Looker Studio Function : REPLACE
REPLACE Function in Looker Studio – Simple Text Transformation Explained
Category: Text Function
The REPLACE function in Looker Studio is a simple yet very useful text function used to replace a specific portion of a text value with another value. Unlike REGEXP_REPLACE, this function does not use regular expressions and works with direct text matching, making it easier to understand and maintain.
In practical reporting scenarios, datasets often contain repeated words, unwanted prefixes, or incorrect naming formats. For example, replacing “_” with space in product names, correcting spelling variations, or removing fixed keywords from campaign names. REPLACE allows analysts to fix these issues directly inside Looker Studio without changing the source data.
From a BI point of view, REPLACE is ideal for quick text cleanup where patterns are predictable and consistent. It is commonly used in calculated dimensions to improve readability and consistency across dashboards. Since it works at row level and uses simple string matching, it performs efficiently even on large datasets.
Overall, the REPLACE function is best suited for straightforward text transformations where regex complexity is not required.
Purpose of REPLACE Function
1. Simple Text Cleanup
Replaces fixed words or characters to improve text readability and reporting consistency.
2. Naming Standardization
Helps standardize naming conventions across campaigns, products, or categories.
3. Quick Corrections
Fixes repeated or incorrect text values without modifying the original data source.
4. Reporting-Friendly Output
Creates clean and professional-looking text fields for dashboards and tables.
Type of Calculation & Practical Use Cases
1. Text Transformation
Transforms raw text values by replacing specific characters or words.
2. Calculated Dimensions
Commonly used in calculated dimensions to clean and standardize text data.
3. Dashboard Readability
Improves visual clarity by removing unwanted text patterns.
4. Lightweight Data Preparation
Acts as a simple data preparation step inside Looker Studio reports.
REPLACE(text, search_text, replacement_text)
REPLACE Function – Parameters Table
| Parameter | Type | Description |
|---|---|---|
| text | Text | Original text value to be modified |
| search_text | Text | Text to search for within the original value |
| replacement_text | Text | Text that replaces the matched value |
How Does the REPLACE Function Work?
The REPLACE function scans the original text and looks for exact matches of the specified search text. Whenever a match is found, it replaces it with the provided replacement text.
The function is case-sensitive, so “Sales” and “sales” are treated differently. If multiple matches exist, all matching values are replaced. If no match is found, the original text remains unchanged. The function processes each row independently, ensuring consistent results across datasets.
When Should You Use REPLACE Function?
When replacing fixed and predictable text values
When regex-based replacement is not required
When cleaning text for better dashboard readability
When standardizing naming conventions
When avoiding unnecessary regex complexity
Examples of REPLACE Function
Example 1: Replace Underscore with Space
REPLACE(Product_Name, "_", " ")
Example 2: Remove Fixed Keyword
REPLACE(Campaign_Name, "2025_", "")
Example 3: Correct Naming Format
REPLACE(Category, "Mobiles", "Mobile Phones")
Tips & Best Practices
Use REPLACE only for fixed text matching
Remember that REPLACE is case-sensitive
Prefer REGEXP_REPLACE for complex patterns
Test results in tables before final use
Keep calculated fields simple and readable
Yes, REPLACE is case-sensitive, so the search text must exactly match the text case for successful replacement.
REPLACE uses direct text matching, while REGEXP_REPLACE supports complex pattern-based replacements using regular expressions.
No, REPLACE only affects calculated field output inside Looker Studio and does not change the source data.
Yes, all matching occurrences of the search text within the original text are replaced automatically.
Yes, it is lightweight and performs well on large datasets when used for simple text replacement.