How to Access Custom Dimensions in Google Analytics

  • Jul 19, 2018

  • by Saurabh Kumar

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How to Access Custom Dimensions in Google Analytics

Introduction to dimensions and metrics

Google Analytics shows two types of data in its reports. These types of data are called dimensions and metrics. A dimension is a trait of the visitors on the website. Such traits include age, city, gender, keyword, source, browser, operating system, and devise category. A metric is a number which measures the characteristics of a dimension. If you take, source, the characteristics would be the number of sessions, new users, bounce rate, new sessions, etc. These are the metrics which you would find a Google Analytics report.  

Custom dimensions and metrics in Google Analytics are the same as the default dimensions and metrics in an Analytics account. The only difference is that you create custom dimensions and metrics yourself. This option lets you collect and analyse data that Analytics doesn’t track automatically. With this option, you can combine Analytics data with non-Analytics data. This can be explained better with the below-mentioned points –

-For game developers, metrics like ‘high score’ or ‘task completion’ might be more relevant than other default settings like ‘screen views’. Case in point here is the custom metric, using which you can effectively track your progress against other relevant metrics in a seamless manner and easy-to-read custom reports.

-You can use custom dimensions as ‘segments’ in standard reports.

-Region, gender, traffic source, etc. are some of the predefined dimensions and bounce rate, pageview, etc. are some of the predefined metrics.

Differences between dimensions and metrics in Google Analytics

Difference 1: While dimensions and metrics define the characteristics of your website visitors, they differ in the way they are configured, processed, collected, and reported in Google Analytics. For instance, you cannot use metric as a dimension and/or vice versa in Google Analytics via API.

Difference 2: A dimension provides context to a metric in Google Analytics. Therefore, a standalone metric renders no meaning to a report in Google Analytics. Therefore, the metric session will make sense only when it is used with dimensions like ‘country’, ‘user type’, etc.

Difference 3: Metrics are reported under three categories – behaviour metrics, acquisition metrics, and conversion metrics – unlike dimensions.

Predefined and custom dimensions

Predefined dimensions are dimensions which can found in a Google Analytics report by default. They are also ready to use and do not need any customization. On the other hand, custom dimensions are defined by users as per their requirements. Custom dimensions are needed when you need to measure the characteristics of a user which cannot be done by any predefined or pre-existing dimensions. In such a case, you need to create and use your own dimension to get the result you want.

For example, you can produce a custom dimension to find and collect keywords which stemmed from a phone call on your website. Likewise, you can also create a custom dimension for logged in users, logged out users, website usage data by authors, etc. Thus, with the help of custom dimensions, you can collect the data which Google Analytics does not collect by itself.

Lifecycle of custom dimensions

The lifecycle of custom dimensions has four stages as explained below –

  • Configuration: In this stage, you can define your custom dimension with an index, and properties such as scope, a name, etc. Analytics assign an index number to the defined name, which can be later used as a reference.
  • Collection: Custom dimensions are sent to Analytics at the time of collection as a pair of value & index parameters. This index parameter corresponds to the index number assigned by Google Analytics in the configuration stage.
  • Processing: At this stage, the level of scope determines how a particular custom dimension value will be applied, while the filters find out which associated values will be processed underreporting.
  • Reporting: After all the three stages in the pipeline are completed, custom dimensions are made available through the API or the user reporting interface.

Custom Dimension Google Analytics Free Vs Paid (360)

Google has stressed that their enterprise analytics are designed to assist e-commerce sites in order to create better customer experiences. This simply means that you need to get into granular details before making a switch to the paid version of Google Analytics. The main difference between the paid and free version of Google Analytics lies in how each product deals with data and default integration. With a standard Google Analytics account, it is possible to create 20 custom metrics and 20 custom dimensions. On the other hand, a 360 Google Analytics account can give you about 200 of each. Additionally, with a standard Google Analytics, you can get up to 10 million hits in a month as opposed to 1 billion+ hits with a GA 360 as per a specific tier. Here, the main difference between the two versions is the total number of ‘hits’. You can look at it this way – if you have thousands of users, then you can easily reach your ‘hit-limit’. In such a situation, you can experience inconsistencies in your data collection. Google has also been known to remove some of the key data because the ‘hits’ reached a limit.

Thus, getting a free or paid version depends upon the size of your business and the total number of users you want to track. It makes sense for large businesses, such as e-commerce sites, to go for the paid version of Google Analytics since they have to track a large number of ‘hits’ in a month as opposed to a small business.

So, keep these guidelines in mind while managing custom dimensions in Google Analytics.

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About author

Saurabh Kumar
  • Saurabh Kumar

A marketing enthusiast with a fascination for technology, an interest in tinkering with data and systems, and 4+ years of experience at ebookers, Saurabh Kumar Founder Envigo, a digital marketing agency, in the year 2007. His passion for Digital Marketing led him to launch a data-driven digital marketing solutions agency.

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