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This is an implementation guide for Google Analytics 4. The guide is aimed at Google Tag Manager users and has been designed to complement the official documentation. A thing to keep in mind is that Google Analytics 4 (GA4) is still very new. The Google Tag Manager integration is still in its infancy, and implementation places a lot of responsibility on accurate tagging and proper dataLayer instrumentation. In this guide, I’ll explain the new data model from an implementation perspective, and walk you through the nuts and bolts on how to get the different parts of GA4’s Ecommerce machine to work in unison.

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When you use Google Analytics on the web, you are most likely implementing one of analytics.js, the global site tag (gtag.js), or Universal Analytics tags via Google Tag Manager. These libraries all end up doing the same thing: compiling a payload-rich HTTP request to an endpoint at https://www.google-analytics.com. What if you want to have the JavaScript libraries do their job, but instead of sending the data to Google’s servers, you send them to a new, custom endpoint?

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Recently I published an article on how to set up an impact test for the “flicker effect” omnipresent in client-side A/B-testing tools. Be sure to check out that article first to get some context to what we’re going to be talking about here. In this short follow-up, I’ll show you how to measure the average time of the anti-flicker snippet delaying page visibility, if you choose to deploy the snippet.

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With the enforcement of SameSite settings in the latest versions of Google Chrome, it’s become a mad scramble to get cookies working across first-party and third-party contexts. I’ve covered this phenomenon before in my SameSite article, as well as in my guide for setting up cookieless tracking for iframes. Recently, Google Analytics updated its libraries (App+Web, gtag.js, and analytics.js) with a new setting: cookieFlags (analytics.js) or cookie_flags (App+Web and gtag.js).

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In Google Analytics: App + Web, you collect events. One event differs from another event by the name it uses. An event with the name page_view is different from, say, an event with the name file_download. This is all run-of-the-mill stuff. You know this. However, the fundamental change that App + Web introduces, when compared to Universal Analytics, is how event parameters are collected and processed. This gets more complicated than it should be.

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I’ve thoroughly enjoyed writing short (and sometimes a bit longer) bite-sized tips for my #GTMTips topic. With the advent of Google Analytics: App + Web and particularly the opportunity to access raw data through BigQuery, I thought it was a good time to get started on a new tip topic: #BigQueryTips. For Universal Analytics, getting access to the BigQuery export with Google Analytics 360 has been one of the major selling points for the expensive platform.

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Update 7 October 2020: BigQuery Export can now be configured via the property settings of Google Analytics: App + Web, so you don’t need to follow the steps in this article. Check out Charles Farina’s guide for how to do this. Here’s yet another article inspired by the fairly recent release of Google Analytics: App + Web properties. This new property type surfaces Firebase’s analytics capabilities for websites as well, when before they were restricted to mobile apps only (see my guides for iOS and Android).

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Simo Ahava

Husband | Father | Analytics developer
simo (at) simoahava.com

Senior Data Advocate at Reaktor

Finland