Phone Number Formatting: International Standards & Best Practices (2026)
Phone numbers in CSV come in every shape: with or without country code, spaces, dashes, parentheses. This guide explains the E.164 standard, lists common country codes, and shows how to validate and normalize phone numbers for CRM, SMS, and international dialing—including regex examples you can reuse.
Table of Contents
Inconsistent phone formats cause duplicate contacts, failed lookups, and errors when feeding data into CRMs or SMS gateways. A single international standard—E.164—lets you store one format that works everywhere. This guide shows how to get from messy CSV columns to clean, dialable numbers.
1. Why Standardize Phone Numbers in CSV?
The same number can appear as +49 30 12345678, 0049 30 12345678, (030) 1234-5678. Without a single format, deduplication fails, integrations reject values, and dialers may mis-dial. Standardizing to E.164 gives you one canonical form for storage and display variants when needed. For general normalization of CSV columns, see Data Normalization 101.
2. The E.164 Standard
E.164 is the ITU-T standard for international telephone numbers. Format: a + followed only by digits; no spaces, dashes, or parentheses. Country code first (e.g. 49 for Germany, 1 for US/Canada), then national number. Maximum 15 digits. Example: +493012345678 (Berlin).
Before (mixed) → After (E.164):
030 12345678 → +493012345678
+1 (555) 123-4567 → +15551234567
0044 20 7946 0958 → +4420794609583. Common Country Codes
When normalizing, you often need to add or recognize country codes. Here are frequently used ones:
| Country | Code | E.164 example |
|---|---|---|
| USA / Canada | +1 | +15551234567 |
| UK | +44 | +442079460958 |
| Germany | +49 | +493012345678 |
| France | +33 | +33123456789 |
| Spain | +34 | +34612345678 |
| Australia | +61 | +61212345678 |
| Switzerland | +41 | +41221234567 |
| Austria | +43 | +43123456789 |
Full lists are published by the ITU; for CSV you typically maintain a small mapping (e.g. country name or 2-letter ISO → E.164 prefix) to add missing codes when normalizing.
4. How to Normalize to E.164
Steps: (1) Strip everything except digits and a leading +. (2) If the number starts with 0 (e.g. 030…), treat as national and prepend country code. (3) If it has no country code but you know the country (e.g. from another column), prepend the code. (4) Add + in front. Libraries like libphonenumber (Java/JS/Python ports) handle parsing and formatting for you; for simple CSV cleanup you can combine whitespace and character cleanup with a script or tool that applies these rules.
5. Regex for Validation and Cleaning
To strip non-digits (before adding + and country code): replace(/\D/g, ''). To check a string that should already be E.164: /^\+[1-9]\d{8,14}$/ — starts with +, then 1–9, then 8–14 more digits (E.164 max 15 total). That regex rejects leading zeros and too short/long numbers.
JavaScript example – strip to digits then format:
const digits = raw.replace(/\D/g, '');
const e164 = digits.length >= 10 ? '+' + digits : '';Clean CSV Before Phone Normalization
Remove extra spaces and odd characters from phone columns with our CSV cleaner. Then use your script or a phone library to convert to E.164.
Clean CSV6. Phone Numbers in Your CSV Workflow
Best practice: (1) Trim whitespace and remove obvious junk (letters, multiple plus signs) with a CSV cleaning step. (2) Normalize to E.164 in a dedicated column so you keep the original if needed. (3) Validate with a regex or library before import into CRM or SMS. (4) For international lists, keep a country column so you can apply the right code when it’s missing. That keeps duplicates down and ensures dialability—see also Data Normalization 101 and Remove Whitespace from CSV for the rest of your cleanup pipeline.