Dimensions and metrics in Google Data Studio

A dimension is the attribute of visitors to your website. It is used to describe or categorize your data.

For example, let’s say a man aged between 25-34 from London visited your website after clicking on an organic search listing on Google which he found by searching for the keyword ‘attribution modeling’.

Let us also assume that he visited your website via a chrome browser which is installed on a desktop computer that runs windows.

Now following are the attributes of the visitor to your website along with their values:

Gender – male

Age – 25-34

City – London

Source / Medium – Google / Organic

Keyword – Attribution Modelling

Browser – Chrome

Device Category – desktop

Operating System – Windows

Here Gender, Age, City, Source /Medium, Keyword, Browser, Device Category and Operating System are all examples of dimensions because they are the characteristics of your website users.

Dimension in your data source appears as green fields.

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Types of dimensions in data studio

There are three types of dimensions in data studio:

Regular dimension – It is the attribute of visitors to your website. It is used to describe or categorize your data.
Data source specific calculated dimension – It is a dimension that performs some action(s) on other field(s) in your data source via a formula.
Chart specific calculated dimension – It is a dimension that performs some action(s) on other field(s) in your chart via a formula.

All calculated dimensions appear in the data source with an ‘fx’ symbol:

Metrics in Google Data Studio

A metric is a number that is used to measure one of the characteristics of a dimension.

A dimension can have one or more characteristics.

For example, the following are the characteristics of the dimension called ‘Source / Medium’:

Sessions
% New Sessions
New Users
Bounce Rate
Pages / Sessions
Avg. Session Duration
Goal Conversion Rate
Goal Completions
Goal Value

Here, Sessions, % New Sessions, New Users, Bounce Rate, Pages / Sessions, etc are all examples of metrics because they are the characteristics of the dimension called ‘Source / Medium’

Metrics in your data source appear as blue fields:

Types of metrics in data studio

There are three types of metrics in data studio:

Regular metric – It is a number that is used to measure one of the characteristics of a dimension.
Data source specific calculated metric – It is a metric that performs some action(s) on other field(s) in your data source via a formula.
Chart specific calculated metric – It is a metric that performs some action(s) on other field(s) in your chart via a formula.

All calculated metrics appear in the data source with an ‘fx’ symbol:

Difference between dimensions & metrics in Google Analytics and Google Data Studio

In Google Analytics, a dimension can not be used as a metric and vice versa. Whereas, in the Google data studio, a dimension can be used as a metric and vice versa.

For example in the data table below, ‘age’ (which is defined as a dimension in GA) is used as a metric:

And you can see how data studio is reporting the values of the ‘age’ field in the data table.

But when you use ‘age’ as a dimension, you see the correct data in the data table:

Similarly, in Google Analytics, certain dimensions and metrics can not be used/queried together.

However, this is not the case with Google data studio.

For example, in Google Analytics you can not use/query ‘user type’ dimension with Adwords ‘impressions’ metric. In data studio you can do that but you are going to see ‘User Configuration Error’:

So when you are using Google Analytics dimensions and metrics in Google data studio, you need to be extra careful.

Do you know the difference between Web Analytics and Google Analytics?

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Maths and Stats for Web Analytics and Conversion Optimization

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Attribution Modelling in Google Analytics and Beyond

Attribution Modelling in Google Ads and Facebook

Maths and Stats for Web Analytics and Conversion Optimization
This expert guide will teach you how to leverage the knowledge of maths and statistics in order to accurately interpret data and take actions, which can quickly improve the bottom-line of your online business.

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Himanshu Sharma

Digital Marketing Consultant and Founder of Optimizesmart.com

Himanshu helps business owners and marketing professionals in generating more sales and ROI by fixing their website tracking issues, helping them understand their true customers purchase journey and helping them determine the most effective marketing channels for investment.

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The Digital Analytics Association is a world renowned not-for-profit association which helps organisations overcome the challenges of data acquisition and application.

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