See How Marketing Agencies Use Looker Studio Regex to Save Time

You’ve likely lost hours wrestling with messy campaign names or UTM codes inside Looker Studio. Regex can end that struggle — automating clean‑up, grouping, and insight‑generation in seconds. As a marketing team veteran, you’ll learn how regex transforms drudgery into speed and clarity — with zero advanced math or coding skills required.

Many marketers don’t realize regex can automate the tedious manual work and save countless hours. We’ll show you how it works in Looker Studio — no math degree required.

Why Marketing Agencies Use Looker Studio Regex

Marketing agencies juggle campaign data from dozens of platforms, clients, and tracking systems — and let’s be real — it’s almost never clean. You’ll spot:

• Typos in campaign names
• Inconsistent UTM formats
• Shifting naming standards over time

That’s where regular expressions — regex — change the game.

Simple filters in Looker Studio are rigid. They only work when the label matches exactly — letter for letter, space for space.

But regex? It speaks in patterns.

Say you want to group all Facebook-related campaigns. A basic filter can’t handle the mess. But a single regex pattern can catch:

• FB_ads
• fb-ads
• facebook_ads
• Facebook Ads
• Even lowercase one-offs like fb or fcb

All in one swoop. No more guessing what someone typed in the UTM tag that day.

• Group campaigns by traffic source
• Clean up inconsistent labels
• Filter out bot or test data
• Segment performance automatically

No manual updates needed when naming conventions change.

Example scenario: A digital marketing agency had 12 variations of Facebook campaign labels across its clients. Using REGEXP_CONTAINS to group all of them into a unified “Facebook” bucket, they reduced manual filter maintenance by 90% and cut reporting preparation time from 2 hours per client to under 10 minutes. Overall, regex automation trimmed 25 agency-hours per month on average — adding up to 300+ hours a year for this small agency.

What You Need to Know Before Using Regex

Don’t worry — this isn’t going to be coding school. You just need to know your way around the basics of Looker Studio. If you know how to create Looker Studio calculated fields for marketing agencies and set up filters, you’re off to a strong start.

It also helps if you’re familiar with campaign tracking tools, like UTM parameters (utm_source, utm_medium, utm_campaign) and how your team typically names campaigns.

How to Use Regex Functions in Looker Studio

While regex requires some analytical knowledge, inserting them into your Looker report is as easy as 1-2-3. For starters:

1. Navigate to your Looker Studio report.
2. Click on Resource in the top menu, then select Manage added data sources.
3. Choose the relevant data source and click Edit.
4. Within the data source editor, click on + ADD A FIELD.
5. Then click on Add calculated field.
6. Give a clear name to your new field, for example, Branded keywords.
7. In the formula editor, write down your regex function.

Main Regex Functions in Looker Studio

Looker Studio has a handful of regex functions that each handle different tasks. Some check if patterns exist, others extract specific pieces of text, and some replace text based on patterns. But don’t worry about memorizing them all upfront — most marketers end up using just one or two regularly.

Here’s a table that breaks down what each function does and when you’d actually use it in your reports.

If your text patterns are simple and consistent, choose these functions. They run faster and keep your reports simpler.

Regex vs. Other Looker Studio Functions

Looker Studio has tons of ways to work with text data, and honestly, regex isn’t always your best bet. While it’s super flexible and can handle pretty much any pattern you throw at it, sometimes you might just overload your reports with complex calculations.

If you can get the job done with something basic like CONTAINS or STARTS_WITH, your reports will run faster, and you won’t have to overthink it.

When to Use Regex

Regex is powerful in handling data that doesn’t play with the basic Looker Studio text functions. And there are quite a few cases where regex should be your go-to choice when building a report:

• When names are all over the place. For example, your Google Ads campaigns are tagged as g_ads, google_ads_campaign, or google_campaign_123. Regex can then catch them all without you having to create separate filters for each variation.
• When you don’t want to clean up manually. Instead of manually fixing inconsistent UTM values or campaign labels one by one, a regex can standardize everything automatically.
• When you want to group things that belong together. Maybe you want to see all your social campaigns in one bucket, even though they’re scattered across fb, x, instagram, and social_media tags.

Example: Your client’s Facebook campaigns include labels like facebook₁_retarget, FB-retarget_aug, fb‑prospect7. Set REGEXP_CONTAINS(Campaign, "(?i)fb(ook)?[-_]?prospect|retarget") and capture all these variations. Without this, you’d manually create and manage 5 to 7 filters every reporting period.

So, when the basic text functions start feeling like you’re playing the guessing game with data variations, that’s when regex steps in to save the time.

When to Use Simple Text Functions

Use simple text functions when patterns are clear and consistent, like:

• When the pattern is super obvious, for example, if all your specific client’s campaigns start with client_123, just use STARTS_WITH and you’re good to go.
• When you know exactly what you’re looking for. To view all your Google traffic, simply filter for utm_source containing google, and the widget will only display data associated with the GA4 data source.
• When you want your reports to load quickly. Basic functions don’t make Looker Studio work as hard, which means your dashboards load faster and you’re less likely to run into various errors.

Simple Regex Expressions for Marketing Agencies

Before moving to some advanced calculations, you can explore the simplest regex functions that help you spot data connectors’ errors and possible reporting issues.

How to Improve Your Client Reporting with Looker’s Regex

Marketing agencies signing up with new clients face a classic headache — messy data. Every client has their own naming conventions, some campaigns have incomplete UTM parameters, and creating consistent reports across multiple accounts is a task that requires hours of an Account Manager’s time. Here’s how to fix the mess and save time with some smart regex functions.

Start with Data Audit

Start by figuring out what you’re working with. This simple regex will tell you if your client has any naming structure at all:

REGEXP_EXTRACT(Campaign, "([A-Za-z]+)_?([A-Za-z]+)?_?([0-9]{4})?")

This function will look for three things:

• A campaign type (letters)
• An audience (optional letters)
• A year (optional 4-digit number)

After running this through the client’s data sources, the formula will capture stuff like Spring_sale_2024 or Email_campaign_2025. Run this across all their campaign names and you’ll quickly spot inconsistencies that need cleanup.

Data Standardization with Regex

Once you audit campaign naming patterns, build calculated fields that automatically standardize inconsistent labels. Set up rules at the report level to flag any new campaign names that don’t match your agreed naming conventions.

Example: You work with clients who all use different terms for brand campaigns like Brand_awar_2024, Awareness_br_03, or BrndPromo2025. One reusable CASE + REGEXP_CONTAINS formula captures all these variations and assigns them to a unified “Brand” category — reducing maintenance and manual updates across clients.

For this, you’ll need to build a few regex patterns for marketing data you can reuse for similar clients — no need to reinvent the wheel every time.

How to categorize campaign types with regex:

1. Click + ADD A FIELD to create a new calculated field, name it “Campaign type”
2. Enter this formula (adjust Campaign field as needed):

CASE

  WHEN REGEXP_CONTAINS(Campaign, "(?i)(brand|trademark)") THEN "Brand"

  WHEN REGEXP_CONTAINS(Campaign, "(?i)(competitor|conquest)") THEN "Competitor"

  WHEN REGEXP_CONTAINS(Campaign, "(?i)(retarget|remarketing)") THEN "Retargeting"

  WHEN REGEXP_CONTAINS(Campaign, "(?i)(prospecting|acquisition)") THEN "Acquisition"

  ELSE "Other"

END

3. Save the field and add it to your report or table widget.
4. The result: campaigns named Brand_awar_2024 or Awareness_br_03 both get assigned “Brand” automatically.

This works regardless of what your clients actually named their campaigns. A campaign called Brand_awar_2024 and another called Awareness_br_03 both get categorized as Brand campaigns.

Casual UTM Checks

When UTM parameters go missing, your traffic turns into a mystery. You don’t know what’s working. You don’t know where your best leads came from.

This regex flags it fast. It scans every campaign and calls out what’s wrong — blank source, missing medium, or a typo like Googlads instead of google-ads.

Run it across the board, and you’ll get a clear list of campaigns that need fixing — no detective work required.

Better yet, automate it. That way, you catch issues the moment they happen, not three months later when the client’s asking why last quarter’s attribution looks like spaghetti.

Set up automated UTM audits in Looker Studio:

1. Create a calculated field named “UTM status”
2. Use this formula: 

CASE  

  WHEN utm_source IS NULL OR utm_source = "" THEN "Missing source" 
  WHEN utm_medium IS NULL OR utm_medium = "" THEN "Missing medium"  

  WHEN utm_campaign IS NULL OR utm_campaign = "" THEN "Missing campaign"  

  WHEN NOT REGEXP_CONTAINS(utm_source, "^[a-z0-9_-]+$") THEN "Invalid source format"  

  ELSE "Complete"  

END

3. Add this field to your report to instantly see which campaigns have missing or invalid UTM data
4. Set up alerts or scheduled report views to catch errors the moment they happen — before clients spot inconsistencies later.

How to Optimize Regex Functions in Looker Studio

Regex in Looker Studio can unlock some handy features for your marketing agency, but there’s a catch — use it wrong and your reports will slow down. Here’s how to get all the benefits without facing the eternal loading screen.

Be Mindful About Calculations

The location of your regex calculations makes a huge difference in performance:

Choose where to calculate regex for best performance:

1. Use data‑source level fields for calculations you reuse across reports (e.g. campaign grouping by type or source).
2. Use chart‑level fields for one‑off visual needs or data‑backed client digs.
3. For heavy regex logic, use Extract Data connector to pre‑compute everything and avoid slow dashboards.

Keep Your Patterns Simple

Say you’re trying to categorize campaigns for a client who runs both Google Ads and Facebook campaigns. Don’t start by trying to catch every possible variation right away. Begin with something basic like:

• REGEXP_CONTAINS(Campaign, "(?i)search") for search campaigns.
• REGEXP_CONTAINS(Campaign, "(?i)display") for display campaigns.

Then, you can add another layer of complexity. Maybe your client also uses abbreviations like SEM or PPC for paid campaigns. Add those one at a time:

• First add (?i)(search|sem).
• Then, include (?i)(search|sem|ppc).
• Finally, wrap everything in one regex, like (?i)(search|sem|ppc|paid_search).

This way, if something breaks in your client report, you’ll know exactly which addition caused it.

Cache Data with Extract Data Connector

Regex is powerful — but slow.

Instead of running those heavy calculations live every time someone opens a report, use the Extract connector. It pre-processes everything in the background.

That’s a lifesaver when you’re juggling 800 campaigns, 15,000 ad groups, and over 100,000 keywords across dozens of accounts.

Need to sort campaigns by goal, location, or product type using regex? Run it once with Extract. The result: clean, snappy dashboards that load in seconds — not a full coffee break later.

To connect the Extract Data connector with regex calculations in Looker Studio:

1. Open your Looker Studio report
2. In the top menu, click Resource
3. Then select Manage added data sources
4. In the window, click on + ADD A DATA SOURCE at the bottom of the list
5. Search for the Extract Data connector from the available options
6. After connecting, pick the main data source that contains your campaign data, for example, Google Ads, GA4, or BigQuery
7. Select all the original fields you’ll need for regex logic, for example, campaign name and location
8. After selecting all needed fields, save the connector and wait for the report to refresh
9. Then, head back to the connector and click on + ADD A FIELD and select Add calculated field
10. Tune the connector with regex-based calculated fields, like:

CASE  

  WHEN REGEXP_CONTAINS(Base Campaign Name, "(?i)search") THEN "Search"  

  WHEN REGEXP_CONTAINS(Base Campaign Name, "(?i)display") THEN "Display"  

  ELSE "Uncategorized"  

END

11. Click Save — now the regex logic runs once, and your reports load fast every time.

How to Introduce Regex to Your Clients

Regex is great for cleaning up messy campaign data — but say the word “regex” to a client, and their eyes might glaze over. They don’t care about pattern matching. They care about clean, reliable reports.

So don’t explain the tool. Explain the result.

During onboarding, mention that you use automation to catch errors and keep reports accurate, no matter how complex the data gets. It’s part of your quality process — baked in, not bolted on.

This way, clients know from the start that clean data is part of the package.

Show Clients the Business Value of Regex — Without the Jargon

Regex sounds technical — and it is. But your clients don’t care about pattern matching. They care about results.

So show them what regex does, not how it works.

Start with business outcomes:

• Messy campaign names? Regex groups them automatically by platform, product line, or funnel stage.
• Manually tagging campaigns? That’s gone. Regex cuts hours of grunt work per week.
• Confusing reports? Regex sorts and cleans the data, so insights surface fast — and stakeholders stay happy.

Show What Clean Data Looks Like

Before-and-after examples drive it home. Compare this:

With this:

Be Transparent — Especially With Enterprise Clients

Bigger companies want to know what’s happening under the hood. Make it simple for them to trust your process.

• Add a short explainer in your report. Something like:
  “We automatically categorize campaigns using naming patterns to keep your reporting consistent and accurate.”
  
• Create a glossary of calculated fields. Use plain terms like:
  “Campaign type = identifies campaigns as Search, Display, or Video based on keywords.” No regex code. Just outcomes.
  
• For enterprise clients, map the data flow.
  A simple chart showing how data moves — from source to transformation to report — can satisfy compliance teams and show you know your stuff.
  
• Use their campaigns to explain your logic.
  Don’t use placeholder data. Grab real campaign names from their account, then show how they get auto-labeled and cleaned up. It makes the automation feel custom — because it is.

Troubleshooting Looker Studio Regex

Regex is powerful — but unforgiving. One small typo in your calculated field can cause filters to break, campaigns to vanish, or trigger the dreaded “An error has occurred while loading the results.”

Here’s how to troubleshoot the most common regex issues marketing agencies face when cleaning up UTM tags or campaign names in Looker Studio.

When Filters Don’t Work as Expected

The most common issue? You’ve stacked multiple regex filters, and they clash. Campaigns disappear, duplicates pop up, or your categories get messy and confusing.

The fix: ditch the filter stacking. Instead, combine everything into a single REGEXP_CONTAINS statement using OR logic.

For example, instead of this:

REGEXP_CONTAINS(Campaign, "search")

REGEXP_CONTAINS(Campaign, "display")

Use this:

REGEXP_CONTAINS(Campaign, "(?i)(search|display|video|shopping)")

This single filter catches all your campaign types in one clean sweep — no more conflicts, no more missing data.

REGEXP_EXTRACT Returns NULL

If your REGEXP_EXTRACT function returns nothing — leaving blank fields — it usually means your pattern doesn’t match the actual data format.

First, test your regex on real campaign names. Add (?i) to make the match case-insensitive. Also, double-check your pattern works with Looker Studio’s RE2 regex engine.

For example, if this returns nothing:

REGEXP_EXTRACT(Campaign, "(Brand)")

Change it to this to ignore case differences and catch similar terms:

REGEXP_EXTRACT(Campaign, "(?i)(brand|generic|competitor)")

Unexpected matches due to special characters or case?

Complex REGEXP_REPLACE calculations can slow down your reports — especially if you chain multiple replacements. To speed things up, use capture groups and simplify your logic.

For example, instead of this:

REGEXP_REPLACE(REGEXP_REPLACE(text, "facebook", "fb"), "google", "g")

Use this cleaner approach:

REGEXP_REPLACE(text, "(facebook|google)",

  CASE

    WHEN $1 = "facebook" THEN "fb"

    WHEN $1 = "google" THEN "g"

  END)

This way, your regex does one pass and runs faster.

Fields Returning NULL Across Your Report?

Wrap your regex expressions inside a CASE statement with a fallback value like “Uncategorized”. This prevents blank or missing fields from showing up in your reports

Track Pattern Misfires:

Create a summary table that counts how many results return “Uncategorized”. If that number climbs, it’s your signal to review and update your regex patterns.

Handle Case Sensitivity With (?i)

Case differences often cause regex to miss matches. To ignore case, always add (?i) at the start of your pattern — especially when UTM parameters or campaign names use mixed capitalization.

For example, if your regex is:

REGEXP_CONTAINS(Campaign, "YouTube")

Change it to:

REGEXP_CONTAINS(Campaign, "(?i)youtube")

This ensures your pattern matches “YouTube”, “youtube”, “YOUTUBE”, or any case variation.

Handle Regex Errors

When regex breaks in Looker Studio, you might not see a big red error. Instead, your charts could show no data, calculated fields might return NULL, or filters stop working altogether.

To avoid this, always wrap your REGEXP_CONTAINS or REGEXP_EXTRACT functions inside a CASE statement with a fallback value like “Uncategorized”. This way, your fields always return something meaningful — even when the data doesn’t match your patterns.

Regex patterns can also break over time because clients change how they name campaigns. So, regularly check your regex results. Set up alerts or summary tables that track how many “Uncategorized” entries you’re seeing. If that number climbs, it’s a clear sign it’s time to update your regex and tidy up your reports before they get messy.

Test Your Regex Formulas

Writing regex is only half the battle. The real challenge is making sure your patterns work with actual client data. Before you add any regex formulas to client reports, test them in the right environment.

Looker Studio uses the RE2 regex engine, which behaves a bit differently from others. To avoid surprises:

1. Go to regex101.com.
2. In the top-left flavor selector, pick Google RE2.
3. Paste your regex pattern there.
4. Add real campaign names from at least 3 to 5 different client accounts.
5. Check for spelling quirks, case differences, and formatting variations.
   

Tweak your regex here until it reliably matches your data—then copy it into Looker Studio. This simple step saves hours of troubleshooting later.

Advanced Looker Studio Regex Techniques

Once you’ve mastered the basics of regex, it’s time to level up. Advanced formulas can save you hours of manual cleanup and data entry in your client reports. Below are some common regex patterns marketers rely on to automate tedious tasks and keep data neat.

Automate Like You Mean It

Regex in Looker Studio is a powerful ally for marketing agencies drowning in messy campaign data. Automate UTM clean‑up, campaign categorization, and naming consistency — cutting routine manual work from agency workflows.

Start small: audit your current naming, build reusable regex formulas, track “Uncategorized” outputs, and use an Extract Data connector to keep dashboards fast. Solve one data chaos problem first and expand automation as you gain confidence.

If you’re looking to simplify your reporting workflow without sacrificing clarity or customization, Swydo offers a 14-day free trial — no strings attached. It’s a good way to see if it fits your agency’s rhythm.