPINNER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{ "records": [ ... ], "options": { "dry_run": true } }'

Then import with confidence

Write the clean records to your CRM with your existing import tooling. Because the repair is deterministic and audited, you can reconcile exactly what changed if anything looks off after import.