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How to enrich your Google Tag Manager monitor data with the new Google Analytics integration




Jack Pace has begun investigating datasets on student performance in the world of analytics working at John Wiley & Sons. Jack quickly moved to marketing analytics and joined the startup community to help newly launched companies adopt web analytics best practices, data management platforms, and attribution platforms. At MightyHive, Jack has leveraged Google Analytics 360 to focus on enterprise clients that unleash insights outside the traditional marketing focus. Jack, who recently moved to Austin, Texas, enjoys exploring the outdoors and eating all the barbecues and tacos.

We are <3 Shimo

Last year, our favorite Google Analytics and Google Tag Manager expert Simo Ahava wrote a great blog post on how to create a Google Tag Manager monitor. This tool is very useful for unlocking statistics about site tag launches from various dataLayer event pushes.

Many important data points used for activation and measurement depend on the success of the dataLayer push. We’ve made some updates to the Simos Google Tag Manager monitoring framework to allow Google Analytics to combine data. These updates help unlock the ability to identify trends and patterns of stability issues across devices, browsers, and operating systems that can adversely affect dataLayer push. This is a big win for marketers suffering from data breaches.

I’m tinkering

Let’s take a look at the various adaptations we’ve made to Simos’ methodology to combine the results of the Google Tag Manager monitoring tool with BigQuery’s Google Analytics dataset, and what these extensions mean for marketers. The participation we created enhances the data collected through the Google Tag Manager monitoring tool with Google Analytics data points such as devices, browsers, and OScrucial information to understand trends and patterns that impact performance. Will be done.

This post provides an overview of how to measure the stability of your analytical implementation by adding new metrics to your analytical toolset. If you are interested in digging into the details or discussing real use cases, please contact me.

Technical settings

Before you can combine Google Tag Manager monitoring tool data with Google Analytics data, you need to make some prerequisites.

Existing implementation of Simos Monitoring Tool (started using his guide) Access to Google Tag Manager Access to Google Cloud Platform project Google Analytics 360 Export

The following is an architectural diagram showing the Google Tag Manager monitoring tool integrated with Google Analytics.

Secret sauce

The MightyHive data science team designed a series of updates to the Simos monitoring tool to provide enhanced data and diagnostic insights, including tool template updates to capture Google Analytics client IDs, and the Google Analytics BigQuery dataset. I made it possible to participate in. Our team also noticed that in order to interconnect all the events on a particular page, we need to set a universal unique identifier (UUID) via a new custom HTML tag.

With the updated monitor tool template and UUID tag set, the team updated the cloud function to create a BigQuery table schema for the extension tool with three additional fields: ga_client_id, event_id, and urlpath.

Send us an email with details of the custom HTML tags used, key Google Tag Manager settings, and code snippets for template updates.

Does this look like an IRL?

Clientlet claims that Company X released a new version of the site around August 3rd. When the updated site was published, Company X noticed that the gtm.load dataLayer event push would occur later, or in some cases not at all. These tags are launched less frequently after release because the various tags are set to launch in gtm.load.

Tracking breaks like this is a marketer’s nightmare. Did the tag fail? Has anyone published an update with broken code or without a very important dataLayer push? Is the interruption occurring on a particular device, browser, or operating system? These questions and the like can lead to frustrating, time-consuming wild goose tracking, not to mention data loss until the problem is fixed.

Google Tag Manager Load Factor Calculation

To troubleshoot Company X’s misfire, we used a new metric, Google Tag Manager read rates. The load factor was calculated using the following simple formula:

Suppose Company X has an order confirmation dataLayer event that occurs after gtm.js. Replacing gtm.load with the order confirmation dataLayer event gives Company X the rate of fire for order confirmation.


The Google Tag Manager load speed query calculates the rate of fire in the following groups:

Device category – Device category provided by Google Analytics (desktop, tablet, mobile) Device browser – Device browser provided by Google Analytics (Chrome, Safari, Firefox) Page path – Page load count where the event occurred – Gtm.load event Count JS count – number of gtm.js events GTM load rate – load count / JS count

The query output looks like this:

In this example, using the BigQuery Google Tag Manager load rate query, you can quickly see that Company X’s home page (/) has the lowest load rate of 91%. This means that 9% of the traffic (!!!) may not be tracked because 9% of the home page gtm.loads didn’t start.

Better insights, faster solutions

An update to the Google Tag Manager Monitoring Tool can remove some of your guesswork by automating a solution that audits dataLayer launches and quickly diagnoses when problems occur. By leveraging a version of the tool that added Google Analytics data points, Company X was able to quickly identify a sharp drop in rate of fire after its release in Chrome and Firefox, but Safari was unaffected. .. With a quick and reliable diagnosis, Company X quickly fixed the problem, reduced data breaches, and strengthened the analysis settings for future problems.

No data leaks!

In advanced analytics use cases, troubleshooting and diagnostics monopolize huge amounts of time and resources, and there is a risk of large amounts of data leakage until the problem is resolved. You can now combine the insights of the Google Tag Manager Monitor Tool into your Google Analytics dataset to identify when and where your dataLayer push failed, and on which device, browser, and operating system. For marketers, this saves hours, days, and even weeks, and means minimal data loss.

Are you interested in the details? Please email me at [email protected]

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