What do you mean by data-driven marketing?

In simple language, it can be defined as the understanding that works for you. This doesn’t mean the data you have must only be accurate, but it should also be right “Google Analytics”.

There is a critical role played by the data integrity of good reporting, but the analytics auditing shouldn’t only target on the tracking code’s validity. We have experienced the attitude among the digital marketing teams who target reporting where they thought about out of the box implementation of the Google Analytics that will offer all the insight you could need.

Due to those, there are very few chances where Google Analytics is utilized to its full potential. When we talk about the in-depth insights- analyzing the ROI of main marketing activities, the effect of content engagement on raw business KPIs or the conditions of particular subsets of your public. For instance, there are lots of individuals who will avoid this. There is a need for some small investment to track the setup and a careful advisement about the circumstances would be extremely useful.

In this article, we will talk about the different methods through which Custom dimensions can be used for boosting your Google Analytics reporting setup. Here, we will talk about some practical examples prior to dive into different options for applying. At the conclusion of the article, you will get adequate information about implementing these techniques to your own reporting and utilizing its power for proving your strength to your bosses or clients.

What are custom dimensions?

Custom dimensions give you the capability to record non-standard and additional data in Google Analytics. You can segment your data on the basis of the custom dimensions in a similar way as you can do that with the standard dimensions, such as medium, browser, city, or source. Furthermore, custom dimensions can act as filters at the view-level that allow the users for isolating a particular subset of your traffic or audience for in-depth analysis.

It has a content grouping feature through which you can bucket your present pages into logical groups. You will be allowed to attach new details to sessions, users or hits by the custom dimensions. This last point is very important, i.e. custom dimensions take benefits of several levels of scope provided by Google Analytics. This way, your new dimensions can be implemented to an individual user and a single pageview hit to every interaction on your site.

We will only think about a simple circumstance in this entire article. According to which, you are running a well-known commerce website having a content marketing planning hanging all over the blog. We will begin with describing some methods where custom dimensions can offer a new eye view.

1. User Engagement

You are posting lots of tutorials on your blog that are performing wonderfully in organic search and social media, but you still struggling to figure out the monetary value of your hard efforts. You are suspicious that the engagement of the tutorials having a co-relation with the significant costly purchases and want to illustrate this in Analytics. You can figure out the behavior of the engaged users by figuring out a user-level custom dimension known as Commenter that can communicate both right or wrong depending on the comment of the user on your blog.

2. Out of stock products

The majority of the e-commerce has to deal with the SEO conundrum of product retirement on some occasions. Now, the question is what can be done to URLs of the product that no longer available? It is often a part of the debate to whether redirect them, 404 them or leave them online. In general, there will be very less impact on these products on conversions or bigger behavioral impacts of stock level. We can do justification of our actions with data by capturing out of stock page views as a custom dimension.

3. User Demographics

The status of user login is recommended frequently as a custom dimension as it allows you for isolating your present loyal visitors or customers. It can be an ideal source of insight, but it can be taken one step further. Just imagine that you are collecting anonymous or additional data during the process of user registration, why not proceed this details as a user-level custom dimension to Analytics? For instance, take example website and let’s thought that your user registration form consists a menu of a drop-down. The purchase patterns of several professions can be compared by communicating the selection of users to Analytics.

So, now we have a clear understanding of potential custom dimensions. Let’s talk about the process to apply that.

How to apply custom dimensions?

First of all, all the custom dimensions should need to be created in the admin interface of Google Analytics. They are located on the Property Level, not on the View Level and non-premium Google Analytics accounts that allow up to 20 custom dimensions in a property. Expanding custom definitions, hitting custom dimensions, and after that, the red New Custom Dimension button.

You will require to give a name to your dimension on the next screen and choose a Scope (hit, user, session, or enhanced e-commerce application-product), and check the Active box for enabling it. Tap on create and you will see a boilerplate version of the code that is required for beginning the process of collecting data.

The code that can be seen documented on Google Support and Google Developers is extremely simple:

var mozDimensionValue = 'Howdy Moz Fans';ga('set', 'dimension1', mozDimensionValue);

From the above value, you can see that we have used a JavaScript variable for defining the value of our dimension and after that utilized the set method with the ga() command queue for passing that variable as a custom dimension to Analytics. Every subsequent hit on the page (events, page views, etc.) will then consist this custom dimension.

You should also keep in mind that our dimension is referred by its index number, which is 1 in this instance and then returning to the major Custom Dimensions screen that can be seen in the Admin area for watching the index number. It is assigned by the Analytics to your new dimension.

The nuts and bolts of implementation are generally handled by your developer, namely finding out the technique for passing your desired value into a JavaScript Variable, which is a syntax that can be modified easily. If we use some of our first examples from the starting- tracking commenters- we wish to send the value of commenter to the slot 2 that is configured for firing when a third person comment on the blog. As a user-level dimension, we will use this pre-configured:

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

'dimension2':  'commenter'

});

It is a wonderful approach, but it has its own disadvantages. It demands significant developer involvement, on-page tracking code modification and doesn’t have the capability to scale in an adequate manner.

It can be made so easier with the help of Google Tag Manager.

Implementation with Google Tag Manager

If you are utilizing GTM for deploying your Analytics tracking for some of the basic implementations, then we will certainly suggest you continue that. This way, the custom dimensions becomes easier and simpler. If you are a complete beginner, then you should check out some introductory talk about that. There are lots of useful information available about it all over the internet.

In this tutorial, we thought that you are aware of the basic things of GTM. In this case, our blog comment event tag simply expands “Custom Dimensions under More Settings for adding a custom dimension to a specific tag. Then enter the value and index number of the dimension you are looking to set. Please note to check the More Settings configuration options and you require checking the Enable overriding settings” box if you are not utilizing the settings of Google Analytics Variable for configuring your implementation.

Now, you should be thinking about our later two examples, out of stock products and user demographics?

There is a user registration form involved in our demographic circumstances that consist an “Occupation” field. Taking our commenting example in consideration, the dimension will require setting programmatically on the basis of user input. It is a difficult true/ false variable, which can be attached to a particular event tag in an easier way.

We can utilize the DOM Element variable type for scrapping the drop-down field directly off the page, this type of approach is not suitably scalable. A much better answer will be boosting the field value in addition to the values of different fields that you feel needed to the data layer of your website.

Attention, individuals who don’t use a data layer

Your development team will be required to participate in the implementation of a data layer, which is a very useful effort. The benefits for your reporting can be so big, especially for the huge companies. The explanation of the data layer of your site is a wonderful opportunity for having a cross-term collaboration, which means every potential insightful data points can be accessed in a platform-agnostic and machine-readable format that can be fired readily GA. There are chances for lesser mistakes as compared to the ad-hoc tracking code. Your data will be isolated much similar like how style can separate CSS out from the style.

Your developer requires the details that are available in the data layer before it can be defined as a Data Layer Variable in GTM and begin using it in your tags. Check out the example that we have mentioned below:

Just think that the JavaScript variable ‘myValue’ is configured for returning the occupation that the user enters as a string. First of all, we will be pushing them to the data layer and after that explain them as a Data Layer Variable in GTM:

var myValue = 'Professional Juggler';

dataLayer.push({'userOccupation': 'myValue'});

It is important to attach a custom dimension to the event tag of user registration and then simply use this reference data layer variable for the dimension value, it’s done.

The exact same principles are followed by our third example, i.e. having the in-stock status of identified product acting as an ideal data point with the insight reporting and data configured for returning it as a variable on the pages of products. Our page view tag will be simply configured for utilizing this variable for this new custom dimension as the value.

Reporting & analysis

The easiest method for viewing the custom dimension data in Analytics for implementation of a secondary dimension to a standard report. In the below-mentioned example, our new “User Occupation” dimension has been set as the main dimension in a new/ returning visitor report. It will allow is to figure out the professions of the latest users and repeated visitors.

Your new dimensions can be cross-referenced with the behavioral data, i.e. thinking social share numbers by occupation. You will get details into the audience’s subsets, who will be engaged or converted.

In reality, implementation of a secondary dimension in this way is rarely beneficial for an effective analysis. There are many instances where the number of data rows will be increased by this approach without offering any instant useful details. Due to which, it is often mandatory for taking things one step further where the data can be exported into Excel for having an in-depth analysis or developing a custom dashboard so that the data can be pivoted in the way you desire. In the below-mentioned example, we have configured a chart and table to feature our most out of stock things over the period of last week, Check out the timely insight!

There are instances when it becomes mandatory for completely isolating a data set in an enchanting view. It can be extremely strong when utilized with a user-level custom dimension. Let’s we desire to check only our engaged users. It can be done by implementing a Filter for getting a new view. In the below-mentioned example, we have implemented a custom “Include” filter that targets a value of commenter on the basis of our “Blog Commenter” custom dimension.

The result? A wonderful view that reports only on the engaged users.

Conclusion

Having an in-depth understanding of your target audience is always an amazing thing. Custom dimensions are one of some methods through which Google Analytics can be expanded more than just default configuration for offering actionable insights that can cater the requirements of your business.

Execution is an important thing as there are lots of advanced features in Analytics. It is right to have no custom dimensions than wasting your limited slots with the dimensions that are applied in a poor way. There is a need to collaborative planning and implementation between your management, marketing, and development teams.

We believe that this article has offered you some amazing ideas on the techniques through which custom dimensions can give you a new perspective on the targeted audience.