Today's feature function is REGEXP_MATCH from Google Data Studio, a handy tool for parsing and analyzing text data based on patterns. This function harnesses the power of regular expressions, granting the users an efficient way to sift through text data, match, filter and categorize information like never before.
The structure of this function hews to the following prototype:
REGEXP_MATCH(X, regular_expression)Xregular_expressionThe output of this method is Boolean, that is, it returns 'True' if the text matches the regular expression, otherwise, 'False'.
Unlike rudimentary text matches, REGEXP_MATCH doesn't merely look for direct matches in the string, but matches based on the pattern set in the regular expression. This function attempts to match the entire string contained in the field_expression.
An interesting aspect of this function is it's affinity to RE2 expression syntax. If the regular_expression contains any escape characters, such as "\", it may require additional escaping in Google Data Studio.
In the realm of Sales Metrics, REGEXP_MATCH can be widely applied for segmenting customers, analyzing product preferences or understanding buying patterns. For example, imagine a field named Product_Purchased which holds alpha-numeric product IDs like "T123", "C250", "T498".
To classify products by type, say T-type and C-type, we can use:
CASE
WHEN REGEXP_MATCH(Product_Purchased, 'T.*') THEN "T-type"
WHEN REGEXP_MATCH(Product_Purchased, 'C.*') THEN "C-type"
ELSE "Other"
ENDAlthough highly potent, REGEXP_MATCH largely depends on the regular expression fed into it. Crafting efficient regular expressions demands practice and understanding. Additionally, when handling large datasets, regular expressions may have an impact on query performance and execution time. It may also return unexpected results if the syntax isn't accurate.
Embrace the power of REGEXP_MATCH and transform your data studio reports today!
