Cleaning phone numbers before a CSV import: the checklist
Bad formatting, duplicates and test data are the most common reasons a clean-looking list performs badly. A practical checklist to run before you import.
·5 min read
A contact list that looks tidy in a spreadsheet can still contain problems that cost money once it is imported: numbers in inconsistent formats, duplicates that get texted twice, names in the wrong column, and test rows nobody removed.
Spending a few minutes on cleanup before an import is one of the highest-return habits in texting. This checklist covers what to look for.
Standardize phone formats
Numbers arrive as (555) 123-4567, 555.123.4567, 5551234567, +1 555 123 4567, and every combination in between. Pick one format, ideally digits only or the international form with a plus and country code, and apply it across the column. Text2Sale's CSV import maps your columns and cleans common formatting, but starting from consistent data makes problems easier to spot.
Remove duplicates
The same person often appears more than once, especially when lists come from several sources. Duplicates cause double texts, which annoy people and inflate cost. Sort by the phone column and remove repeats, keeping the row with the most complete information.
Check for junk and test rows
Scan for obviously invalid entries: numbers that are too short or too long, repeated digits like 5555555555, area codes that do not exist, and placeholder rows such as test, n/a, or none. Delete them rather than importing noise.
- ✓Numbers not ten digits (for US numbers)
- ✓Repeated or sequential digits
- ✓Placeholder names and test rows
- ✓Blank phone fields
Fix the name columns
First names are used in messages, so check that the first-name column contains first names, not full names or company names, and that capitalization is sensible. A message that says "Hi SMITH, JOHN" is worse than one with no name. Fill gaps or send those contacts a version of the message that does not use the name.
Check consent, tag the source, and import opt-outs first
Only import people who agreed to receive texts from you. Add a source column so you can later trace where each contact came from, and import your existing opt-out list before the main list so those numbers are never messaged. Keep the original file untouched and save a cleaned copy, so you can always go back.
A worked example
Suppose you buy a list of 5,000 contacts. After removing duplicate numbers you are down to 4,600. Deleting invalid and junk rows brings it to 4,450, and removing people on your opt-out list leaves 4,380. You have cut 620 rows, about 12 percent of the file.
At $0.015 per segment, that saves roughly $9 on every one-segment message you send to the list. The money matters less than the other effects: fewer duplicate texts, fewer complaints, and delivery numbers that reflect real people.
Key takeaways
- →Standardize phone formatting before importing.
- →Remove duplicates so nobody is texted twice.
- →Delete junk numbers and test rows.
- →Make sure first-name columns hold usable first names.
- →Import opt-outs first, tag the source, and keep the original file.
Put this into practice with Text2Sale
Import leads, create follow-up campaigns, organize replies, and connect texting, calling, and appointments in one workspace.
Frequently asked questions
What format should phone numbers be in?
Any consistent format works. Digits only, or the international format with a plus and country code, are the easiest to check.
Does Text2Sale clean phone numbers on import?
The CSV import maps your columns and cleans common formatting, but cleaning the file first makes problems easier to catch.
Why import opt-outs first?
So any contact who previously unsubscribed is flagged before the main list is loaded and never receives a message.