Digital marketing is data-centric. Effective marketing requires the collection, collation, and analysis of data from multiple data points. The code and tool to collect data from your website is called a data layer and the Google Tag Manager.
The Google Tag Manager allows you to deploy marketing tags directly from your website or mobile app. GTM integrates with Google Analytics and third-party tags such as Crazy Egg and Facebook.
Data layers, in simple terms, are a way to collect relevant data from your website and then analyze it. Data layers enable you to gather relevant data from multiple pages and combine it into one place.
This article will provide more information about the data layer.
What is a Data Layer?
Data layers are the engine that will power your GTM. Both are intended to work together. The data layer acts as a virtual map for your website or mobile application, which is where the GTM can access data. It’s a JavaScript code that contains the data you wish to collect. The Google Tag Manager uses these data in its tags and triggers as well as variables.
Let’s suppose your marketing team wishes to modify the URL of a webpage for better SEO or align the content. GTM can’t capture relevant data if your site code is changed in this way. A data layer reduces the chance of code changes interrupting your analytic reports.
Your data layer can be thought of as a small bucket with many balls. Each ball contains specific data, such as user IDs, link clicks and page categories. The GTM triggers when it is needed. It then searches the bucket for the appropriate ball.
Every page on your website will have a unique data Layer that specifies the data points you wish to capture.
You can define and control which data points you want by including data layers in your digital analytics implementation.
Why is a Data Layer necessary?
Data layers allow you to use your data for efficient and effective analysis. Each third party software used to support your business has its own data collection process. Data layers allow you to standardize the data collection process and define the data points that are most relevant for your business.
A clearly defined data layer will allow you to be agile in your marketing efforts. You can change the tags and increase flexibility in what you measure. It is simple to change tags and doesn’t require dedicated IT resources.
Data layers are the best and most flexible way to capture data you need to track and measure. Let’s take a closer look at each benefit.
Increased reliability
Two methods are used by most companies to gather data for analytic tools. They are Document Object Model (DOM), scraping, and creating data layers. While creating data layers takes more time upfront, it is much more reliable in the long-term.
DOM scraping has the primary advantage of being able to track data points you are interested in. This flexibility comes at a cost. DOM scraping can be linked to the HTML attributes of a webpage. Any change to these attributes (page title, URL, product details etc.) will cause a problem in data collection. Data collection will be affected.
Let’s suppose a developer makes changes to the site but doesn’t update the DOM structure. Or the SEO team adds keywords to the page name or URL. DOM scraping is great for static sites.
Let’s suppose your SEO team wishes to modify the URL of a page in order to improve search engine rankings. Or, change the HTML to align the content. These changes can lead to gaps or breaks in your data collection if you have a static domain.
This issue is often encountered by developers who can help you troubleshoot the problem and fix it. A well-defined data layer is essential for a dynamic website.
Your data layer provides you with a shield against site changes and protects your analytics reports from data errors or incomplete collection. Your analytics reports and data-driven decision making become more reliable and accurate.
Greater Flexibility
Let’s suppose you use Google Analytics to do your data analysis. Your developer will need to insert a JavaScript snippet at the end of each measurable data point into the HTML. This could make the HTML of the page more complicated and slow down page load times. Slower page loads can negatively impact your SEO and user experience.
Another scenario is where your team would like to switch from Google Analytics to Adobe Analytics or Mixpanel. If your web analytics implementation does not have an agnostic design, this can quickly become a nightmare.
Because it is not vendor-specific, a data layer provides flexibility. It can be used with any third-party application because it is generic and not designed for specific vendors. A data layer is useful if you are in a situation where you often switch between competing applications.
What is the Data Layer?
A data layer is the single reference point for a particular data point. Based on user activity, it collects data from each page. The tag manager then accesses this data and passes it on to your analysis tools for further analysis.
Data layers give you detailed visibility into user behavior. This allows online shops and websites to gauge the success of a category, page, or product. This data layer transmits the raw data to an analytic tool. Management decisions are made based on these interpretations.
GTM tag managers make it easy to deploy marketing tags by providing an interface that allows you to make changes to the data collection process. The data layer doesn’t slow down a page because it isn’t part HTML code.
Two types of data are collected by the data layer. The first type of data is static data, where the value doesn’t change. Dynamic data is the second type. It allows for changes in value. One example would be the purchase price or the number purchased.
The data layer defines the firing rules of dynamic data tags. Basic rules will fire tags based upon the URL of the page, while advanced tags will fire using the code in the data layer (dynamical data).
Three Steps to Improve Quality in Your Data Layer Architecture
We have already discussed what a data layer looks like, how it functions, and its uses. Let’s now discuss how to design the architecture for your data layer. The first step to building your data layer architecture involves identifying the data points you want to measure. Begin with the analysis that you require and then work your way back to the relevant points.
You should design a simple, efficient way to change data points when necessary. For instance, changing the URL of a webpage or adding a new measure.
An architecture that supports data layers will make it easy to make changes without having to change the architecture. These are the three key areas to build a data layer of high quality.
- Collaborate
- Communicate
- Validate and test
Let’s briefly examine each area.
Collaborate
Begin by brainstorming with all stakeholders and then collaborate with them, including the marketing and analytics teams. Identify the business goals, then go backwards to determine the data points that you need to measure.
Every business has its own business goals, so spend time defining them. You should ensure they are SMART (Specific, Measurable. Achievable. Relevant. And Time-bound). Next, assign each data point to the appropriate page on your website.
There will be cases when one data point needs to be pulled from multiple pages. Your developer should have a clear plan for each page.
Communicate
You should ensure that the developer and your team communicate regularly. The code is written by the developer, and the analytics team will produce the results.
Analytics teams must communicate both business goals and data points. This allows the developer to identify data points that might have been overlooked. This will make sure your data layer is well-equipped and robust to meet your business goals.
Validation and Testing
Once your data layer has been set up, it is time to validate and test it. Validation and testing ensure that your data layer populates with the correct data for every page. To ensure robustness of the data layer, test each page in a variety of scenarios.
Automated testing is the best way to test your data layer. There are many tools that can do this. Below is a screenshot of DataTrue’s Data Layer Validation function. This allows you to verify both the data passing through the data layer and the tags.
functional and unit testing are used to validate the data layer. The functional test validates the functioning of the data layer, while the unit test validates code accuracy.
Conclusion
A data layer will improve your decision-making and analytics capabilities. Although it takes some time to build your data layer, it will pay off in the long-term.
Many companies prefer to proceed in a gradual manner, particularly those who are new to the Tag Management System environment (TMS). Many TMS systems such as Google Tag Management have limited data layer capabilities. They prefer to start there.
This logic is simple: “Let’s get started, then we will evaluate creating data layers down the line.” People become complacent and stop moving to the data layer. They continue to struggle with accuracy, reliability, limited analytical capabilities, and hit-or miss decision making. It is best to invest time and effort upfront in solving the problem once and for all.
