How you can Measure Performance with Custom Dimensions in the search engines Analytics [Tutorial]

Published by Custom dimensions feature may be used to enhance your Google Analytics reporting setup. We’ll tell you some practical examples before diving in to the various choices for implementation. Through the finish, you will be outfitted to use they for your own reporting, and employ these to prove your prowess for your clients or bosses.

What exactly are custom dimensions?

The bottom line is, they allow you to record additional, non-standard data in the search engines Analytics. After that you can pivot or segment your computer data according to these dimensions, much like the way you would with standard dimensions like source, medium, city, or browser. Custom dimensions can also be utilized as filters in the View-level, enabling you to isolate a particular subset of the audience or traffic for much deeper analysis.

As opposed to the information Grouping feature — which enables you to definitely bucket your overall pages into logical groups — custom dimensions allow you to attach entirely new data to hits, sessions, or users. This last point is crucial custom dimensions can engage in the various amounts of scope provided by Google Analytics. What this means is your brand-new dimension can use for an individual user and all sorts of their subsequent interactions in your website, or one pageview hit.

For that purpose of this tutorial, we’re likely to make a simple scenario: You take a well known e-commerce website having a content online marketing strategy that hinges around your site. We’ll begin by illustrating a few of the ways that custom dimensions can offer a brand new perspective.

1. User engagement

You publish a number of tutorials in your blog, even though they succeed in search as well as in social, you find it difficult to demonstrate the financial worth of your ongoing efforts. You think that engagement using the tutorials correlates positively with eventual high-value purchases, and would like to demonstrate this in Analytics. By configuring a person-level custom dimension known as “Commenter” which communicates a realOrfake based on if the user has ever commented in your blog, you are able to track the behaviour of those engaged users.

2. User census

User login status is often suggested like a custom dimension, because it enables you to definitely isolate your overall customers or loyal visitors. This is often a great supply of insight, but we are able to take this a step further: Presuming that you simply collect additional (anonymous) data throughout the user registration process, why don’t you fire these details to Analytics like a user-level custom dimension? Within the situation in our example website, let’s suppose your user registration form features a drop-lower menu for occupation. By communicating users’ selections to Analytics, you are able to compare the acquisition patterns of various professions.

3. Out-of-stock products

Most e-commerce sites have, at some point, experienced the Search engine optimization conundrum of product retirement. What in the event you use product URLs that no more exist? This really is frequently presented like a question of whether or not to leave them online, redirect them, or 404 them. Less often investigated is the effect on conversion, or from the wider behavior results of stock level generally. By recording out-of-stock pageviews like a custom dimension, we are able to justify our actions with data.

Now we have a obvious concept of the potential for custom dimensions, let’s dive into the entire process of implementation.

How you can implement custom dimensions

All custom dimensions must first be produced within the Google Analytics Admin interface. They exist around the Property level, and not the View level, and non-premium Google Analytics accounts are permitted as much as 20 custom dimensions per Property. Expand Custom Definitions, hit Custom Dimensions, and so the red New Custom Dimension button.

C:UsersThomasB.BUILTVISIBLEAppDataLocalMicrosoftWindowsINetCacheContent.Wordcreating-custom-dimensions-1.png

Within the next screen, it’s important to provide your dimension a reputation, pick a Scope (hit, session, user, or — for enhanced e-commerce implementations — product), and appearance the Active box to allow it. Hit Create, and you will be proven a boilerplate form of the code essential to start collecting data.

C:UsersThomasB.BUILTVISIBLEAppDataLocalMicrosoftWindowsINetCacheContent.Wordour-custom-dimension.png

The code — that is documented fully on the internet Developers and Google Support — really is easy:

var mozDimensionValue = 'Howdy Moz Fans'

ga('set', 'dimension1', mozDimensionValue)

As you can tell, we’re defining the need for our dimension inside a JavaScript variable, then while using set method using the ga() command queue to pass through that variable to Analytics like a custom dimension. All subsequent hits around the page (pageviews, occasions, etc) would then include this practice dimension. Observe that we make reference to our dimension by its index number, which in cases like this is 1 go back to the primary Custom Dimensions screen within the Admin place to begin to see the index number which Analytics allotted to your brand-new dimension.

While your developer will typically handle the actual process of implementation — namely exercising the best way to pass through your preferred value right into a JavaScript variable — the syntax is straightforward enough that it may be modified effortlessly. While using to begin our examples from earlier — tracking commenters — you want to send something of ‘commenter’ towards the Dimension 2 slot included in a celebration hit that is configured to fireplace when somebody comments around the blog. With this particular slot pre-configured like a user-level dimension, we’d use:

ga('send', 'event', 'Engagement', 'Blog Comment', )

This method is great, but it isn’t without its drawbacks. It takes on-page tracking code changes, significant developer participation, and doesn’t scale particularly well.

Because of Google Tag Manager, the largest things much simpler.

Implementation with Google Tag Manager

If you are using GTM to deploy your Analytics tracking — as well as for basically the easiest of implementations, I would suggest that you simply do — then deploying custom dimensions becomes far simpler. For individuals a new comer to GTM, I gave an opening talk around the platform at BrightonSEO (slides here), and I’d highly recommend bookmarking both Google’s official documentation and Simo Ahava’s excellent blog.

With regard to this tutorial, I’ll assume you’re acquainted with the fundamentals of GTM. To include a custom dimension to particular tag — within this situation, our blog comment event tag — simply expand “Custom Dimensions” under More Settings, and go into the index number and cost from the dimension you would like to create. Observe that to determine the greater Settings configuration options, it’s important to look into the “Enable overriding settings within this tag” box if you are not utilizing a Google Analytics Settings Variable to configure your implementation.

C:UsersThomasB.BUILTVISIBLEAppDataLocalMicrosoftWindowsINetCacheContent.Wordgtm.png

How about our latter two examples, user census and out-of-stock products?

Our demographic scenario involved a person registration form which incorporated an “Occupation” field. As opposed to our commenting example, the dimension value in cases like this will have to be set programmatically based on user input — it isn’t an easy true/false variable that may be easily mounted on a appropriate event tag.

Basically we can use the “DOM Element” variable type to scrape the need for the “Occupation” drop-lower field directly from the page, this kind of approach isn’t particularly scalable. A much better solution is always to fire the need for the area — combined with the values associated with a other fields you are feeling offer — for your website’s data layer.

Attention, individuals who don’t yet make use of a data layer:

While your team of developers will have to be active in the implementation of the data layer, it’s worth the effort. The benefits for the reporting could be huge, designed for bigger organizations. Defining the items in your site’s data layer is a superb chance for mix-team collaboration, and implies that all potentially insightful data points are available inside a machine-readable and platform-agnostic format, prepared to be fired to GA. It is also less susceptible to mistakes than ad-hoc tracking code. Similar to how CSS separates out style from content, the information layer isolates your computer data.

Your developer will have to result in the needed information obtainable in the information layer before you define it as being an information Layer Variable in GTM and begin utilizing it inside your tags. Within the example below, think the JavaScript variable ‘myValue’ continues to be configured to come back the occupation joined through the user, like a string. We push it towards the data layer, then define it as being an information Layer Variable in GTM:

var myValue = 'Professional Juggler'

dataLayer.push()

C:UsersThomasB.BUILTVISIBLEAppDataLocalMicrosoftWindowsINetCacheContent.Wordgtm-dlv.png

Fasten a custom dimension for your User Registration event tag, as before, then simply just reference this Data Layer Variable because the dimension value. Done!

Our third example follows the very same concepts: Getting identified product-in-stock status like a hit-level datapoint with potential reporting insight, with our data layer configured to come back this like a variable on product pages, we just configure our pageview tag to make use of this variable because the value for any new custom dimension.

C:UsersThomasB.BUILTVISIBLEAppDataLocalMicrosoftWindowsINetCacheContent.Wordgtm-stock.png

Reporting & analysis

The easiest method to view custom dimension data in Analytics is to use another dimension to some standard report. Within the example below, we’ve set our new “User Occupation” dimension because the secondary dimension inside a New/Coming back customer report, allowing us to recognize the professions in our newest users, and individuals in our frequent visitors.

C:UsersThomasB.BUILTVISIBLEAppDataLocalMicrosoftWindowsINetCacheContent.Wordsecondary-dim.png

By mix-referencing your brand-new dimensions with behavior data — think social share frequency by occupation — you will gain understanding of the subsets of the audience who are likely to interact or convert.

In reality, however, applying another dimension in this way isn’t favorable to effective analysis. In most cases, this method will hugely increase the amount of rows of information inside your report without supplying any immediately helpful information. As a result, it’s frequently essential to start out a step further: You are able to export the information into Stand out for much deeper analysis, or develop a custom dashboard to pivot the information exactly how you need it. Within the example below, a chart and table happen to be configured to exhibit our most viewed out-of-stock products during the period of the final week. Timely, actionable insight!

C:UsersThomasB.BUILTVISIBLEAppDataLocalMicrosoftWindowsINetCacheContent.Worddashboard.png

Sometimes, it’s essential to completely isolate a subset of information inside a dedicated view. This is often particularly effective when combined with a person-level custom dimension. Your house we want to drill lower to exhibit only our most engaged users. Are going to this by making use of a Filter to a different view. Within the following example, we’ve applied a custom ‘Include’ Filter which specifies something of ‘commenter’ according to our “Blog Commenter” custom dimension.

C:UsersThomasB.BUILTVISIBLEAppDataLocalMicrosoftWindowsINetCacheContent.Wordfilter-include.png

The end result? A passionate view which reports on engaged users only.

For additional info on the intricacies of filtering data according to session or user-level custom dimensions — as well as their implications for the Real-time reports — make sure to read this great publish from LunaMetrics.

Final ideas

A much deeper knowledge of your audience isn’t a poor factor. Custom dimension is just one of the numerous ways that Google Analytics could be extended beyond its default configuration to supply more granular, actionable insights tailored to the requirements of your company.

Just like a number of other advanced Analytics features, execution is everything. It’s easier to don’t have any custom dimensions whatsoever rather than waste your limited slots with dimensions that are poorly implemented or simply plain unnecessary. Planning and implementation ought to be a collaborative process involving the marketing, management, and development teams.

Hopefully this information has provided ideas for the way custom dimensions might provide you with a new perspective in your audience.

Thank you for studying!

Join The Moz Top Ten, a semimonthly mailer updating you on top ten hottest bits of Search engine optimization news, tips, and rad links uncovered through the Moz team. Consider it as being your exclusive digest of stuff you do not have time for you to search lower but wish to read!

Published by Custom dimensions feature may be used to enhance your Google Analytics reporting setup. We’ll tell you some practical examples before diving in to the various choices for implementation. Through the finish, you will be outfitted to use they for your own reporting, and employ these to prove your prowess for your clients or bosses.

What exactly are custom dimensions?

The bottom line is, they allow you to record additional, non-standard data in the search engines Analytics. After that you can pivot or segment your computer data according to these dimensions, much like the way you would with standard dimensions like source, medium, city, or browser. Custom dimensions can also be utilized as filters in the View-level, enabling you to isolate a particular subset of the audience or traffic for much deeper analysis.

As opposed to the information Grouping feature — which enables you to definitely bucket your overall pages into logical groups — custom dimensions allow you to attach entirely new data to hits, sessions, or users. This last point is crucial custom dimensions can engage in the various amounts of scope provided by Google Analytics. What this means is your brand-new dimension can use for an individual user and all sorts of their subsequent interactions in your website, or one pageview hit.

For that purpose of this tutorial, we’re likely to make a simple scenario: You take a well known e-commerce website having a content online marketing strategy that hinges around your site. We’ll begin by illustrating a few of the ways that custom dimensions can offer a brand new perspective.

1. User engagement

You publish a number of tutorials in your blog, even though they succeed in search as well as in social, you find it difficult to demonstrate the financial worth of your ongoing efforts. You think that engagement using the tutorials correlates positively with eventual high-value purchases, and would like to demonstrate this in Analytics. By configuring a person-level custom dimension known as “Commenter” which communicates a realOrfake based on if the user has ever commented in your blog, you are able to track the behaviour of those engaged users.

2. User census

User login status is often suggested like a custom dimension, because it enables you to definitely isolate your overall customers or loyal visitors. This is often a great supply of insight, but we are able to take this a step further: Presuming that you simply collect additional (anonymous) data throughout the user registration process, why don’t you fire these details to Analytics like a user-level custom dimension? Within the situation in our example website, let’s suppose your user registration form features a drop-lower menu for occupation. By communicating users’ selections to Analytics, you are able to compare the acquisition patterns of various professions.

3. Out-of-stock products

Most e-commerce sites have, at some point, experienced the Search engine optimization conundrum of product retirement. What in the event you use product URLs that no more exist? This really is frequently presented like a question of whether or not to leave them online, redirect them, or 404 them. Less often investigated is the effect on conversion, or from the wider behavior results of stock level generally. By recording out-of-stock pageviews like a custom dimension, we are able to justify our actions with data.

Now we have a obvious concept of the potential for custom dimensions, let’s dive into the entire process of implementation.

How you can implement custom dimensions

All custom dimensions must first be produced within the Google Analytics Admin interface. They exist around the Property level, and not the View level, and non-premium Google Analytics accounts are permitted as much as 20 custom dimensions per Property. Expand Custom Definitions, hit Custom Dimensions, and so the red New Custom Dimension button.

C:UsersThomasB.BUILTVISIBLEAppDataLocalMicrosoftWindowsINetCacheContent.Wordcreating-custom-dimensions-1.png

Within the next screen, it’s important to provide your dimension a reputation, pick a Scope (hit, session, user, or — for enhanced e-commerce implementations — product), and appearance the Active box to allow it. Hit Create, and you will be proven a boilerplate form of the code essential to start collecting data.

C:UsersThomasB.BUILTVISIBLEAppDataLocalMicrosoftWindowsINetCacheContent.Wordour-custom-dimension.png

The code — that is documented fully on the internet Developers and Google Support — really is easy:

var mozDimensionValue = 'Howdy Moz Fans'

ga('set', 'dimension1', mozDimensionValue)

As you can tell, we’re defining the need for our dimension inside a JavaScript variable, then while using set method using the ga() command queue to pass through that variable to Analytics like a custom dimension. All subsequent hits around the page (pageviews, occasions, etc) would then include this practice dimension. Observe that we make reference to our dimension by its index number, which in cases like this is 1 go back to the primary Custom Dimensions screen within the Admin place to begin to see the index number which Analytics allotted to your brand-new dimension.

While your developer will typically handle the actual process of implementation — namely exercising the best way to pass through your preferred value right into a JavaScript variable — the syntax is straightforward enough that it may be modified effortlessly. While using to begin our examples from earlier — tracking commenters — you want to send something of ‘commenter’ towards the Dimension 2 slot included in a celebration hit that is configured to fireplace when somebody comments around the blog. With this particular slot pre-configured like a user-level dimension, we’d use:

ga('send', 'event', 'Engagement', 'Blog Comment', )

This method is great, but it isn’t without its drawbacks. It takes on-page tracking code changes, significant developer participation, and doesn’t scale particularly well.

Because of Google Tag Manager, the largest things much simpler.

Implementation with Google Tag Manager

If you are using GTM to deploy your Analytics tracking — as well as for basically the easiest of implementations, I would suggest that you simply do — then deploying custom dimensions becomes far simpler. For individuals a new comer to GTM, I gave an opening talk around the platform at BrightonSEO (slides here), and I’d highly recommend bookmarking both Google’s official documentation and Simo Ahava’s excellent blog.

With regard to this tutorial, I’ll assume you’re acquainted with the fundamentals of GTM. To include a custom dimension to particular tag — within this situation, our blog comment event tag — simply expand “Custom Dimensions” under More Settings, and go into the index number and cost from the dimension you would like to create. Observe that to determine the greater Settings configuration options, it’s important to look into the “Enable overriding settings within this tag” box if you are not utilizing a Google Analytics Settings Variable to configure your implementation.

C:UsersThomasB.BUILTVISIBLEAppDataLocalMicrosoftWindowsINetCacheContent.Wordgtm.png

How about our latter two examples, user census and out-of-stock products?

Our demographic scenario involved a person registration form which incorporated an “Occupation” field. As opposed to our commenting example, the dimension value in cases like this will have to be set programmatically based on user input — it isn’t an easy true/false variable that may be easily mounted on a appropriate event tag.

Basically we can use the “DOM Element” variable type to scrape the need for the “Occupation” drop-lower field directly from the page, this kind of approach isn’t particularly scalable. A much better solution is always to fire the need for the area — combined with the values associated with a other fields you are feeling offer — for your website’s data layer.

Attention, individuals who don’t yet make use of a data layer:

While your team of developers will have to be active in the implementation of the data layer, it’s worth the effort. The benefits for the reporting could be huge, designed for bigger organizations. Defining the items in your site’s data layer is a superb chance for mix-team collaboration, and implies that all potentially insightful data points are available inside a machine-readable and platform-agnostic format, prepared to be fired to GA. It is also less susceptible to mistakes than ad-hoc tracking code. Similar to how CSS separates out style from content, the information layer isolates your computer data.

Your developer will have to result in the needed information obtainable in the information layer before you define it as being an information Layer Variable in GTM and begin utilizing it inside your tags. Within the example below, think the JavaScript variable ‘myValue’ continues to be configured to come back the occupation joined through the user, like a string. We push it towards the data layer, then define it as being an information Layer Variable in GTM:

var myValue = 'Professional Juggler'

dataLayer.push()

C:UsersThomasB.BUILTVISIBLEAppDataLocalMicrosoftWindowsINetCacheContent.Wordgtm-dlv.png

Fasten a custom dimension for your User Registration event tag, as before, then simply just reference this Data Layer Variable because the dimension value. Done!

Our third example follows the very same concepts: Getting identified product-in-stock status like a hit-level datapoint with potential reporting insight, with our data layer configured to come back this like a variable on product pages, we just configure our pageview tag to make use of this variable because the value for any new custom dimension.

C:UsersThomasB.BUILTVISIBLEAppDataLocalMicrosoftWindowsINetCacheContent.Wordgtm-stock.png

Reporting & analysis

The easiest method to view custom dimension data in Analytics is to use another dimension to some standard report. Within the example below, we’ve set our new “User Occupation” dimension because the secondary dimension inside a New/Coming back customer report, allowing us to recognize the professions in our newest users, and individuals in our frequent visitors.

C:UsersThomasB.BUILTVISIBLEAppDataLocalMicrosoftWindowsINetCacheContent.Wordsecondary-dim.png

By mix-referencing your brand-new dimensions with behavior data — think social share frequency by occupation — you will gain understanding of the subsets of the audience who are likely to interact or convert.

In reality, however, applying another dimension in this way isn’t favorable to effective analysis. In most cases, this method will hugely increase the amount of rows of information inside your report without supplying any immediately helpful information. As a result, it’s frequently essential to start out a step further: You are able to export the information into Stand out for much deeper analysis, or develop a custom dashboard to pivot the information exactly how you need it. Within the example below, a chart and table happen to be configured to exhibit our most viewed out-of-stock products during the period of the final week. Timely, actionable insight!

C:UsersThomasB.BUILTVISIBLEAppDataLocalMicrosoftWindowsINetCacheContent.Worddashboard.png

Sometimes, it’s essential to completely isolate a subset of information inside a dedicated view. This is often particularly effective when combined with a person-level custom dimension. Your house we want to drill lower to exhibit only our most engaged users. Are going to this by making use of a Filter to a different view. Within the following example, we’ve applied a custom ‘Include’ Filter which specifies something of ‘commenter’ according to our “Blog Commenter” custom dimension.

C:UsersThomasB.BUILTVISIBLEAppDataLocalMicrosoftWindowsINetCacheContent.Wordfilter-include.png

The end result? A passionate view which reports on engaged users only.

For additional info on the intricacies of filtering data according to session or user-level custom dimensions — as well as their implications for the Real-time reports — make sure to read this great publish from LunaMetrics.

Final ideas

A much deeper knowledge of your audience isn’t a poor factor. Custom dimension is just one of the numerous ways that Google Analytics could be extended beyond its default configuration to supply more granular, actionable insights tailored to the requirements of your company.

Just like a number of other advanced Analytics features, execution is everything. It’s easier to don’t have any custom dimensions whatsoever rather than waste your limited slots with dimensions that are poorly implemented or simply plain unnecessary. Planning and implementation ought to be a collaborative process involving the marketing, management, and development teams.

Hopefully this information has provided ideas for the way custom dimensions might provide you with a new perspective in your audience.

Thank you for studying!

Join The Moz Top Ten, a semimonthly mailer updating you on top ten hottest bits of Search engine optimization news, tips, and rad links uncovered through the Moz team. Consider it as being your exclusive digest of stuff you do not have time for you to search lower but wish to read!

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