Regex Made Easy in Make

If there’s one tool that has an unfair reputation in tech, it’s regex (regular expressions). For many, it feels like ancient wizardry: a strange jumble of symbols that only seasoned developers understand. But inside Make, regex can be a simple, practical helper, especially when you’re cleaning up messy data.

You don’t need to master every regex trick. A handful of patterns will cover most real-world use cases. In this post, I’ll show you how regex can save the day in Make scenarios, with copy-and-paste examples you can try straight away. And if you want a nice tester, head on over to regex101.

Why Regex Matters in Make

Make is brilliant at moving and transforming data between systems. But sometimes, that data isn’t tidy:

• Phone numbers come with brackets, dashes, or spaces.

• User inputs contain typos or unwanted characters.

• Email addresses sneak through without proper formatting.

Regex helps you extract, validate, and clean that data before it flows downstream. The bonus? It’s often faster and more reliable than trying to stitch together complex formulas.

Example 1: Extracting Phone Numbers from Messy Strings

Say you’re pulling form submissions, and the phone number field looks like this:

Call me on (021) 123-4567 or office: +64 9 234 5678

Regex to the rescue:

Pattern:

(\+?\d[\d\s-]{7,}\d)

What it does:

• \+? allows an optional “+” for international numbers.

• \d looks for digits.

• [\d\s-]{7,} captures digits, spaces, or dashes, ensuring it’s at least 7 characters long.

Result:From the example above, Make will extract:

• 021 123-4567

• +64 9 234 5678

Perfect for cleaning up before saving into a CRM.

Example 2: Validating Email Addresses

You don’t want junk emails like test@@example clogging up your workflows. Use regex to validate before committing them to your database.

Pattern:

^[\w\.-]+@[\w\.-]+\.\w{2,}$

What it does:

• ^[\w\.-]+ → checks the start for one or more word characters, dots, or hyphens.

• @ → enforces the @ symbol.

• [\w\.-]+ → looks for a valid domain.

• \.\w{2,}$ → checks for a dot followed by at least 2 letters (like .com or .co.nz).

Examples that match (valid):

• alex.smith@example.com

• john-doe@my-company.org

• test_123@service.co.nz

Examples that fail (invalid):

• alex@@example (two @ symbols)

• jane@ (nothing after @)

• hello@example. (dot but no suffix)

• user@domain.c (suffix too short, only one letter)

Example 3: Replacing Unwanted Characters from Inputs

Sometimes people paste all sorts of things into text boxes—extra spaces, emojis, or even odd punctuation. Regex can strip these out quickly.

Pattern to keep only letters, numbers, and spaces:

[^A-Za-z0-9\s]

How to use: Apply this in Make with a replace function, swapping the unwanted characters with an empty string.

Result:

• Input: Hello!!! This ☀️ is messy text...

• Output: Hello This is messy text

Handy for creating clean database entries or filenames.

Copy and Paste Regex Cheatsheet

Here’s a quick cheatsheet to bookmark for your Make scenarios:

• Phone numbers: (\+?\d[\d\s-]{7,}\d)

• Emails: ^[\w\.-]+@[\w\.-]+\.\w{2,}$

• Keep only alphanumeric + spaces: [^A-Za-z0-9\s]

• Extract digits only: \d+

• Trim whitespace: ^\s+|\s+$

Final Thoughts

Regex doesn’t have to be intimidating. Think of it as a pocketknife, small but powerful. With just a few patterns, you can save hours of frustration cleaning up data in Make scenarios.

Next time you’re about to write a messy chain of functions, try reaching for regex instead. You might be surprised how simple it can be.