Summary

1. CASE Functions in Data Studio
   1. Scenario 1: How do I re-create my Google Analytics “Custom Channel Groupings” in Google Data Studio?
2. REGEXP_EXTRACT and REGEXP_REPLACE Functions in Data Studio
   1. Scenario 1: How to extract a UTM parameter into a separate dimension with REGEXP_EXTRACT?
   2. Scenario 2: How to remove parameters from URLs with REGEXP_REPLACE?
3. CONCAT and IMAGE Functions in Data Studio
   1. Scenario 1: How do I use CONCAT to combine dimensions?
   2. Scenario 2: How do I use CONCAT with IMAGE to display images in a table?
4. DATE_DIFF Function
   1. Scenario 1: How do I calculate the difference between two dates using DATE_DIFF?
5. LOWER/UPPER Function
   1. Scenario 1: How to use LOWER and UPPER functions to standardize text strings?

Let’s dive into the world of functions!

1. CASE Functions in Data Studio

In a previous post, I wrote an introductory article with a lot of examples on how to use CASE statements in Google Data Studio, so if you are starting out, I would recommend you to look at that first. For this scenario, I want to focus on a more complex use of the CASE function.

Scenario 1: How do I re-create my Google Analytics “Custom Channel Groupings” in Google Data Studio?

In Google Analytics, users have the capability to create “Custom Channel Groupings” that allow them to sort how users have arrived at their website (eg. direct, organic search and etc). However, the Google Analytics native connector for Data Studio does not offer those groupings. Therefore, you have to create them yourself with a CASE, which is actually a positive, because Data Studio gives you much more flexibility with functions when compared to the “Custom Channel Groupings” in Google Analytics.

As usual, you would have to create a new calculated field at the data source-level. Since the “Custom Channel Groupings” are going to include an extensive list of “Source/Medium” attributes, your CASE might get a bit crowded. Nothing to worry about, I have already pre-written the code for you, but here are a few callouts:

• All CASE statements follow a sequential order when executing. For example, if your rule for “Paid Search” is generic, that might pick up the “Organic Search” data before the specific rule for “Organic Search” has had the chance to run.
• You can use OR and AND combinations within your CASE to make things more digestible. I highly recommend that.
• Always have a back-up rule! For example, you might have “Social Source Referral=Yes”, but if Google Analytics misclassifies your social channel – “l.facebook”- as “Referral”, you need to have that addressed with a regular expression.

CASE 
WHEN (Source="(direct)" AND Medium="(none)") OR (Source="(direct)" AND Medium="(not set)") THEN "Direct"
WHEN (Source="google" AND Medium="cpc") OR REGEXP_MATCH(Medium,"(cpc|ppc)") THEN "Paid Search" 
WHEN Medium="organic" OR REGEXP_MATCH(Source, ".*(^google|baidu|bing).*|.*(search).*") THEN "Organic Search" 
WHEN Social Source Referral="Yes" OR REGEXP_MATCH(Source,".*(social|fb|facebook|twitter|linkedin|pinterest).*") THEN "Social" 
WHEN REGEXP_MATCH(Source, ".*(mail|outlook).*") THEN "E-mail" 
WHEN REGEXP_MATCH(Source, ".*(youtube).*") THEN "Video YouTube"
WHEN Medium="affiliate" THEN "Affiliates" 
WHEN (Source="dfa" AND Medium="cpm") OR REGEXP_MATCH(Source,"(display|banner|native|.*(doubleclick).*)") THEN "Display (Campaign Manager)"
WHEN Medium="referral" THEN "Referral" 
ELSE "(Other)" 
END

2. REGEXP_EXTRACT and REGEXP_REPLACE Functions in Data Studio

Scenario 1: How to extract a UTM parameter into a separate dimension with REGEXP_EXTRACT?

If you have multiple campaigns and want to see how each one is contributing to your business, the REGEXP_EXTRACT function can be a real time-saver. Here is an example, the URL below includes a campaign name that we want to extract, as that part of the UTM is valuable to us.

https://datastudio.ca/?utm_source=dfa&utm_medium=cpm&utm_campaign=summer_sale

In this case, you would have to write the formula below. Firstly, selecting the dimensions that you want to extract from – “URL (with utm)”. After that, the regular expression simply says “starting from campaign= match everything until you reach another &“.

Once you apply the function, the result is pretty cool. We have extracted the campaign name in a separate and much cleaner dimension.

Scenario 2: How to remove parameters from URLs with REGEXP_REPLACE?

Building upon the previous function, let’s assume that you want to remove all parameters to get the original URL. This is often used when you want to only see the traffic to a landing page and ignore all parameters. In our case, you can write the formula below. Once again, the regular expression says “look for anything after ?“. Then, the third argument specifies what we want our replacement to be. We simply leave that blank.

The final table would look something like this and our URL is now much more visible.

3. CONCAT and IMAGE Functions in Data Studio

Scenario 1: How do I use CONCAT to combine dimensions?

This is a function that could prove to be extremely useful, especially if you are working with Campaign Manager (CM) or Google Ads. For many organizations, “naming conventions” are important, as they allow for easy data parsing. Well, with the CONCAT function, you can combine a few pre-existing fields into a single and more powerful dimension. Here is how it works:

Luckily, the function has a simple syntax. You type your first dimension, in this case, “Campaign Name” and then a delimiter. The delimiter specifies how dimensions will be separated from each other. A few common delimiters are “|”(pipe) and “,”(comma).

The result is a compact and more informative dimension that combines all of the three fields. Remember, you can add as many dimensions as you want, as long as the final field is useful to you.

Scenario 2: How do I use CONCAT with IMAGE to display images in a table?

A more advanced and exciting use of the CONCAT function is nesting it inside an IMAGE function. For this scenario, I have used the product data from the Google Analytics Demo Account. All we are doing here is, using “Product SKU” as a dynamic dimension since the product code is used in the URL. You can apply this same methodology with your eCommerece data set, as long as you have a dynamic element in the URL.

Once you have created the IMAGE calculated field, you can add it to a table. You will see the images showing-up immediately. A little caveat, the IMAGE dimension is only supported in tables, not other chart types.

4. DATE_DIFF Function

Scenario 1: How do I calculate the difference between two dates using DATE_DIFF?

This is a familiar one for those of you who use SQL on a regular basis. The function returns the difference between two date/time values in a numeric format. An interesting scenario where I found this function useful is paid media for eCommerce. We often look at the time a user was exposed to an ad and the time he/she actually made a purchase. This gives us a sense of what we would call “time-lag”, which indicates that the user needs some time to think before committing to a purchase. This is exactly where DATE_DIFF kicks-in.

The formula is straight-forward, you just have to include the two date fields that need to be subtracted.

5. LOWER/UPPER Function

Scenario 1: How to use LOWER and UPPER functions to standardize text strings?

Last but not least, this function allows you to standardize your text dimensions. For example, imagine you have an online form on your website with a free-form input field that asks “How did you hear about us?” and the input field is free-form. The user might input any type of capitalization, so to clean up the mess, we can use LOWER.

You can also use the UPPER function in a similar fashion, converting all provinces to the standard uppercase abbreviations.

I hope this was helpful and you can now use all of these Data Studio functions in your own projects! If you have any questions, feel free to DM me on LinkedIn ?

Dynamic Dashboards and Data Analysis with Google Data Studio – 2021

If you want to learn how to build powerful data visualizations and unlock insights that can help you drive business results for your clients or employers, take a look at my full course on Udemy.

The post Top 5 Most Useful Calculated Fields and Functions in Google Data Studio appeared first on Data Studio.

*NEW – By popular demand, I have decided to expand the comprehensive guide on CASE and added IF statements, as the Google Data Studio team has had some incredible updates over the past year. Enjoy!

What is the Data Studio CASE function?

The CASE function returns dimensions and metrics based on conditional expressions; it is often used to create new groupings of data and sort them in categories. Let’s imagine the following scenario. In the table below, you have a paid digital marketing campaign that includes “EN” and “FR” in the campaign name. However, you want to see the table below only per “Language”. This is where you can use the CASE function to write a specific statement.

The trick with the CASE function is that you often have to find a unique common denominator to parse the data correctly. In the statement below, I am opening the CASE with a WHEN, specifying the first condition. Then adding IN, which instructs Data Studio that I will be listing text strings. Finally, I go ahead and choose my campaign name(s) and specify with THEN what I want to name the result of my WHEN condition. I know…. this is a bit of a tongue twister.

The result is exactly what we were looking for. Now if we want to see the campaign simply filtered by the “Language” dimension, we can even exclude “Campaign”.

How to use REGEXP_MATCH function within CASE?

Now, what happens if things get more complicated. For example, in the “Complex Campaign” dimension, we have a lot of different and unique campaign iterations. If we were to use IN, then the list in the CASE function is going to grow substantially and we are more likely to make a mistake while writing it. This is why we are going to try out the REGEXP_MATCH function, also referred to as regex.

Quick background: Regular expressions are a notation for describing sets of character in a specific word or any text string. Google Data Studio (and most Google products) uses the RE2-style syntax.

We start the WHEN statement the same way, without much difference. However, the modified dimension i.e. “Campaign” is inside brackets, unlike IN where it is outside. All we are doing with the regular expression is telling Data Studio that every time the capital letters “EN” or “FR” are spotted in the “Complex Campaign” dimension, the text strings should be named “English” and “French”, respectively. In a little bit more detail:

How to write simplified CASE statements by declaring a field to test once?

In recent feature updates, Google has made it even easier to write repetitive CASE statements by declaring the field at the beginning. For example, if you have the same “Campaign” name, but this time, you want to use the year as a matching criteria.

Following the same logic as our past examples, you would normally write this standard CASE statement.

However, with the simplified CASE statement, you can do the following and save a bit of time!

How to create IF statements in Google Data Studio?

This has been a highly anticipated function and everyone was ecstatic when Google brought it to life. Similar to other platforms, you can use the IF function to return a dimension based on a given condition or rule. The basic syntax is as follow:

IF(condition, true_result, false_result)

Parameters

• Condition. This is the expression to evaluate. It consists of any valid boolean expression.
• True_result: This refers to the value that will be returned if the condition is true. It can be any valid expression.
• False_result: This refers to the value that will be returned if the condition is false. It can be any valid expression

Let’s look at an example. This simple condition will calculate if “Actual Sales” exceed the “Forecast Sales”. If they do, your new column will the “Bonus” by 1.4, which presumably is going to info you sales team. Then, bonuses will be distributed accordingly.

IF(Actual Sales > Forecast Sales, Bonus * 1.4, Bonus)

In addition, you can use a more complex condition with regular expression matching and logical AND. Although AND and OR allow you to test multiple conditions, simple CASE and CASE are the best to use if there are more than two possible results. Let’s see how that works.

IF (Event name = "purchase" AND (REGEXP_MATCH(Page path, ".*clothes.*") OR REGEXP_MATCH(Page path, ".*hoodies.*")), "hoodies Sales", Page title)

As you can see, the IF statement can be quite powerful.

Data Studio CASE examples from the Media Agency world:

These are few of the most common CASE statements across the Google Marketing Platform (GMP), ready to be copy/pasted!

Google Campaign Manager (CM) – Language

CASE
WHEN REGEXP_MATCH(Campaign, '.*(EN).*') THEN 'English'
WHEN REGEXP_MATCH(Campaign, '.*(FR).*') THEN 'French'
ELSE 'Other Language'
END

Display and Video 360 (DV360) – Language

CASE
WHEN REGEXP_MATCH(Insertion Order, '.*(EN).*') THEN 'English'
WHEN REGEXP_MATCH(Insertion Order, '.*(FR).*') THEN 'French'
ELSE 'Other Language'
END

Display and Video 360 (DV360) – Standard Media Tactics

CASE
WHEN REGEXP_MATCH(Line Item, '.*(Remarketing).*') THEN 'Remarketing'
WHEN REGEXP_MATCH(Line Item, '.*(Lookalikes).*') THEN 'Lookalikes'
WHEN REGEXP_MATCH(Line Item, '.*(Prospecting).*') THEN 'Prospecting'
ELSE 'Other Tactic'
END

Google Ads – Device Type

CASE
WHEN REGEXP_MATCH(Campaign, '.*((M|m)obile).*') THEN 'Mobile'
WHEN REGEXP_MATCH(Campaign, '.*((T|t)ablet).*') THEN 'Tablet'
WHEN REGEXP_MATCH(Campaign, '.*((C|c)omputer).*') THEN 'Desktop'
ELSE 'Other'
END

Google Ads – Age Breakdown (this cleans up your default age dimension)

CASE
WHEN Age IN ('Undetermined') THEN 'Unknown'
WHEN Age IN ('18to24') THEN 'A18-24'
WHEN Age IN ('25to34') THEN 'A25-34'
WHEN Age IN ('35to44') THEN 'A35-44'
WHEN Age IN ('45to54') THEN 'A45-54'
WHEN Age IN ('55to64') THEN 'A55-64'
WHEN Age IN ('gt64') THEN 'A65+'
ELSE 'N/A'
END

GA360 or Google Analytics (Free) – Blog “Page” Sorting

CASE
WHEN REGEXP_MATCH(Page, ‘((?i).*.*/about.*).*’) THEN ‘About Us
WHEN REGEXP_MATCH(Page, ‘((?i).*.*/blog/.*).*’) THEN ‘Blog Posts’’
WHEN REGEXP_MATCH(Page, ‘((?i).*.*/contact$).*’) THEN ‘Contact Us’
WHEN REGEXP_MATCH(Page, ‘((?i).*^/blog$).*’) THEN ‘Blog Page’
WHEN REGEXP_MATCH(Page, ‘((?i).*^/solutions/.*).*’) THEN ‘Solutions Pages’
WHEN REGEXP_MATCH(Page, ‘((?i).*^/services$).*’) THEN ‘Services’
WHEN REGEXP_MATCH(Page, ‘((?i).*.*/blog/category/.*).*’) THEN ‘Blog Category Page’
WHEN REGEXP_MATCH(Page, ‘((?i).*^/products/.*).*’) THEN ‘Product Pages’
WHEN REGEXP_MATCH(Page, ‘((?i).*^/contact/.*).*’) THEN ‘Thank You – Contact’
CASE WHEN REGEXP_MATCH(Page, ‘((?i).*^/$|^/\\?.*).*’) THEN ‘Homepage’
ELSE ‘_Other’
END

I hope this was helpful! If you have any questions, feel free to DM me on LinkedIn or subscribe to my newsletter for more updates

Dynamic Dashboards and Data Analysis with Google Data Studio

If you want to learn how to build powerful data visualizations and unlock insights that can help you drive business results for your clients or employers, take a look at my full course on Udemy.

The post How to use CASE statements in Google Data Studio? – 2021 appeared first on Data Studio.